In Nelson-Gon/manymodelr: Build and Tune Several Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

get_var_corr

As can probably(hopefully) be guessed from the name, this provides a convenient way to get variable correlations. It enables one to get correlation between one variable and all other variables in the data set.

Previously, one would set get_all to TRUE if they wanted to get correlations between all variables. This argument has been dropped in favor of simply supplying an optional other_vars vector if one does not want to get all correlations.

library(manymodelr)
# getall correlations

# default pearson

head( corrs <- get_var_corr(mtcars,comparison_var="mpg") )

Previously, one would also set drop_columns to TRUE if they wanted to drop factor columns. Now, a user simply provides a character vector specifying which column types(classes) should be dropped. It defaults to c("character","factor").

data("yields", package="manymodelr")
# purely demonstrative
get_var_corr(yields,"height",other_vars="weight",
             drop_columns=c("factor","character"),method="spearman",
             exact=FALSE)

Similarly, get_var_corr_ (note the underscore at the end) provides a convenient way to get combination-wise correlations.

head(get_var_corr_(yields),6)

To use only a subset of the data, we can use provide a list of columns to subset_cols. By default, the first value(vector) in the list is mapped to comparison_var and the other to other_Var. The list is therefore of length 2.

head(get_var_corr_(mtcars,subset_cols=list(c("mpg","vs"),c("disp","wt")),
                   method="spearman",exact=FALSE))

plot_corr

Obtaining correlations would mostly likely benefit from some form of visualization. plot_corr aims to achieve just that. There are currently two plot styles, squares and circles. circles has a shape argument that can allow for more flexibility. It should be noted that the correlation matrix supplied to this function is an object produced by get_var_corr_.

To modify the plot a bit, we can choose to switch the x and y values as shown below.

plot_corr(mtcars,show_which = "corr",
          round_which = "correlation",decimals = 2,x="other_var",  y="comparison_var",plot_style = "squares"
          ,width = 1.1,custom_cols = c("green","blue","red"),colour_by = "correlation")

To show significance of the results instead of the correlations themselves, we can set show_which to "signif" as shown below. By default, significance is set to 0.05. You can override this by supplying a different signif_cutoff.

# color by p value
# change custom colors by supplying custom_cols
# significance is default 
set.seed(233)
plot_corr(mtcars, x="other_var", y="comparison_var",plot_style = "circles",show_which = "signif", colour_by = "p.value", sample(colours(),3))