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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
<!-- badges: start -->
[](https://github.com/Aariq/holodeck/actions/workflows/R-CMD-check.yaml)
[]( https://CRAN.R-project.org/package=holodeck) 
[](https://app.codecov.io/gh/Aariq/holodeck?branch=master)
[](https://zenodo.org/badge/latestdoi/167047376)
<!-- badges: end -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# holodeck: A Tidy Interface For Simulating Multivariate Data
`holodeck` allows quick and simple creation of simulated multivariate data with variables that co-vary or discriminate between levels of a categorical variable. The resulting simulated multivariate dataframes are useful for testing the performance of multivariate statistical techniques under different scenarios, power analysis, or just doing a sanity check when trying out a new multivariate method.
## Installation
From CRAN:
``` r
install.packages("holodeck)
```
Development version from r-universe:
``` r
install.packages('holodeck', repos = c('https://aariq.r-universe.dev', 'https://cloud.r-project.org'))
```
## Load packages
`holodeck` is built to work with `dplyr` functions, including `group_by()` and the pipe (` %>% `). `purrr` is helpful for iterating simulated data. For these examples I'll use `ropls` for PCA and PLS-DA.
```{r example, message=FALSE, warning=FALSE}
library(holodeck)
library(dplyr)
library(tibble)
library(purrr)
library(ropls)
```
## Example 1: Investigating PCA and PLS-DA
Let's say we want to learn more about how principal component analysis (PCA) works. Specifically, what matters more in terms of creating a principal component---variance or covariance of variables? To this end, you might create a dataframe with a few variables with high covariance and low variance and another set of variables with low covariance and high variance
### Generate data
```{R}
set.seed(925)
df1 <-
sim_covar(n_obs = 20, n_vars = 5, cov = 0.9, var = 1, name = "high_cov") %>%
sim_covar(n_vars = 5, cov = 0.1, var = 2, name = "high_var")
```
Explore covariance structure visually. The diagonal is variance.
```{r}
df1 %>%
cov() %>%
heatmap(Rowv = NA, Colv = NA, symm = TRUE, margins = c(6,6), main = "Covariance")
```
Now let's make this dataset a little more complex. We can add a factor variable, some variables that discriminate between the levels of that factor, and add some missing values.
```{r}
set.seed(501)
df2 <-
df1 %>%
sim_cat(n_groups = 3, name = "factor") %>%
group_by(factor) %>%
sim_discr(n_vars = 5, var = 1, cov = 0, group_means = c(-1.3, 0, 1.3), name = "discr") %>%
sim_discr(n_vars = 5, var = 1, cov = 0, group_means = c(0, 0.5, 1), name = "discr2") %>%
sim_missing(prop = 0.1) %>%
ungroup()
df2
```
### PCA
```{r}
pca <- opls(select(df2, -factor), fig.pdfC = "none", info.txtC = "none")
plot(pca, parAsColFcVn = df2$factor, typeVc = "x-score")
getLoadingMN(pca) %>%
as_tibble(rownames = "variable") %>%
arrange(desc(abs(p1)))
```
It looks like PCA mostly picks up on the variables with high covariance, **not** the variables that discriminate among levels of `factor`. This makes sense, as PCA is an unsupervised analysis.
### PLS-DA
```{r}
plsda <- opls(select(df2, -factor), df2$factor, predI = 2, permI = 10, fig.pdfC = "none", info.txtC = "none")
plot(plsda, typeVc = "x-score")
getVipVn(plsda) %>%
tibble::enframe(name = "variable", value = "VIP") %>%
arrange(desc(VIP))
```
PLS-DA, a supervised analysis, finds discrimination among groups and finds that the discriminating variables we generated are most responsible for those differences.