From b471ac95f284f64de83c5523726010358b2f0394 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 10:50:33 +0200 Subject: [PATCH 01/26] update CI --- .travis.yml | 83 ++++++++++++++++++++++------------------------------ appveyor.yml | 62 +++++++++++++++++++-------------------- tic.R | 29 ++---------------- 3 files changed, 67 insertions(+), 107 deletions(-) diff --git a/.travis.yml b/.travis.yml index 98cb980..8dc42e1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,51 +1,38 @@ -language: r -sudo: false -dist: trusty -cache: - - packages - - ccache -latex: false +# DO NOT CHANGE THE CODE BELOW +before_install: + - R -q -e 'if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")' + - R -q -e 'if (!requireNamespace("curl", quietly = TRUE)) install.packages("curl")' + - R -q -e 'remotes::install_github("ropenscilabs/tic"); tic::prepare_all_stages(); tic::before_install()' +install: R -q -e 'tic::install()' +after_install: R -q -e 'tic::after_install()' +before_script: R -q -e 'tic::before_script()' +script: R -q -e 'tic::script()' +after_success: R -q -e 'tic::after_success()' +after_failure: R -q -e 'tic::after_failure()' +before_deploy: R -q -e 'tic::before_deploy()' +deploy: + provider: script + script: R -q -e 'tic::deploy()' + on: + all_branches: true +after_deploy: R -q -e 'tic::after_deploy()' +after_script: R -q -e 'tic::after_script()' +# DO NOT CHANGE THE CODE ABOVE + -# These steps apply to all matrix jobs unless defined differently in tic.R -before_install: - - R -q -e 'install.packages("remotes"); remotes::install_github("ropenscilabs/tic"); tic::prepare_all_stages(); tic::before_install(); remotes::install_github("tidyverse/ggplot2")' +# Custom parts: -matrix: - fast_finish: true - allow_failures: - - env: NB='w/ lintr' - - env: NB='w/ covr' - include: - - os: linux - r: devel - install: R -q -e 'tic::install()' - after_install: R -q -e 'tic::after_install()' - before_script: R -q -e 'tic::before_script()' - script: R -q -e 'tic::script()' - before_deploy: R -q -e 'tic::before_deploy()' - deploy: - provider: script - script: R -q -e 'tic::deploy()' - on: - branch: master - condition: - - $TRAVIS_PULL_REQUEST = false - - $TRAVIS_EVENT_TYPE != cron - after_deploy: R -q -e 'tic::after_deploy()' - - os: osx - r: release - install: R -q -e 'tic::install()' - after_install: R -q -e 'tic::after_install()' - before_script: R -q -e 'tic::before_script()' - script: R -q -e 'tic::script()' - - os: linux - r: devel - env: NB='w/ covr' - - os: linux - r: devel - env: NB='w/ lintr' +# Header +language: r +sudo: false +dist: xenial +cache: packages +latex: true +pandoc_version: 2.5 -notifications: - on_success: change # default: always - on_failure: change # default: always - email: false +env: + global: + - _R_CHECK_FORCE_SUGGESTS_=false + - MAKEFLAGS="-j 2" + - BUILD_PKGDOWN=true + - _R_CHECK_TESTS_NLINES_=0 diff --git a/appveyor.yml b/appveyor.yml index e32d316..3001a68 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,35 +8,33 @@ init: Import-Module '..\appveyor-tool.ps1' install: - ps: Bootstrap - -# Adapt as necessary starting from here - -build_script: - - travis-tool.sh install_deps - -test_script: - - travis-tool.sh run_tests - -on_failure: - - 7z a failure.zip *.Rcheck\* - - appveyor PushArtifact failure.zip - -artifacts: - - path: '*.Rcheck\**\*.log' - name: Logs - - - path: '*.Rcheck\**\*.out' - name: Logs - - - path: '*.Rcheck\**\*.fail' - name: Logs - - - path: '*.Rcheck\**\*.Rout' - name: Logs - - - path: '\*_*.tar.gz' - name: Bits - - - path: '\*_*.zip' - name: Bits + - ps: Bootstrap + - cmd: R -q -e "writeLines('options(repos = \'https://cloud.r-project.org\')', '~/.Rprofile')" + - cmd: R -q -e "if (!requireNamespace('remotes', quietly = TRUE)) install.packages('remotes')" + - cmd: R -q -e "if (!requireNamespace('curl', quietly = TRUE)) install.packages('curl')" + - cmd: R -q -e "remotes::install_github('ropenscilabs/tic')" + - cmd: R -q -e "tic::prepare_all_stages()" + - cmd: R -q -e "tic::before_install()" + +cache: + - C:\RLibrary\ + +before_build: Rscript -e "tic::before_install()" +build_script: Rscript -e "tic::install()" +after_build: Rscript -e "tic::after_install()" +before_test: Rscript -e "tic::before_script()" +test_script: Rscript -e "tic::script()" +on_success: Rscript -e "try(tic::after_success(), silent = TRUE)" +on_failure: Rscript -e "tic::after_failure()" +before_deploy: Rscript -e "tic::before_deploy()" +deploy_script: Rscript -e "tic::deploy()" +after_deploy: Rscript -e "tic::after_deploy()" +on_finish: Rscript -e "tic::after_script()" + +platform: x64 +image: Visual Studio 2017 + +environment: + global: + USE_RTOOLS: true + R_ARCH: x64 diff --git a/tic.R b/tic.R index 8141ed4..5183948 100644 --- a/tic.R +++ b/tic.R @@ -1,28 +1,3 @@ -if (Sys.getenv("NB") != "w/ covr" | Sys.getenv("NB") != "w/ lintr") { - add_package_checks() -} +do_package_checks() -if (Sys.getenv("id_rsa") != "") { - - get_stage("before_deploy") %>% - add_step(step_setup_ssh()) - - get_stage("deploy") %>% - add_step(step_build_pkgdown()) %>% - add_step(step_push_deploy(path = "docs", branch = "gh-pages")) - -} - -if (Sys.getenv("NB") == "w/ covr" | Sys.getenv("NB") == "w/ lintr") { - - if (Sys.getenv("NB") == "w/ lintr") { - - get_stage("after_success") %>% - step_run_code(lintr::lint_package(linters = with_defaults(commented_code_linter = NULL, - closed_curly_linter = closed_curly_linter(allow_single_line = TRUE), - open_curly_linter = open_curly_linter(allow_single_line = TRUE)))) - } else { - get_stage("after_success") %>% - step_run_code(covr::codecov(quiet = FALSE)) - } -} +do_pkgdown() From 556b227582328efe9d450e52c095cb90a94d667c Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:07:55 +0200 Subject: [PATCH 02/26] tidy DESCR --- DESCRIPTION | 77 ++++++++++++++++++++++++++++++----------------------- 1 file changed, 44 insertions(+), 33 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index fc6a3a9..07924bc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,39 +1,50 @@ Package: oddsratio -Version: 1.0.3 -Date: 2018-07-15 Title: Odds Ratio Calculation for GAM(M)s & GLM(M)s -Authors@R: c( - person("Patrick", "Schratz", , - email = "patrick.schratz@gmail.com", - role = c("aut", "cre"), - comment = c(ORCID = '0000-0003-0748-6624')) - ) -Description: Simplified odds ratio calculation of GAM(M)s & GLM(M)s. - Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. - It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. - For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant. - Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. - In both cases, confident intervals are returned additionally. - Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot. +Version: 1.1.0 +Date: 2019-06-13 +Authors@R: + person(given = "Patrick", + family = "Schratz", + role = c("aut", "cre"), + email = "patrick.schratz@gmail.com", + comment = c(ORCID = "0000-0003-0748-6624")) +Description: Simplified odds ratio calculation of GAM(M)s & + GLM(M)s. Provides structured output (data frame) of all predictors + and their corresponding odds ratios and confident intervals for + further analyses. It helps to avoid false references of predictors + and increments by specifying these parameters in a list instead of + using 'exp(coef(model))' (standard approach of odds ratio calculation + for GLMs) which just returns a plain numeric output. For GAM(M)s, + odds ratio calculation is highly simplified with this package since it + takes care of the multiple 'predict()' calls of the chosen predictor + while holding other predictors constant. Also, this package allows + odds ratio calculation of percentage steps across the whole predictor + distribution range for GAM(M)s. In both cases, confident intervals + are returned additionally. Calculated odds ratio of GAM(M)s can be + inserted into the smooth function plot. +License: MIT + file LICENSE URL: https://github.com/pat-s/oddsratio BugReports: https://github.com/pat-s/oddsratio/issues -Depends: R (>= 3.0.0) -Imports: mgcv, - MASS, - stats, - ggplot2 (>= 3.0.0), - cowplot -License: MIT + file LICENSE -RoxygenNote: 6.0.1 -Suggests: knitr, - rmarkdown, - grid, - gtable, - scales, - testthat, - pacman, - gam -LazyData: true +Depends: + R (>= 3.0.0) +Imports: + ggplot2 (>= 3.0.0), + MASS, + mgcv, + stats +Suggests: + gam, + grid, + gtable, + knitr, + MASS, + rmarkdown, + scales, + testthat +VignetteBuilder: + knitr ByteCompile: true -VignetteBuilder: knitr +Encoding: UTF-8 +LazyData: true Roxygen: list(markdown = TRUE) +RoxygenNote: 6.0.1 From 7b0ec3ab582ede4f40797174f0530349002991a8 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:08:08 +0200 Subject: [PATCH 03/26] update README --- README.Rmd | 59 ++++-------- README.md | 90 ++++++++++-------- README_files/figure-gfm/unnamed-chunk-4-1.png | Bin 0 -> 36056 bytes README_files/figure-gfm/unnamed-chunk-5-1.png | Bin 0 -> 41343 bytes README_files/figure-gfm/unnamed-chunk-6-1.png | Bin 0 -> 46214 bytes 5 files changed, 69 insertions(+), 80 deletions(-) create mode 100644 README_files/figure-gfm/unnamed-chunk-4-1.png create mode 100644 README_files/figure-gfm/unnamed-chunk-5-1.png create mode 100644 README_files/figure-gfm/unnamed-chunk-6-1.png diff --git a/README.Rmd b/README.Rmd index 3bff8e0..112009e 100644 --- a/README.Rmd +++ b/README.Rmd @@ -3,34 +3,17 @@ output: github_document --- ```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE, eval = FALSE) -library(ggplot2) -library(cowplot) +knitr::opts_chunk$set(echo = TRUE, eval = TRUE) +library("ggplot2") +library("cowplot") ``` -```{r, echo = FALSE, eval = TRUE} -version <- "1.0.0.9000" -``` - -```{r, echo = FALSE, eval = TRUE} -rvers <- "3.0.0" -``` - -#### General -[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1095473.svg)](https://doi.org/10.5281/zenodo.1095473) - -| Resource: | CRAN | Travis CI | Appveyor | -| ------------- | ------------------- | --------------- | ---------------- | -| _Platforms:_ | _Multiple_ | _Linux & macOS_ | _Windows_ | -| R CMD check | CRAN version | Build status | Build status | -| Test coverage | | Coverage Status | | - - -#### CRAN -[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/oddsratio)](https://cran.r-project.org/package=oddsratio) -[![Downloads](https://cranlogs.r-pkg.org/badges/oddsratio?color=brightgreen)](https://www.r-pkg.org/pkg/oddsratio) -![](https://cranlogs.r-pkg.org/badges/grand-total/oddsratio) +[![Build Status](https://travis-ci.org/pat-s/oddsratio.svg?branch=master)](https://travis-ci.org/pat-s/oddsratio) +[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version-ago/oddsratio)](https://cran.r-project.org/package=oddsratio) +[![cran checks](https://cranchecks.info/badges/worst/oddsratio)](https://cran.r-project.org/web/checks/check_results_oddsratio.html) +[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/oddsratio)](https://cran.rstudio.com/web/packages/oddsratio/index.html) +[![lifecycle](https://img.shields.io/badge/lifecycle-stable-blue.svg)](https://www.tidyverse.org/lifecycle/#stable) +[![Dependencies](https://tinyverse.netlify.com/badge/oddsratio)](https://cran.r-project.org/package=oddsratio) Functions for calculation and plotting of odds ratios of Generalized Additive (Mixed) Models and Generalized Linear (Mixed) Models with a binomial @@ -62,7 +45,9 @@ However, other levels can be specified with the param `CI`. Data source: https://stats.idre.ucla.edu/stat/data/binary.csv ```{r} -pacman::p_load(oddsratio, mgcv) +library("oddsratio") +library("mgcv") + df <- data_glm df$rank <- factor(df$rank) fit_glm <- glm(admit ~ gre + gpa + rank, data = df, family = "binomial") @@ -109,13 +94,10 @@ or_gam(data = df, model = fit_gam, Plotting of GAM smooths is also supported: ```{r} -plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") +plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") + + cowplot::theme_minimal_grid() ``` -

- -

- Insert the calculated odds ratios into the smoothing function: ```{r} @@ -127,12 +109,10 @@ plot <- insert_or(plot_object, or_object, or_yloc = 3, values_xloc = 0.04, line_size = 0.5, line_type = "dotdash", values_yloc = 0.5, arrow_col = "red") -plot +plot + + cowplot::theme_minimal_grid() ``` -

- -

Insert multiple odds ratios into one smooth: @@ -145,9 +125,6 @@ insert_or(plot, or_object2, or_yloc = 2.1, values_yloc = 2, rect_col = "green4", rect_alpha = 0.2, line_alpha = 1, line_type = "dashed", arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, - arrow_length = 0.01, rect = T) + arrow_length = 0.01, rect = T) + + cowplot::theme_minimal_grid() ``` - -

- -

diff --git a/README.md b/README.md index 314fd37..d606d7e 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,13 @@ -#### General - -[![Project Status: Active – The project has reached a stable, usable -state and is being actively -developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1095473.svg)](https://doi.org/10.5281/zenodo.1095473) - -| Resource: | CRAN | Travis CI | Appveyor | -| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| *Platforms:* | *Multiple* | *Linux & macOS* | *Windows* | -| R CMD check | CRAN version | Build status | Build status | -| Test coverage | | Coverage Status | | - -#### CRAN - -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/oddsratio)](https://cran.r-project.org/package=oddsratio) -[![Downloads](https://cranlogs.r-pkg.org/badges/oddsratio?color=brightgreen)](https://www.r-pkg.org/pkg/oddsratio) -![](https://cranlogs.r-pkg.org/badges/grand-total/oddsratio) +[![Build +Status](https://travis-ci.org/pat-s/oddsratio.svg?branch=master)](https://travis-ci.org/pat-s/oddsratio) +[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version-ago/oddsratio)](https://cran.r-project.org/package=oddsratio) +[![cran +checks](https://cranchecks.info/badges/worst/oddsratio)](https://cran.r-project.org/web/checks/check_results_oddsratio.html) +[![CRAN +Downloads](https://cranlogs.r-pkg.org/badges/oddsratio)](https://cran.rstudio.com/web/packages/oddsratio/index.html) +[![lifecycle](https://img.shields.io/badge/lifecycle-stable-blue.svg)](https://www.tidyverse.org/lifecycle/#stable) +[![Dependencies](https://tinyverse.netlify.com/badge/oddsratio)](https://cran.r-project.org/package=oddsratio) Functions for calculation and plotting of odds ratios of Generalized Additive (Mixed) Models and Generalized Linear (Mixed) Models with a @@ -49,7 +40,15 @@ including their respective confident intervals. The default level is source: ``` r -pacman::p_load(oddsratio, mgcv) +library("oddsratio") +library("mgcv") +``` + + ## Loading required package: nlme + + ## This is mgcv 1.8-28. For overview type 'help("mgcv-package")'. + +``` r df <- data_glm df$rank <- factor(df$rank) fit_glm <- glm(admit ~ gre + gpa + rank, data = df, family = "binomial") @@ -58,6 +57,13 @@ or_glm(data = df, model = fit_glm, incr = list(gre = 380, gpa = 5, CI = 0.95)) ``` + ## predictor oddsratio CI_low (2.5 %) CI_high (97.5 %) increment + ## 1 gre 2.364 1.054 5.396 380 + ## 2 gpa 55.712 2.229 1511.282 5 + ## 3 rank2 0.509 0.272 0.945 Indicator variable + ## 4 rank3 0.262 0.132 0.512 Indicator variable + ## 5 rank4 0.212 0.091 0.471 Indicator variable + ### GAM For GAMs, the calculation of odds ratio is different. Due to its @@ -84,6 +90,19 @@ or_gam(data = df, model = fit_gam, pred = "x2", percentage = 20, slice = TRUE) ``` + ## predictor value1 value2 perc1 perc2 oddsratio CI_low (2.5%) + ## 1 x2 0.001 0.200 0 20 2510.77 1091.68 + ## 2 x2 0.200 0.400 20 40 0.03 0.03 + ## 3 x2 0.400 0.599 40 60 0.58 0.56 + ## 4 x2 0.599 0.799 60 80 0.06 0.06 + ## 5 x2 0.799 0.998 80 100 0.41 0.75 + ## CI_high (97.5%) + ## 1 5774.53 + ## 2 0.03 + ## 3 0.60 + ## 4 0.06 + ## 5 0.22 + If you want to compute a single odds ratio for specific values, simply set param `slice = FALSE`: @@ -92,18 +111,17 @@ or_gam(data = df, model = fit_gam, pred = "x2", values = c(0.099, 0.198)) ``` -Plotting of GAM smooths is also -supported: + ## predictor value1 value2 oddsratio CI_low (2.5%) CI_high (97.5%) + ## 1 x2 0.099 0.198 23.32353 23.30424 23.34283 + +Plotting of GAM smooths is also supported: ``` r -plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") +plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") + + cowplot::theme_minimal_grid() ``` -

- - - -

+![](README_files/figure-gfm/unnamed-chunk-4-1.png) Insert the calculated odds ratios into the smoothing function: @@ -116,14 +134,11 @@ plot <- insert_or(plot_object, or_object, or_yloc = 3, values_xloc = 0.04, line_size = 0.5, line_type = "dotdash", values_yloc = 0.5, arrow_col = "red") -plot +plot + + cowplot::theme_minimal_grid() ``` -

- - - -

+![](README_files/figure-gfm/unnamed-chunk-5-1.png) Insert multiple odds ratios into one smooth: @@ -136,11 +151,8 @@ insert_or(plot, or_object2, or_yloc = 2.1, values_yloc = 2, rect_col = "green4", rect_alpha = 0.2, line_alpha = 1, line_type = "dashed", arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, - arrow_length = 0.01, rect = T) + arrow_length = 0.01, rect = T) + + cowplot::theme_minimal_grid() ``` -

- - - -

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zw|?|>k(!YX?cZJL-WVA(BuvsV;6Hwp?5{VWfcZbX_#=BxS8dl(Mq@D?m7sj)yuvg2 HEB^lv8|b~e literal 0 HcmV?d00001 From be0a8d352403efc206ae014827b656a261b91d7a Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:08:21 +0200 Subject: [PATCH 04/26] do not force a theme by default --- R/plot_gam.R | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/R/plot_gam.R b/R/plot_gam.R index 8c79780..a464597 100644 --- a/R/plot_gam.R +++ b/R/plot_gam.R @@ -82,8 +82,7 @@ plot_gam <- function(model = NULL, pred = NULL, col_line = "blue", # nocov start geom_ribbon(aes_(x = ~x, ymin = ~se_lwr, ymax = ~se_upr), fill = ci_fill, alpha = ci_alpha) + ylab(ylab) + - xlab(xlab) + - cowplot::background_grid(major = "xy", minor = "none") + xlab(xlab) if (!is.null(limits_y) & !is.null(breaks_y)) { plot_gam <- plot_gam + From 46b978ed20912717b9fecf2866196d72c30b3281 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:08:31 +0200 Subject: [PATCH 05/26] no need for pacman --- tests/testthat/test-or-glm.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-or-glm.R b/tests/testthat/test-or-glm.R index eebb5d7..1fc2b18 100644 --- a/tests/testthat/test-or-glm.R +++ b/tests/testthat/test-or-glm.R @@ -1,6 +1,7 @@ context("or_glm") -pacman::p_load(mgcv, MASS) +library("mgcv") +library("MASS") test_that("correct level count of indicator variable for glm", { data("data_glm") From 043e8bbc3cc551ab72e635162e8e170f9ace67bc Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:11:15 +0200 Subject: [PATCH 06/26] no need for MASS in imports --- DESCRIPTION | 3 +-- R/or_glm.R | 1 - 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 07924bc..8e9040f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -29,7 +29,6 @@ Depends: R (>= 3.0.0) Imports: ggplot2 (>= 3.0.0), - MASS, mgcv, stats Suggests: @@ -47,4 +46,4 @@ ByteCompile: true Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 6.0.1 +RoxygenNote: 6.1.1 diff --git a/R/or_glm.R b/R/or_glm.R index 6403aa4..e012d22 100644 --- a/R/or_glm.R +++ b/R/or_glm.R @@ -4,7 +4,6 @@ #' @importFrom stats coefficients #' @importFrom stats confint #' @import mgcv -#' @import MASS #' #' @description This function calculates odds ratio(s) for specific #' increment steps of GLMs. From ed719f5db62766489359a7fea5a8e7c40f5a0358 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:11:30 +0200 Subject: [PATCH 07/26] NAMESPACE and man --- NAMESPACE | 2 -- man/insert_or.Rd | 12 ++++++------ man/plot_gam.Rd | 5 +++-- 3 files changed, 9 insertions(+), 10 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 85a38c1..87e17ae 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,10 +6,8 @@ export(no_plot) export(or_gam) export(or_glm) export(plot_gam) -import(MASS) import(ggplot2) import(mgcv) -importFrom(cowplot,background_grid) importFrom(grDevices,dev.off) importFrom(grDevices,png) importFrom(graphics,plot) diff --git a/man/insert_or.Rd b/man/insert_or.Rd index a014420..f2be110 100644 --- a/man/insert_or.Rd +++ b/man/insert_or.Rd @@ -5,12 +5,12 @@ \title{Insert odds ratios of GAM(M)s into smoothing function} \usage{ insert_or(plot_object = NULL, or_object = NULL, line_col = "red", - line_size = 1.2, line_type = "solid", line_alpha = 1, text_alpha = 1, - text_size = 4, text_col = "black", rect_alpha = 0.5, rect_col = NULL, - rect = FALSE, arrow = TRUE, values = TRUE, values_yloc = 0, - values_xloc = NULL, or_yloc = 0, arrow_length = NULL, - arrow_yloc = NULL, arrow_col = NULL, arrow_xloc_r = NULL, - arrow_xloc_l = NULL) + line_size = 1.2, line_type = "solid", line_alpha = 1, + text_alpha = 1, text_size = 4, text_col = "black", + rect_alpha = 0.5, rect_col = NULL, rect = FALSE, arrow = TRUE, + values = TRUE, values_yloc = 0, values_xloc = NULL, or_yloc = 0, + arrow_length = NULL, arrow_yloc = NULL, arrow_col = NULL, + arrow_xloc_r = NULL, arrow_xloc_l = NULL) } \arguments{ \item{plot_object}{A \code{ggplot} object from \link{plot_gam}.} diff --git a/man/plot_gam.Rd b/man/plot_gam.Rd index f56f002..4d5e081 100644 --- a/man/plot_gam.Rd +++ b/man/plot_gam.Rd @@ -6,8 +6,9 @@ \usage{ plot_gam(model = NULL, pred = NULL, col_line = "blue", ci_line_col = "black", ci_line_type = "dashed", ci_fill = "grey", - ci_alpha = 0.4, ci_line_size = 0.8, sm_fun_size = 1.1, title = NULL, - xlab = NULL, ylab = NULL, limits_y = NULL, breaks_y = NULL) + ci_alpha = 0.4, ci_line_size = 0.8, sm_fun_size = 1.1, + title = NULL, xlab = NULL, ylab = NULL, limits_y = NULL, + breaks_y = NULL) } \arguments{ \item{model}{A fitted model of class \code{gam}.} From 52d113de76d4cfc62211c0f431a0adc6a3dc3d13 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:11:45 +0200 Subject: [PATCH 08/26] do not import cowplot --- R/insert_or.R | 1 - R/plot_gam.R | 1 - 2 files changed, 2 deletions(-) diff --git a/R/insert_or.R b/R/insert_or.R index 55aa23a..a4c02c0 100644 --- a/R/insert_or.R +++ b/R/insert_or.R @@ -5,7 +5,6 @@ #' a plot of a GAM(M) smoothing function. #' #' @import ggplot2 -#' @importFrom cowplot background_grid #' #' @param plot_object A `ggplot` object from [plot_gam]. #' diff --git a/R/plot_gam.R b/R/plot_gam.R index a464597..8b42d24 100644 --- a/R/plot_gam.R +++ b/R/plot_gam.R @@ -5,7 +5,6 @@ #' using the `ggplot2` plotting system. #' #' @import ggplot2 -#' @importFrom cowplot background_grid #' @importFrom grDevices dev.off png #' @importFrom graphics plot #' @param model A fitted model of class `gam`. From f741d94af35fc70040ee5b8de964b1839c892dbe Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:13:00 +0200 Subject: [PATCH 09/26] style package --- R/data.R | 2 +- R/helper_funs.R | 26 +++--- R/insert_or.R | 140 +++++++++++++++++++------------- R/or_gam.R | 75 ++++++++++------- R/or_glm.R | 47 ++++++----- R/plot_gam.R | 19 +++-- tests/testthat/test-insert-or.R | 10 +-- tests/testthat/test-or-gam.R | 40 +++++---- tests/testthat/test-or-glm.R | 11 ++- 9 files changed, 216 insertions(+), 154 deletions(-) diff --git a/R/data.R b/R/data.R index 8d73e80..ac2b177 100644 --- a/R/data.R +++ b/R/data.R @@ -27,4 +27,4 @@ NULL #' #' @source Taken from \url{http://www.ats.ucla.edu/stat/r/dae/logit.htm}, direct download #' link: \url{http://www.ats.ucla.edu/stat/data/binary.csv} -NULL \ No newline at end of file +NULL diff --git a/R/helper_funs.R b/R/helper_funs.R index 5f70f1b..43d62d7 100644 --- a/R/helper_funs.R +++ b/R/helper_funs.R @@ -24,10 +24,12 @@ #' n <- 200 #' sig <- 2 #' dat <- gamSim(1, n = n, scale = sig, verbose = FALSE) -#' dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), -#' rep("D", 50))) +#' dat$x4 <- as.factor(c( +#' rep("A", 50), rep("B", 50), rep("C", 50), +#' rep("D", 50) +#' )) #' fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + -#' offset(x3) + x4, data = dat) # fit model +#' offset(x3) + x4, data = dat) # fit model #' #' tmp <- plot(fit_gam, pages = 1) # plot output #' tmp <- no_plot(fit_gam) # no plot output @@ -65,10 +67,12 @@ no_plot <- function(model = NULL) { #' n <- 200 #' sig <- 2 #' dat <- gamSim(1, n = n, scale = sig, verbose = FALSE) -#' dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), -#' rep("D", 50))) +#' dat$x4 <- as.factor(c( +#' rep("A", 50), rep("B", 50), rep("C", 50), +#' rep("D", 50) +#' )) #' fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + -#' offset(x3) + x4, data = dat) # fit model +#' offset(x3) + x4, data = dat) # fit model #' #' tmp <- gam_to_df(fit_gam, "x2") #' @export @@ -80,9 +84,11 @@ gam_to_df <- function(model = NULL, pred = NULL) { # get list index of spec. predictor set_pred <- which(grepl(pred, plot_df)) - df <- data.frame(x = plot_df[[set_pred]]$x, - se_upr = plot_df[[set_pred]]$fit + plot_df[[set_pred]]$se, - se_lwr = plot_df[[set_pred]]$fit - plot_df[[set_pred]]$se, - y = plot_df[[set_pred]]$fit) + df <- data.frame( + x = plot_df[[set_pred]]$x, + se_upr = plot_df[[set_pred]]$fit + plot_df[[set_pred]]$se, + se_lwr = plot_df[[set_pred]]$fit - plot_df[[set_pred]]$se, + y = plot_df[[set_pred]]$fit + ) return(df) } diff --git a/R/insert_or.R b/R/insert_or.R index a4c02c0..29438c7 100644 --- a/R/insert_or.R +++ b/R/insert_or.R @@ -59,31 +59,39 @@ #' # load data (Source: ?mgcv::gam) and fit model #' library(mgcv) #' fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + -#' offset(x3) + x4, data = data_gam) # fit model +#' offset(x3) + x4, data = data_gam) # fit model #' #' # create input objects (plot + odds ratios) #' library(oddsratio) #' plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") -#' or_object1 <- or_gam(data = data_gam, model = fit_gam, -#' pred = "x2", values = c(0.099, 0.198)) +#' or_object1 <- or_gam( +#' data = data_gam, model = fit_gam, +#' pred = "x2", values = c(0.099, 0.198) +#' ) #' #' # insert first odds ratios to plot -#' plot_object <- insert_or(plot_object, or_object1, or_yloc = 3, -#' values_xloc = 0.04, line_size = 0.5, -#' line_type = "dotdash", text_size = 6, -#' values_yloc = 0.5, arrow_col = "red") +#' plot_object <- insert_or(plot_object, or_object1, +#' or_yloc = 3, +#' values_xloc = 0.04, line_size = 0.5, +#' line_type = "dotdash", text_size = 6, +#' values_yloc = 0.5, arrow_col = "red" +#' ) #' #' # calculate second odds ratio -#' or_object2 <- or_gam(data = data_gam, model = fit_gam, pred = "x2", -#' values = c(0.4, 0.6)) +#' or_object2 <- or_gam( +#' data = data_gam, model = fit_gam, pred = "x2", +#' values = c(0.4, 0.6) +#' ) #' #' # add or_object2 into plot -#' insert_or(plot_object, or_object2, or_yloc = 2.1, values_yloc = 2, -#' line_col = "green4", text_col = "black", -#' rect_col = "green4", rect_alpha = 0.2, -#' line_alpha = 1, line_type = "dashed", -#' arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, -#' arrow_length = 0.01, rect = TRUE) +#' insert_or(plot_object, or_object2, +#' or_yloc = 2.1, values_yloc = 2, +#' line_col = "green4", text_col = "black", +#' rect_col = "green4", rect_alpha = 0.2, +#' line_alpha = 1, line_type = "dashed", +#' arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, +#' arrow_length = 0.01, rect = TRUE +#' ) #' @export insert_or <- function(plot_object = NULL, or_object = NULL, line_col = "red", # nocov start # nolint @@ -96,14 +104,20 @@ insert_or <- function(plot_object = NULL, or_object = NULL, line_col = "red", # arrow_xloc_r = NULL, arrow_xloc_l = NULL) { plot_object <- plot_object + - geom_vline(xintercept = or_object$value1, color = line_col, - size = line_size, linetype = line_type, alpha = line_alpha) + - geom_vline(xintercept = or_object$value2, color = line_col, - size = line_size, linetype = line_type, alpha = line_alpha) + - annotate("text", x = mean(c(or_object$value2, or_object$value1)), - y = min(plot_object$data$se_lwr) + or_yloc, - label = paste0("OR: \n", round(or_object$oddsratio, 2)), - color = text_col, size = text_size) + geom_vline( + xintercept = or_object$value1, color = line_col, + size = line_size, linetype = line_type, alpha = line_alpha + ) + + geom_vline( + xintercept = or_object$value2, color = line_col, + size = line_size, linetype = line_type, alpha = line_alpha + ) + + annotate("text", + x = mean(c(or_object$value2, or_object$value1)), + y = min(plot_object$data$se_lwr) + or_yloc, + label = paste0("OR: \n", round(or_object$oddsratio, 2)), + color = text_col, size = text_size + ) if (rect) { if (is.null(rect_col)) { @@ -111,15 +125,19 @@ insert_or <- function(plot_object = NULL, or_object = NULL, line_col = "red", # } # set drawing order to place rect behind smoothing fun - plot_object$layers <- c(geom_rect(data = plot_object$data[1,], # avoids multiple rect drawings # nolint - ymin = ggplot_build(plot_object)$layout$ - panel_params[[1]]$y.range[1], - ymax = ggplot_build(plot_object)$layout$ - panel_params[[1]]$y.range[2], - xmin = or_object$value1, - xmax = or_object$value2, - alpha = rect_alpha, fill = rect_col), - plot_object$layers) + plot_object$layers <- c( + geom_rect( + data = plot_object$data[1, ], # avoids multiple rect drawings # nolint + ymin = ggplot_build(plot_object)$layout$ + panel_params[[1]]$y.range[1], + ymax = ggplot_build(plot_object)$layout$ + panel_params[[1]]$y.range[2], + xmin = or_object$value1, + xmax = or_object$value2, + alpha = rect_alpha, fill = rect_col + ), + plot_object$layers + ) } if (values) { @@ -142,7 +160,7 @@ insert_or <- function(plot_object = NULL, or_object = NULL, line_col = "red", # if (is.null(arrow_xloc_l)) { # calc arrow shift from x axis range - arrow_xloc_l <- - (max(plot_object$data$y) - min(plot_object$data$y)) * + arrow_xloc_l <- -(max(plot_object$data$y) - min(plot_object$data$y)) * 0.002 } @@ -154,14 +172,18 @@ insert_or <- function(plot_object = NULL, or_object = NULL, line_col = "red", # plot_object <- plot_object + - annotate("text", x = or_object$value1 - values_xloc, - y = min(plot_object$data$se_lwr) + values_yloc, - label = or_object$value1, - color = text_col, alpha = text_alpha, size = text_size) + - annotate("text", x = or_object$value2 + values_xloc, - y = min(plot_object$data$se_lwr) + values_yloc, - label = or_object$value2, - color = text_col, alpha = text_alpha, size = text_size) + annotate("text", + x = or_object$value1 - values_xloc, + y = min(plot_object$data$se_lwr) + values_yloc, + label = or_object$value1, + color = text_col, alpha = text_alpha, size = text_size + ) + + annotate("text", + x = or_object$value2 + values_xloc, + y = min(plot_object$data$se_lwr) + values_yloc, + label = or_object$value2, + color = text_col, alpha = text_alpha, size = text_size + ) if (arrow) { @@ -173,23 +195,27 @@ insert_or <- function(plot_object = NULL, or_object = NULL, line_col = "red", # plot_object <- plot_object + # left arrow - geom_segment(x = or_object$value1 - values_xloc + arrow_xloc_l, - xend = or_object$value1 - values_xloc + arrow_length, - y = min(plot_object$data$se_lwr) + values_yloc + - arrow_yloc, - yend = min(plot_object$data$se_lwr) + values_yloc + - arrow_yloc, - color = arrow_col, - arrow = arrow(length = unit(0.2, "cm"), type = "closed")) + + geom_segment( + x = or_object$value1 - values_xloc + arrow_xloc_l, + xend = or_object$value1 - values_xloc + arrow_length, + y = min(plot_object$data$se_lwr) + values_yloc + + arrow_yloc, + yend = min(plot_object$data$se_lwr) + values_yloc + + arrow_yloc, + color = arrow_col, + arrow = arrow(length = unit(0.2, "cm"), type = "closed") + ) + # right arrow - geom_segment(x = or_object$value2 + values_xloc + arrow_xloc_r, - xend = or_object$value2 + values_xloc - arrow_length, - y = min(plot_object$data$se_lwr) + values_yloc + - arrow_yloc, - yend = min(plot_object$data$se_lwr) + values_yloc + - arrow_yloc, - color = arrow_col, - arrow = arrow(length = unit(0.2, "cm"), type = "closed")) + geom_segment( + x = or_object$value2 + values_xloc + arrow_xloc_r, + xend = or_object$value2 + values_xloc - arrow_length, + y = min(plot_object$data$se_lwr) + values_yloc + + arrow_yloc, + yend = min(plot_object$data$se_lwr) + values_yloc + + arrow_yloc, + color = arrow_col, + arrow = arrow(length = unit(0.2, "cm"), type = "closed") + ) } } return(plot_object) diff --git a/R/or_gam.R b/R/or_gam.R index e169ab5..009e591 100644 --- a/R/or_gam.R +++ b/R/or_gam.R @@ -60,21 +60,26 @@ #' # load data (Source: ?mgcv::gam) and fit model #' library(mgcv) #' fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + -#' offset(x3) + x4, data = data_gam) # fit model +#' offset(x3) + x4, data = data_gam) # fit model #' #' # Calculate OR for specific increment step of continuous variable -#' or_gam(data = data_gam, model = fit_gam, pred = "x2", -#' values = c(0.099, 0.198)) +#' or_gam( +#' data = data_gam, model = fit_gam, pred = "x2", +#' values = c(0.099, 0.198) +#' ) #' #' ## Calculate OR for change of indicator variable -#' or_gam(data = data_gam, model = fit_gam, pred = "x4", -#' values = c("B", "D")) +#' or_gam( +#' data = data_gam, model = fit_gam, pred = "x4", +#' values = c("B", "D") +#' ) #' #' ## Calculate ORs for percentage increments of predictor distribution #' ## (here: 20%) -#' or_gam(data = data_gam, model = fit_gam, pred = "x2", -#' percentage = 20, slice = TRUE) -#' +#' or_gam( +#' data = data_gam, model = fit_gam, pred = "x2", +#' percentage = 20, slice = TRUE +#' ) #' @export or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, percentage = NULL, slice = FALSE, CI = NULL) { @@ -97,23 +102,26 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, range_v <- c(range_v, min(data[, pred]) + step * i) } - result <- data.frame(predictor = length(100 / percentage), - value1 = numeric(length = 100 / percentage), - value2 = numeric(length = 100 / percentage), - perc1 = character(length = 100 / percentage), - perc2 = character(length = 100 / percentage), - oddsratio = numeric(length = 100 / percentage), - CI_low = numeric(length = 100 / percentage), - CI_high = numeric(length = 100 / percentage), - stringsAsFactors = FALSE) + result <- data.frame( + predictor = length(100 / percentage), + value1 = numeric(length = 100 / percentage), + value2 = numeric(length = 100 / percentage), + perc1 = character(length = 100 / percentage), + perc2 = character(length = 100 / percentage), + oddsratio = numeric(length = 100 / percentage), + CI_low = numeric(length = 100 / percentage), + CI_high = numeric(length = 100 / percentage), + stringsAsFactors = FALSE + ) # apply OR calc for vector for (x in 1:(100 / percentage)) { # set all preds to their mean if they are numeric for (i in names_pred) { - if (is.numeric(data[[i]])) + if (is.numeric(data[[i]])) { data[[i]] <- mean(data[[i]]) + } } # reduce to 1 row @@ -125,7 +133,8 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, data[, pred] <- range_v[x] # calc log odds for value 1 pred_gam1 <- as.numeric(predict(model, data, - type = "link", se.fit = TRUE)) + type = "link", se.fit = TRUE + )) # calc 95% CI log odds (mean +- 2* stdev) pred_gam1_CI_low <- pred_gam1[1] - (2 * pred_gam1[2]) pred_gam1_CI_high <- pred_gam1[1] + (2 * pred_gam1[2]) @@ -134,20 +143,21 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, data[, pred] <- range_v[x + 1] # calc log odds for value 2 pred_gam2 <- as.numeric(predict(model, data, - type = "link", se.fit = TRUE)) + type = "link", se.fit = TRUE + )) # calc 95% CI log odds (mean +- 2* stdev) pred_gam2_CI_low <- pred_gam2[1] - (2 * pred_gam2[2]) pred_gam2_CI_high <- pred_gam2[1] + (2 * pred_gam2[2]) result$predictor <- pred result$oddsratio[x] <- round(as.numeric(exp(pred_gam2[1] - - pred_gam1[1])), 2) + pred_gam1[1])), 2) result$value1[x] <- round(range_v[x], 3) result$value2[x] <- round(range_v[x + 1], 3) result$CI_high[x] <- round(as.numeric(exp(pred_gam2_CI_low - - pred_gam1_CI_low)), 2) # no mistake # nolint + pred_gam1_CI_low)), 2) # no mistake # nolint result$CI_low[x] <- round(as.numeric(exp(pred_gam2_CI_high - - pred_gam1_CI_high)), 2) # no mistake # nolint + pred_gam1_CI_high)), 2) # no mistake # nolint result$perc1[x] <- percentage * x - percentage result$perc2[x] <- percentage * x } @@ -161,8 +171,9 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, # set all preds to their mean if they are numeric for (i in names_pred) { - if (is.numeric(data[[i]])) + if (is.numeric(data[[i]])) { data[[i]] <- mean(data[[i]]) + } } # reduce to 1 row @@ -192,13 +203,15 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, odds_ratio_low <- as.numeric(exp(pred_gam2_CI_low - pred_gam1_CI_low), 2) odds_ratio_high <- as.numeric(exp(pred_gam2_CI_high - pred_gam1_CI_high), 2) - result <- data.frame(predictor = pred, - value1 = values[1], - value2 = values[2], - oddsratio = odds_ratio, - CI_low = odds_ratio_high, # no mistake - CI_high = odds_ratio_low, # no mistake - stringsAsFactors = FALSE) + result <- data.frame( + predictor = pred, + value1 = values[1], + value2 = values[2], + oddsratio = odds_ratio, + CI_low = odds_ratio_high, # no mistake + CI_high = odds_ratio_low, # no mistake + stringsAsFactors = FALSE + ) # change col names colnames(result)[5] <- paste0("CI_low (2.5%)") diff --git a/R/or_glm.R b/R/or_glm.R index e012d22..5db2911 100644 --- a/R/or_glm.R +++ b/R/or_glm.R @@ -31,27 +31,32 @@ #' ## Example with glm() #' # load data (source: http://www.ats.ucla.edu/stat/r/dae/logit.htm) and #' # fit model -#' fit_glm <- glm(admit ~ gre + gpa + rank, data = data_glm, -#' family = "binomial") # fit model +#' fit_glm <- glm(admit ~ gre + gpa + rank, +#' data = data_glm, +#' family = "binomial" +#' ) # fit model #' #' # Calculate OR for specific increment step of continuous variable #' or_glm(data = data_glm, model = fit_glm, incr = list(gre = 380, gpa = 5)) #' #' # Calculate OR and change the confidence interval level -#' or_glm(data = data_glm, model = fit_glm, -#' incr = list(gre = 380, gpa = 5), CI = .70) +#' or_glm( +#' data = data_glm, model = fit_glm, +#' incr = list(gre = 380, gpa = 5), CI = .70 +#' ) #' #' ## Example with MASS:glmmPQL() #' # load data #' library(MASS) #' data(bacteria) -#' fit_glmmPQL <- glmmPQL(y ~ trt + week, random = ~1 | ID, -#' family = binomial, data = bacteria, -#' verbose = FALSE) +#' fit_glmmPQL <- glmmPQL(y ~ trt + week, +#' random = ~ 1 | ID, +#' family = binomial, data = bacteria, +#' verbose = FALSE +#' ) #' #' # Apply function #' or_glm(data = bacteria, model = fit_glmmPQL, incr = list(week = 5)) -#' #' @details Currently supported functions: [glm], #' [glmmPQL] #' @@ -83,18 +88,19 @@ or_glm <- function(data, model, incr, CI = 0.95) { # CI calculation if (class(model)[1] == "glm") { CI_list <- as.data.frame(suppressMessages(confint(model, - level = CI))) [-1, ] + level = CI + ))) [-1, ] } # check if predictor is numeric or integer if (is.numeric(data[[i]]) | is.integer(data[[i]])) { odds_ratios[[i]] <- round(exp(as.numeric(coef[[i]]) * - as.numeric(incr[[i]])), 3) + as.numeric(incr[[i]])), 3) if (!class(model)[1] == "glmmPQL") { CI_low[[i]] <- round(exp(CI_list[i, 1] * # nocov start - as.numeric(incr[[i]])), 3) + as.numeric(incr[[i]])), 3) CI_high[[i]] <- round(exp(CI_list[i, 2] * - as.numeric(incr[[i]])), 3) # nocov end + as.numeric(incr[[i]])), 3) # nocov end } increments[[i]] <- as.numeric(incr[[i]]) or <- odds_ratios[[i]] # nolint @@ -120,13 +126,16 @@ or_glm <- function(data, model, incr, CI = 0.95) { } # create data frame to return - result <- data.frame(predictor = as.character(names(odds_ratios)), - oddsratio = unlist(odds_ratios, use.names = FALSE), - CI_low = unlist(CI_low, use.names = FALSE), - CI_high = unlist(CI_high, use.names = FALSE), - increment = as.character(unlist(increments, - use.names = FALSE)), - stringsAsFactors = FALSE) + result <- data.frame( + predictor = as.character(names(odds_ratios)), + oddsratio = unlist(odds_ratios, use.names = FALSE), + CI_low = unlist(CI_low, use.names = FALSE), + CI_high = unlist(CI_high, use.names = FALSE), + increment = as.character(unlist(increments, + use.names = FALSE + )), + stringsAsFactors = FALSE + ) # set CI column names if (class(model)[1] == "glm") { diff --git a/R/plot_gam.R b/R/plot_gam.R index 8b42d24..b97e27a 100644 --- a/R/plot_gam.R +++ b/R/plot_gam.R @@ -43,11 +43,11 @@ #' # load data (Source: ?mgcv::gam) and fit model #' library(mgcv) #' fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, -#' data = data_gam) +#' data = data_gam +#' ) #' #' library(oddsratio) #' plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") -#' #' @seealso [plot_gam] #' @seealso [or_gam] #' @seealso [insert_or] @@ -74,12 +74,17 @@ plot_gam <- function(model = NULL, pred = NULL, col_line = "blue", # nocov start plot_gam <- ggplot(df, aes_(~x, ~y)) + geom_line(colour = col_line, size = sm_fun_size) + - geom_line(aes_(~x, ~se_upr), linetype = ci_line_type, - colour = ci_line_col, size = ci_line_size) + - geom_line(aes_(~x, ~se_lwr), linetype = ci_line_type, - colour = ci_line_col, size = ci_line_size) + + geom_line(aes_(~x, ~se_upr), + linetype = ci_line_type, + colour = ci_line_col, size = ci_line_size + ) + + geom_line(aes_(~x, ~se_lwr), + linetype = ci_line_type, + colour = ci_line_col, size = ci_line_size + ) + geom_ribbon(aes_(x = ~x, ymin = ~se_lwr, ymax = ~se_upr), - fill = ci_fill, alpha = ci_alpha) + + fill = ci_fill, alpha = ci_alpha + ) + ylab(ylab) + xlab(xlab) diff --git a/tests/testthat/test-insert-or.R b/tests/testthat/test-insert-or.R index aefb42b..4c8692a 100644 --- a/tests/testthat/test-insert-or.R +++ b/tests/testthat/test-insert-or.R @@ -2,8 +2,9 @@ context("insert_or") test_that("check bevhaviour of ggplot_build (changed in ggplot2 v2.2)", { data("data_gam") - fit_gam <- gam(y ~ s(x0) + s(I(x1 ^ 2)) + s(x2) + offset(x3) + x4, - data = data_gam) + fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, + data = data_gam + ) library(oddsratio) plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") @@ -15,7 +16,7 @@ test_that("check bevhaviour of ggplot_build (changed in ggplot2 v2.2)", { # ggplot2 v2.2.x # ymin <- ggplot_build(plot_object)$layout$panel_ranges[[1]]$y.range[1] # ymax <- ggplot_build(plot_object)$layout$panel_ranges[[1]]$y.range[2] - + # ggplot2 v2.3.x library(ggplot2) ymin <- ggplot_build(plot_object)$layout$panel_params[[1]]$y.range[1] @@ -24,6 +25,5 @@ test_that("check bevhaviour of ggplot_build (changed in ggplot2 v2.2)", { expect_equal(class(ymin), "numeric") expect_equal(class(ymax), "numeric") expect_equal(length(ggplot_build(plot_object)$layout$panel_params[[1]]$ - y.range), 2) - + y.range), 2) }) diff --git a/tests/testthat/test-or-gam.R b/tests/testthat/test-or-gam.R index ec307bc..cfa74ec 100644 --- a/tests/testthat/test-or-gam.R +++ b/tests/testthat/test-or-gam.R @@ -3,50 +3,54 @@ context("or_gam") library(mgcv) test_that("or_gam works for continuous variable", { - data("data_gam") library(mgcv) - fit_gam <- gam(y ~ s(x0) + s(I(x1 ^ 2)) + s(x2) + - offset(x3) + x4, data = data_gam) # fit model + fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + + offset(x3) + x4, data = data_gam) # fit model # Calculate OR for specific increment step of continuous variable - out <- or_gam(data = data_gam, model = fit_gam, pred = "x2", - values = c(0.099, 0.198)) + out <- or_gam( + data = data_gam, model = fit_gam, pred = "x2", + values = c(0.099, 0.198) + ) expect_length(out, 6) }) test_that("or_gam works with indicator variables", { - data("data_gam") library(mgcv) - fit_gam <- gam(y ~ s(x0) + s(I(x1 ^ 2)) + s(x2) + - offset(x3) + x4, data = data_gam) # fit model + fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + + offset(x3) + x4, data = data_gam) # fit model ## Calculate OR for change of indicator variable - out <- or_gam(data = data_gam, model = fit_gam, pred = "x4", - values = c("B", "D")) + out <- or_gam( + data = data_gam, model = fit_gam, pred = "x4", + values = c("B", "D") + ) expect_length(out, 6) ## Calculate ORs for percentage increments of predictor distribution ## (here: 20%) - or_gam(data = data_gam, model = fit_gam, pred = "x2", - percentage = 20, slice = TRUE) - + or_gam( + data = data_gam, model = fit_gam, pred = "x2", + percentage = 20, slice = TRUE + ) }) test_that("or_gam works on percentage increments", { - data("data_gam") library(mgcv) - fit_gam <- gam(y ~ s(x0) + s(I(x1 ^ 2)) + s(x2) + - offset(x3) + x4, data = data_gam) # fit model + fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + + offset(x3) + x4, data = data_gam) # fit model ## Calculate ORs for percentage increments of predictor distribution ## (here: 20%) - out <- or_gam(data = data_gam, model = fit_gam, pred = "x2", - percentage = 20, slice = TRUE) + out <- or_gam( + data = data_gam, model = fit_gam, pred = "x2", + percentage = 20, slice = TRUE + ) expect_length(out, 8) }) diff --git a/tests/testthat/test-or-glm.R b/tests/testthat/test-or-glm.R index 1fc2b18..dbeef42 100644 --- a/tests/testthat/test-or-glm.R +++ b/tests/testthat/test-or-glm.R @@ -10,18 +10,17 @@ test_that("correct level count of indicator variable for glm", { out <- or_glm(data = data_glm, model = fit_glm) expect_length(out$predictor, length(levels(data_glm$rank)) - 1) - }) test_that("or_glm works with glmmPQL", { - data(bacteria) - fit_glmmpql <- glmmPQL(y ~ trt + week, random = ~1 | ID, - family = binomial, data = bacteria, - verbose = FALSE) + fit_glmmpql <- glmmPQL(y ~ trt + week, + random = ~ 1 | ID, + family = binomial, data = bacteria, + verbose = FALSE + ) # Apply function out <- or_glm(data = bacteria, model = fit_glmmpql, incr = list(week = 5)) expect_length(out, 5) - }) From 70d5f7efc0a587373ade7c9ab62f55ac1e3cd70a Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:13:08 +0200 Subject: [PATCH 10/26] update version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8e9040f..f4b5370 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: oddsratio Title: Odds Ratio Calculation for GAM(M)s & GLM(M)s -Version: 1.1.0 +Version: 2.0.0 Date: 2019-06-13 Authors@R: person(given = "Patrick", From 21a57009c0739c69847e1aeb3af3d4b8ddc4189b Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:17:39 +0200 Subject: [PATCH 11/26] clear inst --- inst/Plots/smoothing_function_oddsratio.png | Bin 38560 -> 0 bytes .../smoothing_function_oddsratio_two.png | Bin 41470 -> 0 bytes inst/Plots/smoothing_funtion.png | Bin 36101 -> 0 bytes inst/convert_to_ascii_news.sh | 7 - inst/doc/.gitignore | 1 - inst/wordpress.Rmd | 168 --------------- inst/wordpress.md | 192 ------------------ .../unnamed-chunk-6-1.png | Bin 37441 -> 0 bytes .../unnamed-chunk-7-1.png | Bin 42049 -> 0 bytes .../unnamed-chunk-8-1.png | Bin 45981 -> 0 bytes 10 files changed, 368 deletions(-) delete mode 100644 inst/Plots/smoothing_function_oddsratio.png delete mode 100644 inst/Plots/smoothing_function_oddsratio_two.png delete mode 100644 inst/Plots/smoothing_funtion.png delete mode 100644 inst/convert_to_ascii_news.sh delete mode 100644 inst/doc/.gitignore delete mode 100644 inst/wordpress.Rmd delete mode 100644 inst/wordpress.md delete mode 100644 inst/wordpress_files/figure-markdown_strict/unnamed-chunk-6-1.png delete mode 100644 inst/wordpress_files/figure-markdown_strict/unnamed-chunk-7-1.png delete mode 100644 inst/wordpress_files/figure-markdown_strict/unnamed-chunk-8-1.png diff --git a/inst/Plots/smoothing_function_oddsratio.png b/inst/Plots/smoothing_function_oddsratio.png deleted file mode 100644 index 0dadf5d37dda442745be4e4eeed49f9910e97932..0000000000000000000000000000000000000000 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a/inst/convert_to_ascii_news.sh b/inst/convert_to_ascii_news.sh deleted file mode 100644 index 368a9b7..0000000 --- a/inst/convert_to_ascii_news.sh +++ /dev/null @@ -1,7 +0,0 @@ -# Part 1: Replaces all level 2 headers and appends a ":" at the end of the line -# Part 2: Indents all bullet points with a whitespace -# Part 3: Removes all level 2 headers -# Part 4: For all level 1 headers, add linebreak and 80 hyphens (not strictly required but clean) -# Part 5: Remove all level 1 headers - -sed -e '/^##/ s/$/:/' -e 's/^*/ */' -e 's/^## *//' -e "/^#/a\\--------------------------------------------------------------------------------" -e 's/^# *//' < NEWS.md > NEWS diff --git a/inst/doc/.gitignore b/inst/doc/.gitignore deleted file mode 100644 index 5bfb386..0000000 --- a/inst/doc/.gitignore +++ /dev/null @@ -1 +0,0 @@ -function.tutorial.html diff --git a/inst/wordpress.Rmd b/inst/wordpress.Rmd deleted file mode 100644 index 3883a19..0000000 --- a/inst/wordpress.Rmd +++ /dev/null @@ -1,168 +0,0 @@ ---- -output: - md_document - ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - # fig.path = "figures/README-", - fig.align = "center", - fig.height = 4, - fig.width = 6, - collapse = TRUE, - comment = "#>" -) -library(ggplot2) -library(cowplot) -``` - -## Load example data - -Data source: `?mgcv::predict.gam` - -```{r, results='hide'} -library(oddsratio) -suppressPackageStartupMessages(library(mgcv)) -set.seed(1234) -n <- 200 -sig <- 2 -dat <- suppressMessages(gamSim(1, n = n, scale = sig)) -dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), rep("D", 50))) - -fit.gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, data = dat) -``` - -## GAM example - -### Calculate OR for specific increment step of continuous variable - -To calculate specific increment steps of `fit.gam`, we take predictor `x2` (randomly chosen) -and specify for which values we want to calculate the odds ratio. -We can see that the odds of response `y` happening are 22 times higher when predictor `x2` increases -from 0.099 to 0.198 while holding all other predictors constant. - - -```{r} -calc.oddsratio.gam(data = dat, model = fit.gam, pred = "x2", - values = c(0.099, 0.198)) -``` - -Usually, this calculation is done by setting all predictors to their mean value, -predict the response, change the desired predictor to a new value and predict the response again. -These actions results in two log odds values, respectively, which are transformed into odds by exponentiating them. Finally, the odds ratio can be calculated from these two odds values. - -### Calculate OR for level change of indicator variable - -If the predictor is a indicator variable, i.e. consists of fixed levels, you can use the function in the same way by just putting in the respective levels you are interested in: - -```{r} -calc.oddsratio.gam(data = dat, model = fit.gam, - pred = "x4", values = c("A", "B")) -``` - -Here, the change in odds of `y` happening if predictor `x4` is changing from level `A` to `B` is rather small. In detail, an increase in odds of 37.8% is reported. - -### Calculate ORs for percentage increments of predictor distribution - -To get an impression of odds ratio behaviour throughout the complete range of the smoothing function of the fitted GAM model, you can calculate odds ratios based on percentage breaks of the predictors distribution. -Here we slice predictor `x2` into 5 parts by taking the predictor values of every 20% increment step. - - -```{r} -calc.oddsratio.gam(data = dat, model = fit.gam, pred = "x2", - percentage = 20, slice = TRUE) -``` - -We can see that there is a high odds ratio reported when increasing predictor `x2` from 0.008 to 0.206 while all further predictor increases decrease the odds of response `y` happening substantially. - -### Plot GAM(M) smoothing functions - -Right now, the only (quick) possibility to plot the smoothing functions of a GAM(M) -was to use the base `plot()` function. The fiddly work to do the same using the `ggplot2` -plotting system is done by `plot_smooth.gam()`: - -```{r} -plot_smooth.gam(fit.gam, pred = "x2", title = "Predictor 'x2'") -``` - -You can further customize the look using other colors or linetypes. - -### Add odds ratio information into smoothing function plot - -So now, we have the odds ratios and we have a plot of the smoothing function. -Why not combine both? We can do so using `add.oddsratio.into.plot()`. Its main arguments -are (i) a `ggplot` plotting object containing the smooth function and a data frame -returned from `calc.oddsratio.gam()` containing information about the predictor and -the respective values we want to insert. - -```{r} -plot.object <- plot_smooth.gam(fit.gam, pred = "x2", title = "Predictor 'x2'") -or.object <- calc.oddsratio.gam(data = dat, model = fit.gam, - pred = "x2", values = c(0.099, 0.198)) - -plot <- add.oddsratio.into.plot(plot.object, or.object, height.or = 5, x.shift = 0.04) -plot -``` - -The odds ratio information is always centered between the two vertical lines. Hence it only -looks nice if the gap between the two chosen values (here 0.099 and 0.198) is large enough. -If the smoothing line crosses your inserted text, you can just correct it adjusting `height.or`. This param sets the y-location of the inserted odds ratio information. - -Depending on the digits of your chosen values (here 3), you might also need to adjust the -x-axis location of the two values so that they do not interfer with the vertical line. - -Let's do all this by inserting another odds ratio into this plot! This time we simply -take the already produced plot as an input to `add.oddsratio.into.plot()` and use a new odds ratio -object: - -```{r} -or.object2 <- calc.oddsratio.gam(data = dat, model = fit.gam, - pred = "x2", values = c(0.4, 0.6)) - -add.oddsratio.into.plot(plot, or.object2, height.or = 2.5, x.shift = 0.024, - col.line = "green4", col.text = "green4") -``` - -Note that I adjusted `x.shift` because we have only one digit this time. Also, -`height.or` was set to a lower value than in the first example to avoid an interference -with the smoothing function. - -## GLM example - -Create example data. -Data source: http://www.ats.ucla.edu/stat/r/dae/logit.htm - -```{r} -dat <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") -dat$rank <- factor(dat$rank) -fit.glm <- glm(admit ~ gre + gpa + rank, data = dat, family = "binomial") -``` - -### Calculate odds ratio for continuous predictors - -For GLMs, the odds ratio calculation is simpler because odds ratio changes correspond to fixed predictor increases throughout the complete value range of each predictor. - -Hence, function `calc.oddsratio.glm` takes the increment steps of each predictor directly as an input in its parameter `incr`. - -To avoid false predictor/value assignments, the combinations need to be given in a list. - -Odds ratios of indicator variables are computed automatically and do always refer to the base factor level. - -Indicator predictor `rank` has four levels. Subsequently, we will get three odds ratio outputs referring to the base factor level (here: rank1). - -The output is interpreted as follows: "Having `rank2` instead of `rank1` while holding all other values constant results in a decrease in odds of 49.1% (1-0.509)". - -```{r} -calc.oddsratio.glm(data = dat, model = fit.glm, incr = list(gre = 380, gpa = 5)) -``` - -You can also set other confident intervals for GLM(M) models. The resulting data -frame will automatically adjust its column names to the specified level. - -```{r} -calc.oddsratio.glm(data = dat, model = fit.glm, - incr = list(gre = 380, gpa = 5), CI = 0.70) -``` - - diff --git a/inst/wordpress.md b/inst/wordpress.md deleted file mode 100644 index e7c7770..0000000 --- a/inst/wordpress.md +++ /dev/null @@ -1,192 +0,0 @@ -Load example data ------------------ - -Data source: `?mgcv::predict.gam` - - library(oddsratio) - suppressPackageStartupMessages(library(mgcv)) - set.seed(1234) - n <- 200 - sig <- 2 - dat <- suppressMessages(gamSim(1, n = n, scale = sig)) - dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), rep("D", 50))) - - fit.gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, data = dat) - -GAM example ------------ - -### Calculate OR for specific increment step of continuous variable - -To calculate specific increment steps of `fit.gam`, we take predictor -`x2` (randomly chosen) and specify for which values we want to calculate -the odds ratio. -We can see that the odds of response `y` happening are 22 times higher -when predictor `x2` increases from 0.099 to 0.198 while holding all -other predictors constant. - - calc.oddsratio.gam(data = dat, model = fit.gam, pred = "x2", - values = c(0.099, 0.198)) - #> predictor value1 value2 oddsratio CI.low (2.5%) CI.high (97.5%) - #> 1 x2 0.099 0.198 23.32353 23.30424 23.34283 - -Usually, this calculation is done by setting all predictors to their -mean value, predict the response, change the desired predictor to a new -value and predict the response again. These actions results in two log -odds values, respectively, which are transformed into odds by -exponentiating them. Finally, the odds ratio can be calculated from -these two odds values. - -### Calculate OR for level change of indicator variable - -If the predictor is a indicator variable, i.e. consists of fixed levels, -you can use the function in the same way by just putting in the -respective levels you are interested in: - - calc.oddsratio.gam(data = dat, model = fit.gam, - pred = "x4", values = c("A", "B")) - #> predictor value1 value2 oddsratio CI.low (2.5%) CI.high (97.5%) - #> 1 x4 A B 1.377537 1.334837 1.421604 - -Here, the change in odds of `y` happening if predictor `x4` is changing -from level `A` to `B` is rather small. In detail, an increase in odds of -37.8% is reported. - -### Calculate ORs for percentage increments of predictor distribution - -To get an impression of odds ratio behaviour throughout the complete -range of the smoothing function of the fitted GAM model, you can -calculate odds ratios based on percentage breaks of the predictors -distribution. -Here we slice predictor `x2` into 5 parts by taking the predictor values -of every 20% increment step. - - calc.oddsratio.gam(data = dat, model = fit.gam, pred = "x2", - percentage = 20, slice = TRUE) - #> predictor value1 value2 perc1 perc2 oddsratio CI.low (2.5%) - #> 1 x2 0.001 0.200 0 20 2.510768e+03 1.091683e+03 - #> 2 x2 0.200 0.400 20 40 2.870699e-02 2.621879e-02 - #> 3 x2 0.400 0.599 40 60 5.761210e-01 5.556941e-01 - #> 4 x2 0.599 0.799 60 80 6.032289e-02 5.789875e-02 - #> 5 x2 0.799 0.998 80 100 4.063187e-01 7.469151e-01 - #> CI.high (97.5%) - #> 1 5.774533e+03 - #> 2 3.143133e-02 - #> 3 5.972988e-01 - #> 4 6.284853e-02 - #> 5 2.210357e-01 - -We can see that there is a high odds ratio reported when increasing -predictor `x2` from 0.008 to 0.206 while all further predictor increases -decrease the odds of response `y` happening substantially. - -### Plot GAM(M) smoothing functions - -Right now, the only (quick) possibility to plot the smoothing functions -of a GAM(M) was to use the base `plot()` function. The fiddly work to do -the same using the `ggplot2` plotting system is done by -`plot_smooth.gam()`: - - plot_smooth.gam(fit.gam, pred = "x2", title = "Predictor 'x2'") - - - -You can further customize the look using other colors or linetypes. - -### Add odds ratio information into smoothing function plot - -So now, we have the odds ratios and we have a plot of the smoothing -function. Why not combine both? We can do so using -`add.oddsratio.into.plot()`. Its main arguments are (i) a `ggplot` -plotting object containing the smooth function and a data frame returned -from `calc.oddsratio.gam()` containing information about the predictor -and the respective values we want to insert. - - plot.object <- plot_smooth.gam(fit.gam, pred = "x2", title = "Predictor 'x2'") - or.object <- calc.oddsratio.gam(data = dat, model = fit.gam, - pred = "x2", values = c(0.099, 0.198)) - - plot <- add.oddsratio.into.plot(plot.object, or.object, height.or = 5, x.shift = 0.04) - plot - - - -The odds ratio information is always centered between the two vertical -lines. Hence it only looks nice if the gap between the two chosen values -(here 0.099 and 0.198) is large enough. If the smoothing line crosses -your inserted text, you can just correct it adjusting `height.or`. This -param sets the y-location of the inserted odds ratio information. - -Depending on the digits of your chosen values (here 3), you might also -need to adjust the x-axis location of the two values so that they do not -interfer with the vertical line. - -Let's do all this by inserting another odds ratio into this plot! This -time we simply take the already produced plot as an input to -`add.oddsratio.into.plot()` and use a new odds ratio object: - - or.object2 <- calc.oddsratio.gam(data = dat, model = fit.gam, - pred = "x2", values = c(0.4, 0.6)) - - add.oddsratio.into.plot(plot, or.object2, height.or = 2.5, x.shift = 0.024, - col.line = "green4", col.text = "green4") - - - -Note that I adjusted `x.shift` because we have only one digit this time. -Also, `height.or` was set to a lower value than in the first example to -avoid an interference with the smoothing function. - -GLM example ------------ - -Create example data. -Data source: - - dat <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") - dat$rank <- factor(dat$rank) - fit.glm <- glm(admit ~ gre + gpa + rank, data = dat, family = "binomial") - -### Calculate odds ratio for continuous predictors - -For GLMs, the odds ratio calculation is simpler because odds ratio -changes correspond to fixed predictor increases throughout the complete -value range of each predictor. - -Hence, function `calc.oddsratio.glm` takes the increment steps of each -predictor directly as an input in its parameter `incr`. - -To avoid false predictor/value assignments, the combinations need to be -given in a list. - -Odds ratios of indicator variables are computed automatically and do -always refer to the base factor level. - -Indicator predictor `rank` has four levels. Subsequently, we will get -three odds ratio outputs referring to the base factor level (here: -rank1). - -The output is interpreted as follows: "Having `rank2` instead of `rank1` -while holding all other values constant results in a decrease in odds of -49.1% (1-0.509)". - - calc.oddsratio.glm(data = dat, model = fit.glm, incr = list(gre = 380, gpa = 5)) - #> predictor oddsratio CI.low (2.5 %) CI.high (97.5 %) increment - #> 1 gre 2.364 1.054 5.396 380 - #> 2 gpa 55.712 2.229 1511.282 5 - #> 3 rank2 0.509 0.272 0.945 Indicator variable - #> 4 rank3 0.262 0.132 0.512 Indicator variable - #> 5 rank4 0.212 0.091 0.471 Indicator variable - -You can also set other confident intervals for GLM(M) models. 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vignettes/function_tutorial.Rmd rename to vignettes/oddsratio.Rmd index 2039714..8409033 100644 --- a/vignettes/function_tutorial.Rmd +++ b/vignettes/oddsratio.Rmd @@ -1,5 +1,5 @@ --- -title: "Function tutorial" +title: "oddsratio tutorial" author: "Patrick Schratz" date: "`r Sys.Date()`" output: @@ -20,8 +20,8 @@ knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) -library(ggplot2) -library(cowplot) +library("ggplot2") +library("cowplot") ``` ## Load example data & fit model @@ -29,7 +29,7 @@ library(cowplot) Data source: `?mgcv::predict.gam` ```{r, results='hide'} -library(oddsratio) +library("oddsratio") fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, data = data_gam) @@ -44,7 +44,6 @@ and specify for which values we want to calculate the odds ratio. We can see that the odds of response `y` happening are 22 times higher when predictor `x2` increases from 0.099 to 0.198 while holding all other predictors constant. - ```{r} or_gam(data = data_gam, model = fit_gam, pred = "x2", values = c(0.099, 0.198)) @@ -52,7 +51,8 @@ or_gam(data = data_gam, model = fit_gam, pred = "x2", Usually, this calculation is done by setting all predictors to their mean value, predict the response, change the desired predictor to a new value and predict the response again. -These actions results in two log odds values, respectively, which are transformed into odds by exponentiating them. Finally, the odds ratio can be calculated from these two odds values. +These actions results in two log odds values, respectively, which are transformed into odds by exponentiating them. +Finally, the odds ratio can be calculated from these two odds values. ### Calculate OR for level change of indicator variable @@ -70,7 +70,6 @@ Here, the change in odds of `y` happening if predictor `x4` is changing from lev To get an impression of odds ratio behaviour throughout the complete range of the smoothing function of the fitted GAM model, you can calculate odds ratios based on percentage breaks of the predictors distribution. Here we slice predictor `x2` into 5 parts by taking the predictor values of every 20% increment step. - ```{r} or_gam(data = data_gam, model = fit_gam, pred = "x2", percentage = 20, slice = TRUE) @@ -93,10 +92,9 @@ You can further customize the look using other colors or linetypes. ### Add odds ratio information into smoothing function plot So now, we have the odds ratios and we have a plot of the smoothing function. -Why not combine both? We can do so using `insert_or()`. Its main arguments -are (i) a `ggplot` plotting object containing the smooth function and a data frame -returned from `or_gam()` containing information about the predictor and -the respective values we want to insert. +Why not combine both? +We can do so using `insert_or()`. +Its main arguments are (i) a `ggplot` plotting object containing the smooth function and a data frame returned from `or_gam()` containing information about the predictor and the respective values we want to insert. ```{r} plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") @@ -109,16 +107,15 @@ plot <- insert_or(plot_object, or_object, or_yloc = 3, plot ``` -The odds ratio information is always centered between the two vertical lines. Hence it only -looks nice if the gap between the two chosen values (here 0.099 and 0.198) is large enough. -If the smoothing line crosses your inserted text, you can just correct it adjusting `or_yloc`. This param sets the y-location of the inserted odds ratio information. +The odds ratio information is always centered between the two vertical lines. Hence it only looks nice if the gap between the two chosen values (here 0.099 and 0.198) is large enough. +If the smoothing line crosses your inserted text, you can just correct it adjusting `or_yloc`. +This param sets the y-location of the inserted odds ratio information. Depending on the digits of your chosen values (here 3), you might also need to adjust the x-axis location of the two values so that they do not interfer with the vertical line. -Let's do all this by inserting another odds ratio into this plot! This time we simply -take the already produced plot as an input to `insert_or()` and use a new odds ratio -object: +Let's do all this by inserting another odds ratio into this plot! +This time we simply take the already produced plot as an input to `insert_or()` and use a new odds ratio object: ```{r} or_object2 <- or_gam(data = data_gam, model = fit_gam, @@ -169,5 +166,3 @@ frame will automatically adjust its column names to the specified level. or_glm(data = data_glm, model = fit_glm, incr = list(gre = 380, gpa = 5), CI = 0.70) ``` - - From 657f22893337c67be166ef2930727e52c87c97b6 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 11:22:15 +0200 Subject: [PATCH 13/26] move usage example to pkgdown site --- README.Rmd | 96 +------------------------------------- README.md | 132 ++--------------------------------------------------- 2 files changed, 6 insertions(+), 222 deletions(-) diff --git a/README.Rmd b/README.Rmd index 112009e..6b3ef2b 100644 --- a/README.Rmd +++ b/README.Rmd @@ -32,99 +32,7 @@ Get the development version from Github: ```R remotes::install_github("pat-s/oddsratio") ``` -## Examples -### GLM +## Usage -Odds ratio calculation of predictors `gre` & `gpa` of a fitted model `fit_glm` -with increment steps of 380 and 5, respectively. -For factor variables (here: `rank` with 4 levels), automatically all odds ratios -corresponding to the base level (here: `rank1`) are returned including their -respective confident intervals. The default level is 95%. -However, other levels can be specified with the param `CI`. -Data source: https://stats.idre.ucla.edu/stat/data/binary.csv - -```{r} -library("oddsratio") -library("mgcv") - -df <- data_glm -df$rank <- factor(df$rank) -fit_glm <- glm(admit ~ gre + gpa + rank, data = df, family = "binomial") - -or_glm(data = df, model = fit_glm, - incr = list(gre = 380, gpa = 5, CI = 0.95)) -``` - -### GAM - -For GAMs, the calculation of odds ratio is different. -Due to its non-linear definition, odds ratios do only apply to specific -value changes and are not constant throughout the whole value range of the -predictor as for GLMs. -Hence, odds ratios of GAMs can only be computed for one predictor at a time by -holding all other predictors at a fixed value while changing the value of the -specific predictor. -Confident intervals are currently fixed to the 95% level for GAMs. -Data source: `?mgcv::predict.gam()` - -Here, the usage of `or_gam()` is shown by calculating odds ratios of -pred `x2` for a 20% steps across the whole value range of the predictor. - -```{r} -set.seed(1234) -n <- 200 -sig <- 2 -df <- gamSim(1, n = n,scale = sig, verbose = FALSE) -df$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), rep("D", 50))) -fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, data = df) - -or_gam(data = df, model = fit_gam, pred = "x2", - percentage = 20, slice = TRUE) -``` - -If you want to compute a single odds ratio for specific values, simply set -param `slice = FALSE`: - -```{r} -or_gam(data = df, model = fit_gam, - pred = "x2", values = c(0.099, 0.198)) -``` - -Plotting of GAM smooths is also supported: - -```{r} -plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") + - cowplot::theme_minimal_grid() -``` - -Insert the calculated odds ratios into the smoothing function: - -```{r} -plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") -or_object <- or_gam(data = df, model = fit_gam, - pred = "x2", values = c(0.099, 0.198)) - -plot <- insert_or(plot_object, or_object, or_yloc = 3, - values_xloc = 0.04, line_size = 0.5, - line_type = "dotdash", values_yloc = 0.5, - arrow_col = "red") -plot + - cowplot::theme_minimal_grid() -``` - - -Insert multiple odds ratios into one smooth: - -```{r} -or_object2 <- or_gam(data = df, model = fit_gam, pred = "x2", - values = c(0.4, 0.6)) - -insert_or(plot, or_object2, or_yloc = 2.1, values_yloc = 2, - line_col = "green4", text_col = "black", - rect_col = "green4", rect_alpha = 0.2, - line_alpha = 1, line_type = "dashed", - arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, - arrow_length = 0.01, rect = T) + - cowplot::theme_minimal_grid() -``` +See the [Getting Started](https://pat-s.github.io/oddsratio/articles/oddsratio.html) vignette. diff --git a/README.md b/README.md index d606d7e..f45b18c 100644 --- a/README.md +++ b/README.md @@ -27,132 +27,8 @@ Get the development version from Github: remotes::install_github("pat-s/oddsratio") ``` -## Examples +## Usage -### GLM - -Odds ratio calculation of predictors `gre` & `gpa` of a fitted model -`fit_glm` with increment steps of 380 and 5, respectively. -For factor variables (here: `rank` with 4 levels), automatically all -odds ratios corresponding to the base level (here: `rank1`) are returned -including their respective confident intervals. The default level is -95%. However, other levels can be specified with the param `CI`. Data -source: - -``` r -library("oddsratio") -library("mgcv") -``` - - ## Loading required package: nlme - - ## This is mgcv 1.8-28. For overview type 'help("mgcv-package")'. - -``` r -df <- data_glm -df$rank <- factor(df$rank) -fit_glm <- glm(admit ~ gre + gpa + rank, data = df, family = "binomial") - -or_glm(data = df, model = fit_glm, - incr = list(gre = 380, gpa = 5, CI = 0.95)) -``` - - ## predictor oddsratio CI_low (2.5 %) CI_high (97.5 %) increment - ## 1 gre 2.364 1.054 5.396 380 - ## 2 gpa 55.712 2.229 1511.282 5 - ## 3 rank2 0.509 0.272 0.945 Indicator variable - ## 4 rank3 0.262 0.132 0.512 Indicator variable - ## 5 rank4 0.212 0.091 0.471 Indicator variable - -### GAM - -For GAMs, the calculation of odds ratio is different. Due to its -non-linear definition, odds ratios do only apply to specific value -changes and are not constant throughout the whole value range of the -predictor as for GLMs. Hence, odds ratios of GAMs can only be computed -for one predictor at a time by holding all other predictors at a fixed -value while changing the value of the specific predictor. Confident -intervals are currently fixed to the 95% level for GAMs. Data source: -`?mgcv::predict.gam()` - -Here, the usage of `or_gam()` is shown by calculating odds ratios of -pred `x2` for a 20% steps across the whole value range of the predictor. - -``` r -set.seed(1234) -n <- 200 -sig <- 2 -df <- gamSim(1, n = n,scale = sig, verbose = FALSE) -df$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), rep("D", 50))) -fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, data = df) - -or_gam(data = df, model = fit_gam, pred = "x2", - percentage = 20, slice = TRUE) -``` - - ## predictor value1 value2 perc1 perc2 oddsratio CI_low (2.5%) - ## 1 x2 0.001 0.200 0 20 2510.77 1091.68 - ## 2 x2 0.200 0.400 20 40 0.03 0.03 - ## 3 x2 0.400 0.599 40 60 0.58 0.56 - ## 4 x2 0.599 0.799 60 80 0.06 0.06 - ## 5 x2 0.799 0.998 80 100 0.41 0.75 - ## CI_high (97.5%) - ## 1 5774.53 - ## 2 0.03 - ## 3 0.60 - ## 4 0.06 - ## 5 0.22 - -If you want to compute a single odds ratio for specific values, simply -set param `slice = FALSE`: - -``` r -or_gam(data = df, model = fit_gam, - pred = "x2", values = c(0.099, 0.198)) -``` - - ## predictor value1 value2 oddsratio CI_low (2.5%) CI_high (97.5%) - ## 1 x2 0.099 0.198 23.32353 23.30424 23.34283 - -Plotting of GAM smooths is also supported: - -``` r -plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") + - cowplot::theme_minimal_grid() -``` - -![](README_files/figure-gfm/unnamed-chunk-4-1.png) - -Insert the calculated odds ratios into the smoothing function: - -``` r -plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") -or_object <- or_gam(data = df, model = fit_gam, - pred = "x2", values = c(0.099, 0.198)) - -plot <- insert_or(plot_object, or_object, or_yloc = 3, - values_xloc = 0.04, line_size = 0.5, - line_type = "dotdash", values_yloc = 0.5, - arrow_col = "red") -plot + - cowplot::theme_minimal_grid() -``` - -![](README_files/figure-gfm/unnamed-chunk-5-1.png) - -Insert multiple odds ratios into one smooth: - -``` r -or_object2 <- or_gam(data = df, model = fit_gam, pred = "x2", - values = c(0.4, 0.6)) - -insert_or(plot, or_object2, or_yloc = 2.1, values_yloc = 2, - line_col = "green4", text_col = "black", - rect_col = "green4", rect_alpha = 0.2, - line_alpha = 1, line_type = "dashed", - arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, - arrow_length = 0.01, rect = T) + - cowplot::theme_minimal_grid() -``` - -![](README_files/figure-gfm/unnamed-chunk-6-1.png) +See the [Getting +Started](https://pat-s.github.io/oddsratio/articles/oddsratio.html) +vignette. From 3a038c3d816bdff8f139ad767f848f5e9c8bdf00 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:28:33 +0200 Subject: [PATCH 14/26] tidy code, use tibbles --- DESCRIPTION | 3 ++- NAMESPACE | 2 ++ R/data.R | 4 +-- R/helper_funs.R | 2 +- R/insert_or.R | 2 +- R/or_gam.R | 65 +++++++++++++++++++-------------------------- R/or_glm.R | 20 +++++++++----- man/data_gam.Rd | 2 +- man/data_glm.Rd | 2 +- man/gam_to_df.Rd | 11 ++++---- man/insert_or.Rd | 40 +++++++++++++++++----------- man/no_plot.Rd | 8 +++--- man/or_gam.Rd | 69 ++++++++++++++++++++++++------------------------ man/or_glm.Rd | 21 +++++++++------ man/plot_gam.Rd | 4 +-- 15 files changed, 134 insertions(+), 121 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f4b5370..0b8ae2d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,7 +30,8 @@ Depends: Imports: ggplot2 (>= 3.0.0), mgcv, - stats + stats, + tibble Suggests: gam, grid, diff --git a/NAMESPACE b/NAMESPACE index 87e17ae..3d594cd 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,3 +14,5 @@ importFrom(graphics,plot) importFrom(stats,coefficients) importFrom(stats,confint) importFrom(stats,predict) +importFrom(tibble,as_tibble) +importFrom(tibble,tibble) diff --git a/R/data.R b/R/data.R index ac2b177..1c12a7d 100644 --- a/R/data.R +++ b/R/data.R @@ -10,7 +10,7 @@ #' #' @docType data #' -#' @format a `data.frame` randomly created numerical and non-numerical variables +#' @format a [tibble] randomly created numerical and non-numerical variables NULL #' data_glm @@ -23,7 +23,7 @@ NULL #' @keywords datasets #' @keywords internal #' -#' @format a `data.frame` randomly created numerical and non-numerical variables +#' @format a [tibble] randomly created numerical and non-numerical variables #' #' @source Taken from \url{http://www.ats.ucla.edu/stat/r/dae/logit.htm}, direct download #' link: \url{http://www.ats.ucla.edu/stat/data/binary.csv} diff --git a/R/helper_funs.R b/R/helper_funs.R index 43d62d7..63832b5 100644 --- a/R/helper_funs.R +++ b/R/helper_funs.R @@ -84,7 +84,7 @@ gam_to_df <- function(model = NULL, pred = NULL) { # get list index of spec. predictor set_pred <- which(grepl(pred, plot_df)) - df <- data.frame( + df <- tibble( x = plot_df[[set_pred]]$x, se_upr = plot_df[[set_pred]]$fit + plot_df[[set_pred]]$se, se_lwr = plot_df[[set_pred]]$fit - plot_df[[set_pred]]$se, diff --git a/R/insert_or.R b/R/insert_or.R index 29438c7..4d0f15a 100644 --- a/R/insert_or.R +++ b/R/insert_or.R @@ -8,7 +8,7 @@ #' #' @param plot_object A `ggplot` object from [plot_gam]. #' -#' @param or_object A returned data.frame from [or_gam]. +#' @param or_object A [tibble] as returned from [or_gam]. #' #' @param values Logical. Whether to print predictor value information nearby #' the inserted vertical lines. Default to `TRUE`. diff --git a/R/or_gam.R b/R/or_gam.R index 009e591..9aa731f 100644 --- a/R/or_gam.R +++ b/R/or_gam.R @@ -2,45 +2,35 @@ #' @title Calculate odds ratios of Generalized Additive (Mixed) Models #' #' @importFrom stats coefficients predict +#' @importFrom tibble tibble #' #' @description This function calculates odds ratio(s) for specific increment -#' steps of a GAM(M)s. -#' @description Odds ratios can also be calculated for continuous percentage -#' increment steps across the whole predictor distribution using `slice = TRUE`. +#' steps of a GAM(M)s. Odds ratios can also be calculated for continuous +#' percentage increment steps across the whole predictor distribution using +#' `slice = TRUE`. #' #' @param data The data used for model fitting. -#' #' @param model A fitted GAM(M). -#' -#' @param pred Character. Predictor name for which to calculate -#' the odds ratio. -#' -#' @param values Numeric vector of length two. -#' Predictor values to estimate odds ratio from. Function is written to use the -#' first provided value as the "lower" one, i.e. calculating the odds ratio -#' 'from value1 to value2'. Only used if `slice = FALSE`. -#' -#' @param percentage Numeric. Percentage number to split the -#' predictor distribution into. -#' A value of 10 would split the predictor distribution by 10\% intervals. -#' Only needed if `slice = TRUE`. -#' -#' @param slice Logical. `Default = FALSE`. Whether to calculate -#' odds ratios for fixed increment steps over the whole predictor distribution. -#' See `percentage` for setting the increment values. -#' -#' @param CI Numeric. Currently fixed to 95\% confidence interval level -#' (2.5\% - 97.5\%). -#' It should not be changed in a function call! -#' -#' @details Currently supported functions: [mgcv::gam], -#' [mgcv::gamm], [gam::gam]. -#' -#' @details For [mgcv::gamm], the `model` input of -#' [or_gam] needs to be the `gam` output (e.g. `fit_gam$gam`). -#' -#' @return A data frame with (up to) eight columns. `perc1` and `perc2` -#' are only returned if `slice = TRUE`: +#' @param pred Character. Predictor name for which to calculate the odds ratio. +#' @param values Numeric vector of length two. Predictor values to estimate odds +#' ratio from. Function is written to use the first provided value as the +#' "lower" one, i.e. calculating the odds ratio 'from value1 to value2'. Only +#' used if `slice = FALSE`. +#' @param percentage Numeric. Percentage number to split the predictor +#' distribution into. A value of 10 would split the predictor distribution by +#' 10\% intervals. Only needed if `slice = TRUE`. +#' @param slice Logical. `Default = FALSE`. Whether to calculate odds ratios for +#' fixed increment steps over the whole predictor distribution. See +#' `percentage` for setting the increment values. +#' @param CI Numeric. Currently fixed to 95\% confidence interval level (2.5\% - +#' 97.5\%). It should not be changed in a function call! +#' +#' @details Currently supported functions: [mgcv::gam], [mgcv::gamm], +#' [gam::gam]. For [mgcv::gamm], the `model` input of [or_gam] needs to be the +#' `gam` output (e.g. `fit_gam$gam`). +#' +#' @return A data frame with (up to) eight columns. `perc1` and `perc2` are only +#' returned if `slice = TRUE`: #' \item{predictor}{Predictor name} #' \item{value1}{First value of odds ratio calculation} #' \item{value2}{Second value of odds ratio calculation} @@ -102,7 +92,7 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, range_v <- c(range_v, min(data[, pred]) + step * i) } - result <- data.frame( + result <- tibble( predictor = length(100 / percentage), value1 = numeric(length = 100 / percentage), value2 = numeric(length = 100 / percentage), @@ -203,14 +193,13 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, odds_ratio_low <- as.numeric(exp(pred_gam2_CI_low - pred_gam1_CI_low), 2) odds_ratio_high <- as.numeric(exp(pred_gam2_CI_high - pred_gam1_CI_high), 2) - result <- data.frame( + result <- tibble( predictor = pred, value1 = values[1], value2 = values[2], oddsratio = odds_ratio, CI_low = odds_ratio_high, # no mistake - CI_high = odds_ratio_low, # no mistake - stringsAsFactors = FALSE + CI_high = odds_ratio_low# no mistake ) # change col names diff --git a/R/or_glm.R b/R/or_glm.R index 5db2911..bcae107 100644 --- a/R/or_glm.R +++ b/R/or_glm.R @@ -4,6 +4,7 @@ #' @importFrom stats coefficients #' @importFrom stats confint #' @import mgcv +#' @importFrom tibble as_tibble #' #' @description This function calculates odds ratio(s) for specific #' increment steps of GLMs. @@ -83,11 +84,12 @@ or_glm <- function(data, model, incr, CI = 0.95) { odds_ratios <- list() CI_low <- list() CI_high <- list() + for (i in preds) { # CI calculation if (class(model)[1] == "glm") { - CI_list <- as.data.frame(suppressMessages(confint(model, + CI_list <- data.frame(suppressMessages(confint(model, level = CI ))) [-1, ] } @@ -126,22 +128,26 @@ or_glm <- function(data, model, incr, CI = 0.95) { } # create data frame to return - result <- data.frame( + result <- tibble( predictor = as.character(names(odds_ratios)), oddsratio = unlist(odds_ratios, use.names = FALSE), CI_low = unlist(CI_low, use.names = FALSE), CI_high = unlist(CI_high, use.names = FALSE), increment = as.character(unlist(increments, use.names = FALSE - )), - stringsAsFactors = FALSE + )) ) # set CI column names if (class(model)[1] == "glm") { - colnames(result)[3] <- paste0("CI_low (", names(CI_list) [1], ")") - colnames(result)[4] <- paste0("CI_high (", names(CI_list) [2], ")") + + # Clean variable names + col_names = stringr::str_remove_all(names(CI_list), "\\.\\.") + col_names = stringr::str_remove_all(col_names, "X") + + colnames(result)[3] <- paste0("CI_low (", col_names[1], ")") + colnames(result)[4] <- paste0("CI_high (", col_names[2], ")") } - return(result) + return(as_tibble(result)) } diff --git a/man/data_gam.Rd b/man/data_gam.Rd index 1defde5..ebffb86 100644 --- a/man/data_gam.Rd +++ b/man/data_gam.Rd @@ -4,7 +4,7 @@ \name{data_gam} \alias{data_gam} \title{data_gam} -\format{a \code{data.frame} randomly created numerical and non-numerical variables} +\format{a \link{tibble} randomly created numerical and non-numerical variables} \source{ Taken from ?mgcv::gam and added variable "x4" } diff --git a/man/data_glm.Rd b/man/data_glm.Rd index 91c0f1b..f2a7566 100644 --- a/man/data_glm.Rd +++ b/man/data_glm.Rd @@ -4,7 +4,7 @@ \name{data_glm} \alias{data_glm} \title{data_glm} -\format{a \code{data.frame} randomly created numerical and non-numerical variables} +\format{a \link{tibble} randomly created numerical and non-numerical variables} \source{ Taken from \url{http://www.ats.ucla.edu/stat/r/dae/logit.htm}, direct download link: \url{http://www.ats.ucla.edu/stat/data/binary.csv} diff --git a/man/gam_to_df.Rd b/man/gam_to_df.Rd index eb00f95..90d67e7 100644 --- a/man/gam_to_df.Rd +++ b/man/gam_to_df.Rd @@ -9,8 +9,7 @@ gam_to_df(model = NULL, pred = NULL) \arguments{ \item{model}{A fitted GAM(M).} -\item{pred}{Character. Predictor name for which to calculate -the odds ratio.} +\item{pred}{Character. Predictor name for which to calculate the odds ratio.} } \description{ This function converts a fitted GAM model into a tidy data frame @@ -27,10 +26,12 @@ library(mgcv) n <- 200 sig <- 2 dat <- gamSim(1, n = n, scale = sig, verbose = FALSE) -dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), - rep("D", 50))) +dat$x4 <- as.factor(c( + rep("A", 50), rep("B", 50), rep("C", 50), + rep("D", 50) +)) fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + - offset(x3) + x4, data = dat) # fit model + offset(x3) + x4, data = dat) # fit model tmp <- gam_to_df(fit_gam, "x2") } diff --git a/man/insert_or.Rd b/man/insert_or.Rd index f2be110..51f0c7a 100644 --- a/man/insert_or.Rd +++ b/man/insert_or.Rd @@ -15,7 +15,7 @@ insert_or(plot_object = NULL, or_object = NULL, line_col = "red", \arguments{ \item{plot_object}{A \code{ggplot} object from \link{plot_gam}.} -\item{or_object}{A returned data.frame from \link{or_gam}.} +\item{or_object}{A \link{tibble} as returned from \link{or_gam}.} \item{line_col, line_alpha, line_type, line_size}{Aesthetics of vertical lines.} @@ -67,31 +67,39 @@ If you want to insert multiple odds ratio you have to do it iteratively. # load data (Source: ?mgcv::gam) and fit model library(mgcv) fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + - offset(x3) + x4, data = data_gam) # fit model + offset(x3) + x4, data = data_gam) # fit model # create input objects (plot + odds ratios) library(oddsratio) plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") -or_object1 <- or_gam(data = data_gam, model = fit_gam, - pred = "x2", values = c(0.099, 0.198)) +or_object1 <- or_gam( + data = data_gam, model = fit_gam, + pred = "x2", values = c(0.099, 0.198) +) # insert first odds ratios to plot -plot_object <- insert_or(plot_object, or_object1, or_yloc = 3, - values_xloc = 0.04, line_size = 0.5, - line_type = "dotdash", text_size = 6, - values_yloc = 0.5, arrow_col = "red") +plot_object <- insert_or(plot_object, or_object1, + or_yloc = 3, + values_xloc = 0.04, line_size = 0.5, + line_type = "dotdash", text_size = 6, + values_yloc = 0.5, arrow_col = "red" +) # calculate second odds ratio -or_object2 <- or_gam(data = data_gam, model = fit_gam, pred = "x2", - values = c(0.4, 0.6)) +or_object2 <- or_gam( + data = data_gam, model = fit_gam, pred = "x2", + values = c(0.4, 0.6) +) # add or_object2 into plot -insert_or(plot_object, or_object2, or_yloc = 2.1, values_yloc = 2, - line_col = "green4", text_col = "black", - rect_col = "green4", rect_alpha = 0.2, - line_alpha = 1, line_type = "dashed", - arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, - arrow_length = 0.01, rect = TRUE) +insert_or(plot_object, or_object2, + or_yloc = 2.1, values_yloc = 2, + line_col = "green4", text_col = "black", + rect_col = "green4", rect_alpha = 0.2, + line_alpha = 1, line_type = "dashed", + arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, + arrow_length = 0.01, rect = TRUE +) } \seealso{ \link{plot_gam} diff --git a/man/no_plot.Rd b/man/no_plot.Rd index 572e783..d2a10cf 100644 --- a/man/no_plot.Rd +++ b/man/no_plot.Rd @@ -23,10 +23,12 @@ library(mgcv) n <- 200 sig <- 2 dat <- gamSim(1, n = n, scale = sig, verbose = FALSE) -dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), - rep("D", 50))) +dat$x4 <- as.factor(c( + rep("A", 50), rep("B", 50), rep("C", 50), + rep("D", 50) +)) fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + - offset(x3) + x4, data = dat) # fit model + offset(x3) + x4, data = dat) # fit model tmp <- plot(fit_gam, pages = 1) # plot output tmp <- no_plot(fit_gam) # no plot output diff --git a/man/or_gam.Rd b/man/or_gam.Rd index 183291b..745a43d 100644 --- a/man/or_gam.Rd +++ b/man/or_gam.Rd @@ -12,30 +12,27 @@ or_gam(data = NULL, model = NULL, pred = NULL, values = NULL, \item{model}{A fitted GAM(M).} -\item{pred}{Character. Predictor name for which to calculate -the odds ratio.} +\item{pred}{Character. Predictor name for which to calculate the odds ratio.} -\item{values}{Numeric vector of length two. -Predictor values to estimate odds ratio from. Function is written to use the -first provided value as the "lower" one, i.e. calculating the odds ratio -'from value1 to value2'. Only used if \code{slice = FALSE}.} +\item{values}{Numeric vector of length two. Predictor values to estimate odds +ratio from. Function is written to use the first provided value as the +"lower" one, i.e. calculating the odds ratio 'from value1 to value2'. Only +used if \code{slice = FALSE}.} -\item{percentage}{Numeric. Percentage number to split the -predictor distribution into. -A value of 10 would split the predictor distribution by 10\% intervals. -Only needed if \code{slice = TRUE}.} +\item{percentage}{Numeric. Percentage number to split the predictor +distribution into. A value of 10 would split the predictor distribution by +10\% intervals. Only needed if \code{slice = TRUE}.} -\item{slice}{Logical. \code{Default = FALSE}. Whether to calculate -odds ratios for fixed increment steps over the whole predictor distribution. -See \code{percentage} for setting the increment values.} +\item{slice}{Logical. \code{Default = FALSE}. Whether to calculate odds ratios for +fixed increment steps over the whole predictor distribution. See +\code{percentage} for setting the increment values.} -\item{CI}{Numeric. Currently fixed to 95\% confidence interval level -(2.5\% - 97.5\%). -It should not be changed in a function call!} +\item{CI}{Numeric. Currently fixed to 95\% confidence interval level (2.5\% - +97.5\%). It should not be changed in a function call!} } \value{ -A data frame with (up to) eight columns. \code{perc1} and \code{perc2} -are only returned if \code{slice = TRUE}: +A data frame with (up to) eight columns. \code{perc1} and \code{perc2} are only +returned if \code{slice = TRUE}: \item{predictor}{Predictor name} \item{value1}{First value of odds ratio calculation} \item{value2}{Second value of odds ratio calculation} @@ -47,37 +44,39 @@ are only returned if \code{slice = TRUE}: } \description{ This function calculates odds ratio(s) for specific increment -steps of a GAM(M)s. - -Odds ratios can also be calculated for continuous percentage -increment steps across the whole predictor distribution using \code{slice = TRUE}. +steps of a GAM(M)s. Odds ratios can also be calculated for continuous +percentage increment steps across the whole predictor distribution using +\code{slice = TRUE}. } \details{ -Currently supported functions: \link[mgcv:gam]{mgcv::gam}, -\link[mgcv:gamm]{mgcv::gamm}, \link[gam:gam]{gam::gam}. - -For \link[mgcv:gamm]{mgcv::gamm}, the \code{model} input of -\link{or_gam} needs to be the \code{gam} output (e.g. \code{fit_gam$gam}). +Currently supported functions: \link[mgcv:gam]{mgcv::gam}, \link[mgcv:gamm]{mgcv::gamm}, +\link[gam:gam]{gam::gam}. For \link[mgcv:gamm]{mgcv::gamm}, the \code{model} input of \link{or_gam} needs to be the +\code{gam} output (e.g. \code{fit_gam$gam}). } \examples{ # load data (Source: ?mgcv::gam) and fit model library(mgcv) fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + - offset(x3) + x4, data = data_gam) # fit model + offset(x3) + x4, data = data_gam) # fit model # Calculate OR for specific increment step of continuous variable -or_gam(data = data_gam, model = fit_gam, pred = "x2", - values = c(0.099, 0.198)) +or_gam( + data = data_gam, model = fit_gam, pred = "x2", + values = c(0.099, 0.198) +) ## Calculate OR for change of indicator variable -or_gam(data = data_gam, model = fit_gam, pred = "x4", - values = c("B", "D")) +or_gam( + data = data_gam, model = fit_gam, pred = "x4", + values = c("B", "D") +) ## Calculate ORs for percentage increments of predictor distribution ## (here: 20\%) -or_gam(data = data_gam, model = fit_gam, pred = "x2", - percentage = 20, slice = TRUE) - +or_gam( + data = data_gam, model = fit_gam, pred = "x2", + percentage = 20, slice = TRUE +) } \seealso{ \link{or_glm} diff --git a/man/or_glm.Rd b/man/or_glm.Rd index 9ea95e6..63b1437 100644 --- a/man/or_glm.Rd +++ b/man/or_glm.Rd @@ -40,27 +40,32 @@ Currently supported functions: \link{glm}, ## Example with glm() # load data (source: http://www.ats.ucla.edu/stat/r/dae/logit.htm) and # fit model -fit_glm <- glm(admit ~ gre + gpa + rank, data = data_glm, - family = "binomial") # fit model +fit_glm <- glm(admit ~ gre + gpa + rank, + data = data_glm, + family = "binomial" +) # fit model # Calculate OR for specific increment step of continuous variable or_glm(data = data_glm, model = fit_glm, incr = list(gre = 380, gpa = 5)) # Calculate OR and change the confidence interval level -or_glm(data = data_glm, model = fit_glm, - incr = list(gre = 380, gpa = 5), CI = .70) +or_glm( + data = data_glm, model = fit_glm, + incr = list(gre = 380, gpa = 5), CI = .70 +) ## Example with MASS:glmmPQL() # load data library(MASS) data(bacteria) -fit_glmmPQL <- glmmPQL(y ~ trt + week, random = ~1 | ID, - family = binomial, data = bacteria, - verbose = FALSE) +fit_glmmPQL <- glmmPQL(y ~ trt + week, + random = ~ 1 | ID, + family = binomial, data = bacteria, + verbose = FALSE +) # Apply function or_glm(data = bacteria, model = fit_glmmPQL, incr = list(week = 5)) - } \seealso{ \link{or_gam} diff --git a/man/plot_gam.Rd b/man/plot_gam.Rd index 4d5e081..d648b6a 100644 --- a/man/plot_gam.Rd +++ b/man/plot_gam.Rd @@ -51,11 +51,11 @@ using the \code{ggplot2} plotting system. # load data (Source: ?mgcv::gam) and fit model library(mgcv) fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, - data = data_gam) + data = data_gam +) library(oddsratio) plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") - } \seealso{ \link{plot_gam} From 799e140e09eb6ef8a2173af26e972c0cfa20be66 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:28:44 +0200 Subject: [PATCH 15/26] update vignette --- vignettes/oddsratio.Rmd | 84 +++++++++++++++++++++++------------------ 1 file changed, 48 insertions(+), 36 deletions(-) diff --git a/vignettes/oddsratio.Rmd b/vignettes/oddsratio.Rmd index 8409033..f74f34d 100644 --- a/vignettes/oddsratio.Rmd +++ b/vignettes/oddsratio.Rmd @@ -24,10 +24,12 @@ library("ggplot2") library("cowplot") ``` -## Load example data & fit model +## Load example data & fit GAM model Data source: `?mgcv::predict.gam` +First, fit a simple GAM model. + ```{r, results='hide'} library("oddsratio") @@ -37,37 +39,42 @@ fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, ## GAM example -### Calculate OR for specific increment step of continuous variable +### Calculating odds ratio for specific increment steps of a continuous variable -To calculate specific increment steps of `fit_gam`, we take predictor `x2` (randomly chosen) -and specify for which values we want to calculate the odds ratio. -We can see that the odds of response `y` happening are 22 times higher when predictor `x2` increases -from 0.099 to 0.198 while holding all other predictors constant. +In this example we take predictor `x2` (randomly chosen). +We need to define start and stop values via argument `values`. ```{r} or_gam(data = data_gam, model = fit_gam, pred = "x2", values = c(0.099, 0.198)) ``` -Usually, this calculation is done by setting all predictors to their mean value, -predict the response, change the desired predictor to a new value and predict the response again. -These actions results in two log odds values, respectively, which are transformed into odds by exponentiating them. +In the plot it turns out that the odds of response `y` happening are 22 times higher when predictor `x2` increases from 0.099 to 0.198 while holding all other predictors constant. + +What is going on behind the scenes? +Usually, this calculation is done by + +1. setting all predictors to their mean value, +1. predict the response, +1. change the desired predictor to a new value and predict the response again. +These steps result in two "log odds" values, which are transformed into "odds" afterwards. Finally, the odds ratio can be calculated from these two odds values. -### Calculate OR for level change of indicator variable +### Calculating odds ratio for a level change of a nominal variable -If the predictor is an indicator variable, i.e. consists of fixed levels, you can use the function in the same way by just putting in the respective levels you are interested in: +If the predictor is an indicator/nominal/factor variable (i.e. consists of fixed levels) you can use the function in the same way by just putting in the respective factor levels: ```{r} or_gam(data = data_gam, model = fit_gam, pred = "x4", values = c("A", "B")) ``` -Here, the change in odds of `y` happening if predictor `x4` is changing from level `A` to `B` is rather small. In detail, an increase in odds of 37.8% is reported. +Here, the change in odds of `y` happening if predictor `x4` is changing from level `A` to `B` is rather small. +In detail, an increase in odds of 37.8% is reported. -### Calculate ORs for percentage increments of predictor distribution +### Calculating odds ratio for percentage increments of a continuous predictor -To get an impression of odds ratio behaviour throughout the complete range of the smoothing function of the fitted GAM model, you can calculate odds ratios based on percentage breaks of the predictors distribution. +To get an impression of odds ratio changes throughout the complete range of the smoothing function of the fitted GAM model for a specific predictor, you can calculate odds ratios based on percentage breaks of the predictors distribution. Here we slice predictor `x2` into 5 parts by taking the predictor values of every 20% increment step. ```{r} @@ -79,22 +86,27 @@ We can see that there is a high odds ratio reported when increasing predictor `x ### Plot GAM(M) smoothing functions -Right now, the only (quick) possibility to plot the smoothing functions of a GAM(M) -was to use the base `plot()` function. The fiddly work to do the same using the `ggplot2` -plotting system is done by `plot_gam()`: +Plotting smoother functions of GAM models is not very well supported in R. +`plot_gam()` helps to simplify things: ```{r} -plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") +plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") + + cowplot::theme_cowplot() ``` -You can further customize the look using other colors or linetypes. +You can further customize the look using other colors or line types. +I highly recommend the themes from the [cowplot](https://github.com/wilkelab/cowplot) in combination with `oddsratio`. +However, you are of course free to go ahead with any other theme you prefer. ### Add odds ratio information into smoothing function plot -So now, we have the odds ratios and we have a plot of the smoothing function. +So now, we computed the odds ratios and created a plot of a GAM smoothing function. Why not combine both? -We can do so using `insert_or()`. -Its main arguments are (i) a `ggplot` plotting object containing the smooth function and a data frame returned from `or_gam()` containing information about the predictor and the respective values we want to insert. +This is what `insert_or()` is for. +Its main arguments are + +- `ggplot` plotting object containing the smooth function and +- a data frame returned from `or_gam()` containing information about the predictor and the respective values that should be inserted. ```{r} plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") @@ -104,17 +116,17 @@ or_object <- or_gam(data = data_gam, model = fit_gam, plot <- insert_or(plot_object, or_object, or_yloc = 3, values_xloc = 0.05, arrow_length = 0.02, arrow_col = "red") -plot +plot + + cowplot::theme_cowplot() ``` The odds ratio information is always centered between the two vertical lines. Hence it only looks nice if the gap between the two chosen values (here 0.099 and 0.198) is large enough. If the smoothing line crosses your inserted text, you can just correct it adjusting `or_yloc`. -This param sets the y-location of the inserted odds ratio information. +This argument sets the y-location of the inserted odds ratio information. -Depending on the digits of your chosen values (here 3), you might also need to adjust the -x-axis location of the two values so that they do not interfer with the vertical line. +Depending on the digits of your chosen values (here 3), you might also need to adjust the x-axis location of the two values so that they do not interfere with the vertical line. -Let's do all this by inserting another odds ratio into this plot! +Let's do all of this by inserting another odds ratio result into this plot! This time we simply take the already produced plot as an input to `insert_or()` and use a new odds ratio object: ```{r} @@ -126,7 +138,8 @@ insert_or(plot, or_object2, or_yloc = 2.1, values_yloc = 2, rect_col = "green4", rect_alpha = 0.2, line_alpha = 1, line_type = "dashed", arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, - arrow_length = 0.02, rect = TRUE) + arrow_length = 0.02, rect = TRUE) + + cowplot::theme_cowplot() ``` Using `rect = TRUE`, you can additionally highlight certain odds ratio intervals. @@ -146,21 +159,20 @@ fit_glm <- glm(admit ~ gre + gpa + rank, data = data_glm, family = "binomial") For GLMs, the odds ratio calculation is simpler because odds ratio changes correspond to fixed predictor increases throughout the complete value range of each predictor. Hence, function `or_glm` takes the increment steps of each predictor directly as an input in its parameter `incr`. +To avoid false predictor/value assignments, the combinations need to be given in a `list`. +Odds ratios of indicator variables are computed automatically and always refer to the base factor level. -To avoid false predictor/value assignments, the combinations need to be given in a list. - -Odds ratios of indicator variables are computed automatically and do always refer to the base factor level. - -Indicator predictor `rank` has four levels. Subsequently, we will get three odds ratio outputs referring to the base factor level (here: rank1). +The indicator predictor `rank` in this dataset has four levels. +Subsequently, we will get three odds ratio outputs referring to the base factor level (here: `rank1`). -The output is interpreted as follows: "Having `rank2` instead of `rank1` while holding all other values constant results in a decrease in odds of 49.1% (1-0.509)". +The output can be interpreted as follows: "Having `rank2` instead of `rank1` while holding all other values constant results in a decrease in odds of 49.1% (1-0.509)". ```{r} or_glm(data = data_glm, model = fit_glm, incr = list(gre = 380, gpa = 5)) ``` -You can also set other confident intervals for GLM(M) models. The resulting data -frame will automatically adjust its column names to the specified level. +You can also set other confident intervals for GLM(M) models. +The resulting `tibble` will automatically adjust the column names to the specified level. ```{r} or_glm(data = data_glm, model = fit_glm, From f1d9d47505099d0720c28034d9097e0a0c4a5d97 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:40:40 +0200 Subject: [PATCH 16/26] require stringr, internal cleaning --- DESCRIPTION | 1 + NAMESPACE | 1 + R/or_gam.R | 3 +-- R/or_glm.R | 23 +++++++++++------------ man/or_glm.Rd | 18 ++++++++---------- vignettes/oddsratio.Rmd | 6 +++--- 6 files changed, 25 insertions(+), 27 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 0b8ae2d..4c766d1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -31,6 +31,7 @@ Imports: ggplot2 (>= 3.0.0), mgcv, stats, + stringr, tibble Suggests: gam, diff --git a/NAMESPACE b/NAMESPACE index 3d594cd..3103d96 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,5 +14,6 @@ importFrom(graphics,plot) importFrom(stats,coefficients) importFrom(stats,confint) importFrom(stats,predict) +importFrom(stringr,str_remove_all) importFrom(tibble,as_tibble) importFrom(tibble,tibble) diff --git a/R/or_gam.R b/R/or_gam.R index 9aa731f..b1c9e63 100644 --- a/R/or_gam.R +++ b/R/or_gam.R @@ -100,8 +100,7 @@ or_gam <- function(data = NULL, model = NULL, pred = NULL, values = NULL, perc2 = character(length = 100 / percentage), oddsratio = numeric(length = 100 / percentage), CI_low = numeric(length = 100 / percentage), - CI_high = numeric(length = 100 / percentage), - stringsAsFactors = FALSE + CI_high = numeric(length = 100 / percentage) ) # apply OR calc for vector diff --git a/R/or_glm.R b/R/or_glm.R index bcae107..82752af 100644 --- a/R/or_glm.R +++ b/R/or_glm.R @@ -5,26 +5,25 @@ #' @importFrom stats confint #' @import mgcv #' @importFrom tibble as_tibble +#' @importFrom stringr str_remove_all #' -#' @description This function calculates odds ratio(s) for specific -#' increment steps of GLMs. +#' @description This function calculates odds ratio(s) for specific increment +#' steps of GLMs. #' #' @param data The data used for model fitting. #' @param model A fitted GLM(M). #' @param incr List. Increment values of each predictor. -#' @param CI numeric. Which confident interval to calculate. -#' Must be between 0 and 1. Default to 0.95 +#' @param CI numeric. Which confident interval to calculate. Must be between 0 +#' and 1. Default to 0.95 #' -#' @return A data frame with five columns: -#' \item{predictor}{Predictor name(s)} -#' \item{oddsratio}{Calculated odds ratio(s)} -#' \item{CI_low}{Lower confident interval of odds ratio} -#' \item{CI_high}{Higher confident interval of odds ratio} -#' \item{increment}{Increment of the predictor(s)} +#' @return A data frame with five columns: \item{predictor}{Predictor name(s)} +#' \item{oddsratio}{Calculated odds ratio(s)} \item{CI_low}{Lower confident +#' interval of odds ratio} \item{CI_high}{Higher confident interval of odds +#' ratio} \item{increment}{Increment of the predictor(s)} #' #' @details `CI_low` and `CI_high` are only calculated for GLM models because -#' [glmmPQL] does not return confident intervals due to its penalizing -#' behavior. +#' [glmmPQL] does not return confident intervals due to its penalizing +#' behavior. #' #' @author Patrick Schratz #' diff --git a/man/or_glm.Rd b/man/or_glm.Rd index 63b1437..837f1a3 100644 --- a/man/or_glm.Rd +++ b/man/or_glm.Rd @@ -13,20 +13,18 @@ or_glm(data, model, incr, CI = 0.95) \item{incr}{List. Increment values of each predictor.} -\item{CI}{numeric. Which confident interval to calculate. -Must be between 0 and 1. Default to 0.95} +\item{CI}{numeric. Which confident interval to calculate. Must be between 0 +and 1. Default to 0.95} } \value{ -A data frame with five columns: -\item{predictor}{Predictor name(s)} -\item{oddsratio}{Calculated odds ratio(s)} -\item{CI_low}{Lower confident interval of odds ratio} -\item{CI_high}{Higher confident interval of odds ratio} -\item{increment}{Increment of the predictor(s)} +A data frame with five columns: \item{predictor}{Predictor name(s)} +\item{oddsratio}{Calculated odds ratio(s)} \item{CI_low}{Lower confident +interval of odds ratio} \item{CI_high}{Higher confident interval of odds +ratio} \item{increment}{Increment of the predictor(s)} } \description{ -This function calculates odds ratio(s) for specific -increment steps of GLMs. +This function calculates odds ratio(s) for specific increment +steps of GLMs. } \details{ \code{CI_low} and \code{CI_high} are only calculated for GLM models because diff --git a/vignettes/oddsratio.Rmd b/vignettes/oddsratio.Rmd index f74f34d..dbb4397 100644 --- a/vignettes/oddsratio.Rmd +++ b/vignettes/oddsratio.Rmd @@ -91,7 +91,7 @@ Plotting smoother functions of GAM models is not very well supported in R. ```{r} plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") + - cowplot::theme_cowplot() + theme_cowplot() ``` You can further customize the look using other colors or line types. @@ -117,7 +117,7 @@ plot <- insert_or(plot_object, or_object, or_yloc = 3, values_xloc = 0.05, arrow_length = 0.02, arrow_col = "red") plot + - cowplot::theme_cowplot() + theme_cowplot() ``` The odds ratio information is always centered between the two vertical lines. Hence it only looks nice if the gap between the two chosen values (here 0.099 and 0.198) is large enough. @@ -139,7 +139,7 @@ insert_or(plot, or_object2, or_yloc = 2.1, values_yloc = 2, line_alpha = 1, line_type = "dashed", arrow_xloc_r = 0.01, arrow_xloc_l = -0.01, arrow_length = 0.02, rect = TRUE) + - cowplot::theme_cowplot() + theme_cowplot() ``` Using `rect = TRUE`, you can additionally highlight certain odds ratio intervals. From f27561e23c5d3785174060c944417502ecb1d30e Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:40:46 +0200 Subject: [PATCH 17/26] .Rproj --- oddsratio.Rproj | 1 + 1 file changed, 1 insertion(+) diff --git a/oddsratio.Rproj b/oddsratio.Rproj index cba1b6b..da26332 100644 --- a/oddsratio.Rproj +++ b/oddsratio.Rproj @@ -18,4 +18,5 @@ StripTrailingWhitespace: Yes BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source +PackageCheckArgs: --as-cran, --no-vignettes PackageRoxygenize: rd,collate,namespace From 46804a1c2b8f5a7b906ac5691a63b2d67b4d9ce8 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:41:43 +0200 Subject: [PATCH 18/26] suggest cowplot --- DESCRIPTION | 1 + 1 file changed, 1 insertion(+) diff --git a/DESCRIPTION b/DESCRIPTION index 4c766d1..6b04941 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -34,6 +34,7 @@ Imports: stringr, tibble Suggests: + cowplot, gam, grid, gtable, From 63ec16a6da5c0424cd2eb67a6cd994832b66fd70 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:43:47 +0200 Subject: [PATCH 19/26] .md README --- NEWS | 48 ------------------------------------------------ README.Rmd | 38 -------------------------------------- README.md | 32 ++++++++++++++++++-------------- 3 files changed, 18 insertions(+), 100 deletions(-) delete mode 100644 NEWS delete mode 100644 README.Rmd diff --git a/NEWS b/NEWS deleted file mode 100644 index 9090713..0000000 --- a/NEWS +++ /dev/null @@ -1,48 +0,0 @@ -oddsratio 1.0.3 (June 19 2018) --------------------------------------------------------------------------------- - - * update functions to work with ggplot2 v3.0.0 - -oddsratio 1.0.2 (December 08 2017) --------------------------------------------------------------------------------- - -Minor: - * Add CITATION file - -oddsratio 1.0.0 (June 12 2017) --------------------------------------------------------------------------------- - -Major: - * rename functions (snake_case) - -oddsratio 0.3.1 (Nov 9 2016) --------------------------------------------------------------------------------- - - * update functions to work with ggplot2 v2.2.0 - * add data and enable lazy loading in examples - -oddsratio 0.3.0 (Oct 27 2016) --------------------------------------------------------------------------------- - -# New functions: --------------------------------------------------------------------------------- - * `plot_smooth.gam()`: Lets you plot smoothing functions of GAM(M)s using `ggplot2`. - * `add.oddsratio.into.plot()`: Add odds ratios into plot of GAM(M) smoothing function. - -# Function updates: --------------------------------------------------------------------------------- - * `calc.oddsratio.glm`, `calc.oddsratio.gam`: Add odds ratio confident interval calculation - * For GLM models CI level can be specified manually. - * Print 'CI' warning if model is of type `glmmPQL` - -oddsratio 0.2.0 (Oct 12 2016) --------------------------------------------------------------------------------- - - * Remove param `quietly` - * return data.frame in any case - * update DESCRIPTION - -oddsratio 0.1.0 (Oct 11 2016) --------------------------------------------------------------------------------- - - * Initial release attempt to CRAN diff --git a/README.Rmd b/README.Rmd deleted file mode 100644 index 6b3ef2b..0000000 --- a/README.Rmd +++ /dev/null @@ -1,38 +0,0 @@ ---- -output: github_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE, eval = TRUE) -library("ggplot2") -library("cowplot") -``` - -[![Build Status](https://travis-ci.org/pat-s/oddsratio.svg?branch=master)](https://travis-ci.org/pat-s/oddsratio) -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version-ago/oddsratio)](https://cran.r-project.org/package=oddsratio) -[![cran checks](https://cranchecks.info/badges/worst/oddsratio)](https://cran.r-project.org/web/checks/check_results_oddsratio.html) -[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/oddsratio)](https://cran.rstudio.com/web/packages/oddsratio/index.html) -[![lifecycle](https://img.shields.io/badge/lifecycle-stable-blue.svg)](https://www.tidyverse.org/lifecycle/#stable) -[![Dependencies](https://tinyverse.netlify.com/badge/oddsratio)](https://cran.r-project.org/package=oddsratio) - -Functions for calculation and plotting of odds ratios of Generalized Additive (Mixed) -Models and Generalized Linear (Mixed) Models with a binomial -response variable (i.e. logistic regression models). - -## Installation - -Install from CRAN: - -```R -install.packages("oddsratio") -``` - -Get the development version from Github: - -```R -remotes::install_github("pat-s/oddsratio") -``` - -## Usage - -See the [Getting Started](https://pat-s.github.io/oddsratio/articles/oddsratio.html) vignette. diff --git a/README.md b/README.md index f45b18c..6b3ef2b 100644 --- a/README.md +++ b/README.md @@ -1,34 +1,38 @@ +--- +output: github_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE, eval = TRUE) +library("ggplot2") +library("cowplot") +``` -[![Build -Status](https://travis-ci.org/pat-s/oddsratio.svg?branch=master)](https://travis-ci.org/pat-s/oddsratio) +[![Build Status](https://travis-ci.org/pat-s/oddsratio.svg?branch=master)](https://travis-ci.org/pat-s/oddsratio) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version-ago/oddsratio)](https://cran.r-project.org/package=oddsratio) -[![cran -checks](https://cranchecks.info/badges/worst/oddsratio)](https://cran.r-project.org/web/checks/check_results_oddsratio.html) -[![CRAN -Downloads](https://cranlogs.r-pkg.org/badges/oddsratio)](https://cran.rstudio.com/web/packages/oddsratio/index.html) +[![cran checks](https://cranchecks.info/badges/worst/oddsratio)](https://cran.r-project.org/web/checks/check_results_oddsratio.html) +[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/oddsratio)](https://cran.rstudio.com/web/packages/oddsratio/index.html) [![lifecycle](https://img.shields.io/badge/lifecycle-stable-blue.svg)](https://www.tidyverse.org/lifecycle/#stable) [![Dependencies](https://tinyverse.netlify.com/badge/oddsratio)](https://cran.r-project.org/package=oddsratio) -Functions for calculation and plotting of odds ratios of Generalized -Additive (Mixed) Models and Generalized Linear (Mixed) Models with a -binomial response variable (i.e. logistic regression models). +Functions for calculation and plotting of odds ratios of Generalized Additive (Mixed) +Models and Generalized Linear (Mixed) Models with a binomial +response variable (i.e. logistic regression models). ## Installation Install from CRAN: -``` r +```R install.packages("oddsratio") ``` Get the development version from Github: -``` r +```R remotes::install_github("pat-s/oddsratio") ``` ## Usage -See the [Getting -Started](https://pat-s.github.io/oddsratio/articles/oddsratio.html) -vignette. +See the [Getting Started](https://pat-s.github.io/oddsratio/articles/oddsratio.html) vignette. From 52d72b377f59602649d5b71bd59a068d4432818f Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:44:09 +0200 Subject: [PATCH 20/26] comso pkgdown theme --- _pkgdown.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 6ec456c..27a4f8b 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,3 +1,3 @@ template: params: - bootswatch: yeti + bootswatch: cosmo From b606b23ece7fad63feda285ac6ebdb95fea7ed03 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:54:27 +0200 Subject: [PATCH 21/26] NEWS --- NEWS.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/NEWS.md b/NEWS.md index cb58965..ef5e116 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,10 @@ +# oddsratio 2.0.0 (June 13 2019) + +* return a `tibble` instead of a `data.frame` +* clean up code base +* don't use `cowplot` ggplot theme by default +* optimize wording in vignette + # oddsratio 1.0.3 (June 19 2018) * update functions to work with ggplot2 v3.0.0 From aa85cb896fd1670aca87d29a98b1c910d27de627 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:54:36 +0200 Subject: [PATCH 22/26] cran-comments --- cran-comments.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/cran-comments.md b/cran-comments.md index 1e1e948..a5f758c 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,9 +1,8 @@ ## Test environments -* local Arch Linux installation, R 3.5.1 -* ubuntu 14.04 (on travis-ci), R-devel -* OSX 10.12, Xcode 8.3 (on travis-ci), R-devel -* win-builder (devel and release) +* local Arch Linux installation, R 3.6.0 +* ubuntu 16.04 (on travis-ci), R-devel +* win-builder, R-devel ## R CMD check results From c92f000e022754f8f55f374520e87e31c871370b Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 16:54:45 +0200 Subject: [PATCH 23/26] fix .Rproj --- oddsratio.Rproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/oddsratio.Rproj b/oddsratio.Rproj index da26332..7be91f5 100644 --- a/oddsratio.Rproj +++ b/oddsratio.Rproj @@ -18,5 +18,5 @@ StripTrailingWhitespace: Yes BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source -PackageCheckArgs: --as-cran, --no-vignettes +PackageCheckArgs: --as-cran --no-vignettes PackageRoxygenize: rd,collate,namespace From 70ca4dd24039a6330ba651ddf0bdff7728cd9334 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 17:00:34 +0200 Subject: [PATCH 24/26] clean README --- README.md | 10 ---------- README_files/figure-gfm/unnamed-chunk-4-1.png | Bin 36056 -> 0 bytes README_files/figure-gfm/unnamed-chunk-5-1.png | Bin 41343 -> 0 bytes README_files/figure-gfm/unnamed-chunk-6-1.png | Bin 46214 -> 0 bytes 4 files changed, 10 deletions(-) delete mode 100644 README_files/figure-gfm/unnamed-chunk-4-1.png delete mode 100644 README_files/figure-gfm/unnamed-chunk-5-1.png delete mode 100644 README_files/figure-gfm/unnamed-chunk-6-1.png diff --git a/README.md b/README.md index 6b3ef2b..46520ed 100644 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zw|?|>k(!YX?cZJL-WVA(BuvsV;6Hwp?5{VWfcZbX_#=BxS8dl(Mq@D?m7sj)yuvg2 HEB^lv8|b~e From 3fb52fcc438106ee4e90e13f9375b14d2dd14218 Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 17:03:00 +0200 Subject: [PATCH 25/26] add appveyor --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 46520ed..bba4f9b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ [![Build Status](https://travis-ci.org/pat-s/oddsratio.svg?branch=master)](https://travis-ci.org/pat-s/oddsratio) +[![Build status](https://ci.appveyor.com/api/projects/status/s5t0sowc6mxu4yhl/branch/master?svg=true)](https://ci.appveyor.com/project/pat-s/oddsratio/branch/master) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version-ago/oddsratio)](https://cran.r-project.org/package=oddsratio) [![cran checks](https://cranchecks.info/badges/worst/oddsratio)](https://cran.r-project.org/web/checks/check_results_oddsratio.html) [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/oddsratio)](https://cran.rstudio.com/web/packages/oddsratio/index.html) From f5fe73c74b1f64e10546c4164a236d40f9cd591c Mon Sep 17 00:00:00 2001 From: pat-s Date: Thu, 13 Jun 2019 17:19:13 +0200 Subject: [PATCH 26/26] pkgdown only on travis --- tic.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tic.R b/tic.R index 5183948..7b28586 100644 --- a/tic.R +++ b/tic.R @@ -1,3 +1,5 @@ -do_package_checks() +do_package_checks(error_on = "error") -do_pkgdown() +if (ci_has_env("BUILD_PKGDOWN")) { + do_pkgdown(orphan = TRUE) +}