PCA2 {ggfacto} | R Documentation |
Principal Component Analysis
Description
A user-friendly wrapper around PCA
, made to
work better with ggfacto functions like ggpca_cor_circle
.
All variables can be selected by many different expressions, in the way of
the 'tidyverse'. No supplementary vars are to be provided here,
since they can be added afterward.
Usage
PCA2(
data,
active_vars,
wt,
col.w = NULL,
ind_name,
scale.unit = TRUE,
ind.sup = NULL,
ncp = 5,
graph = FALSE,
...
)
Arguments
data |
The data frame. |
active_vars |
<tidy-select> The names of the active variables. |
wt |
The name of the row weight variable |
col.w |
The weights of the columns, as a numeric vector of the same length than 'active_vars.' |
ind_name |
Possibly, a variable with the names of the individuals. |
scale.unit |
A boolean, if 'TRUE' (value set by default) then data are scaled to unit variance. |
ind.sup |
A vector indicating the indexes of the supplementary individuals. |
ncp |
Number of dimensions kept in the results (by default 5). |
graph |
A boolean, set to 'TRUE' to display the base graph. |
... |
Additional arguments to pass to |
Value
A 'res.pca' object, with all the data necessary to draw the PCA.
Examples
active_vars <- c("mpg", "cyl", "hp", "drat", "qsec")
res.pca <- PCA2(mtcars, tidyselect::all_of(active_vars) )