holodeck

A Tidy Interface for Simulating Multivariate Data

https://github.com/aariq/holodeck

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A Tidy Interface for Simulating Multivariate Data

README.Rmd

---
output: github_document
---




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```{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.

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