update vis-matrix #168
Merged
update vis-matrix #168
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
The example from easystats/datawizard#44 works now. library(insight)
library(modelbased)
m1 <- lm(mpg ~ hp + factor(cyl), mtcars)
m2 <- lm(mpg ~ hp + cyl, mtcars)
m3 <- brms::brm(mpg ~ hp + factor(cyl), mtcars, refresh = 0, iter = 100, chains = 2)
predict(m1, newdata = visualisation_matrix(m1, at = "cyl"))
#> 1 2 3
#> 25.12392 19.15627 16.60307
predict(m2, newdata = visualisation_matrix(m2, at = "cyl"))
#> 1 2 3 4 5 6 7 8
#> 25.04464 24.03811 23.03158 22.02505 21.01852 20.01199 19.00546 17.99893
#> 9 10
#> 16.99240 15.98587
predict(m3, newdata = visualisation_matrix(m3, at = "cyl"))
#> Estimate Est.Error Q2.5 Q97.5
#> [1,] 25.45195 3.352775 19.823601 33.23774
#> [2,] 19.61568 3.250236 14.367664 26.16743
#> [3,] 15.68444 3.502450 8.791082 23.09514Created on 2022-01-23 by the reprex package (v2.0.1) |
|
Everything seems to work now... It actually was only necessary to fix the data-frame method. library(insight)
library(modelbased)
m1 <- lm(mpg ~ hp + factor(cyl), mtcars)
m2 <- lm(mpg ~ hp + cyl, mtcars)
m3 <- brms::brm(mpg ~ hp + factor(cyl), mtcars, refresh = 0, iter = 100, chains = 2)
predict(m1, newdata = visualisation_matrix(m1, at = "cyl"))
#> 1 2 3
#> 25.12392 19.15627 16.60307
predict(m2, newdata = visualisation_matrix(m2, at = "cyl"))
#> 1 2 3 4 5 6 7 8
#> 25.04464 24.03811 23.03158 22.02505 21.01852 20.01199 19.00546 17.99893
#> 9 10
#> 16.99240 15.98587
predict(m3, newdata = visualisation_matrix(m3, at = "cyl"))
#> Estimate Est.Error Q2.5 Q97.5
#> [1,] 24.81862 3.692832 17.698192 29.61386
#> [2,] 20.03004 3.467089 13.918569 25.82578
#> [3,] 16.58598 3.978293 9.534934 27.00101
m <- lm(mpg ~ factor(cyl) * hp, data = mtcars)
estimate_slopes(m, trend = "hp", at = "cyl")
#> Estimated Marginal Effects
#>
#> cyl | Coefficient | SE | 95% CI | t(26) | p
#> ----------------------------------------------------------
#> 4.00 | -0.11 | 0.05 | [-0.21, -0.02] | -2.47 | 0.021
#> 6.00 | -7.61e-03 | 0.05 | [-0.11, 0.10] | -0.15 | 0.882
#> 8.00 | -0.01 | 0.02 | [-0.05, 0.02] | -0.86 | 0.395
#> Marginal effects estimated for hp
mtcars$cyl <- factor(mtcars$cyl)
m <- lm(mpg ~ cyl * hp, data = mtcars)
estimate_slopes(m, trend = "hp", at = "cyl")
#> Estimated Marginal Effects
#>
#> cyl | Coefficient | SE | 95% CI | t(26) | p
#> ---------------------------------------------------------
#> 4 | -0.11 | 0.05 | [-0.21, -0.02] | -2.47 | 0.021
#> 6 | -7.61e-03 | 0.05 | [-0.11, 0.10] | -0.15 | 0.882
#> 8 | -0.01 | 0.02 | [-0.05, 0.02] | -0.86 | 0.395
#> Marginal effects estimated for hp
data(mtcars)
lm(mpg ~ as.factor(cyl) * hp, data = mtcars) |>
estimate_slopes(trend = "hp", at = "cyl")
#> Estimated Marginal Effects
#>
#> cyl | Coefficient | SE | 95% CI | t(26) | p
#> ----------------------------------------------------------
#> 4.00 | -0.11 | 0.05 | [-0.21, -0.02] | -2.47 | 0.021
#> 6.00 | -7.61e-03 | 0.05 | [-0.11, 0.10] | -0.15 | 0.882
#> 8.00 | -0.01 | 0.02 | [-0.05, 0.02] | -0.86 | 0.395
#> Marginal effects estimated for hp
lm(mpg ~ factor(cyl) * hp, data = mtcars) |>
estimate_slopes(trend = "hp", at = "cyl")
#> Estimated Marginal Effects
#>
#> cyl | Coefficient | SE | 95% CI | t(26) | p
#> ----------------------------------------------------------
#> 4.00 | -0.11 | 0.05 | [-0.21, -0.02] | -2.47 | 0.021
#> 6.00 | -7.61e-03 | 0.05 | [-0.11, 0.10] | -0.15 | 0.882
#> 8.00 | -0.01 | 0.02 | [-0.05, 0.02] | -0.86 | 0.395
#> Marginal effects estimated for hp
data(mtcars)
m <- lm(mpg ~ factor(cyl) * hp, data = mtcars)
estimate_means(m, at = "cyl")
#> NOTE: Results may be misleading due to involvement in interactions
#> Estimated Marginal Means
#>
#> cyl | Mean | SE | 95% CI
#> ------------------------------------
#> 4.00 | 19.44 | 3.07 | [13.13, 25.75]
#> 6.00 | 19.56 | 1.69 | [16.08, 23.03]
#> 8.00 | 15.99 | 1.31 | [13.30, 18.68]
#>
#> Marginal means estimated at cyl
data(mtcars)
m <- lm(mpg ~ cyl * hp, data = mtcars)
estimate_means(m, at = "cyl")
#> NOTE: Results may be misleading due to involvement in interactions
#> Estimated Marginal Means
#>
#> cyl | Mean | SE | 95% CI
#> ------------------------------------
#> 4.00 | 20.82 | 2.29 | [16.14, 25.50]
#> 4.44 | 20.27 | 2.00 | [16.19, 24.36]
#> 4.89 | 19.73 | 1.71 | [16.22, 23.24]
#> 5.33 | 19.19 | 1.45 | [16.22, 22.15]
#> 5.78 | 18.64 | 1.20 | [16.18, 21.11]
#> 6.22 | 18.10 | 1.00 | [16.04, 20.15]
#> 6.67 | 17.56 | 0.87 | [15.77, 19.34]
#> 7.11 | 17.01 | 0.85 | [15.27, 18.75]
#> 7.56 | 16.47 | 0.94 | [14.55, 18.38]
#> 8.00 | 15.92 | 1.11 | [13.65, 18.20]
#>
#> Marginal means estimated at cyl
data(mtcars)
mtcars$cyl <- factor(mtcars$cyl)
m <- lm(mpg ~ cyl * hp, data = mtcars)
estimate_means(m)
#> We selected `at = c("cyl")`.
#> NOTE: Results may be misleading due to involvement in interactions
#> Estimated Marginal Means
#>
#> cyl | Mean | SE | 95% CI
#> -----------------------------------
#> 4 | 19.44 | 3.07 | [13.13, 25.75]
#> 6 | 19.56 | 1.69 | [16.08, 23.03]
#> 8 | 15.99 | 1.31 | [13.30, 18.68]
#>
#> Marginal means estimated at cylCreated on 2022-01-23 by the reprex package (v2.0.1) |
|
named list now also works: library(modelbased)
data <- rbind(iris, iris[149, ], make.row.names = FALSE)
# Multiple variables are of interest, creating a combination
visualisation_matrix(data, at = c("Sepal.Length = c(3, 1)", "Species = 'setosa'"))
#> Visualisation Grid
#>
#> Sepal.Length | Species | Sepal.Width | Petal.Length | Petal.Width
#> -----------------------------------------------------------------
#> 3 | setosa | 3.06 | 3.77 | 1.21
#> 1 | setosa | 3.06 | 3.77 | 1.21
#>
#> Maintained constant: Sepal.Width, Petal.Length, Petal.Width
# with list-style at-argument
visualisation_matrix(data, at = list(Sepal.Length = c(3, 1), Species = "setosa"))
#> Visualisation Grid
#>
#> Sepal.Length | Species | Sepal.Width | Petal.Length | Petal.Width
#> -----------------------------------------------------------------
#> 3 | setosa | 3.06 | 3.77 | 1.21
#> 1 | setosa | 3.06 | 3.77 | 1.21
#>
#> Maintained constant: Sepal.Width, Petal.Length, Petal.WidthCreated on 2022-01-24 by the reprex package (v2.0.1) |
|
I think this PR fixes some issues and makes the API a bit more flexible, so that @IndrajeetPatil regarding easystats/datawizard#44, we could move the vis-mat functions from here to datawizard and then re-export in modelbased. Maybe you can wait a few days with the submission of datawizard until we made a decision on this. Would be great then to have this stuff already available in the CRAN version. |
|
we should add some tests for this PR, will do later. |
|
@strengejacke Yepp, will wait for your signal before I submit to CRAN. I am also waiting for your review on the PR in datawizard. Can't submit before that happens either. |
|
awesome |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Tries to address issues #161, #167 and easystats/datawizard#44