Package 'swaprinc'

Compare swaprinc Models

Description

The swaprinc function compares a regression model using raw variables to a model with principal components swapped in. The compswap function compares a regression model with raw variables to multiple models with principal components swapped in. Parameter lists are recycled to ensure they are the same length as the longest parameter list.

Usage

compswap(
  data,
  formula,
  engine = "stats",
  .prc_eng_list = list("stats"),
  .pca_varlist = list(c(NULL)),
  .n_pca_list = list(NULL),
  .lpca_center_list = list("none"),
  .lpca_scale_list = list("none"),
  .lpca_undo_list = list(FALSE),
  .gifi_transform_list = list("none"),
  .gifi_trans_vars_list = list(c(NULL)),
  .gifi_trans_dims_list = list(NULL),
  .no_tresp_list = list(FALSE),
  .miss_handler_list = list("none"),
  .model_options_list = list("noaddpars"),
  .prcomp_options_list = list("noaddpars"),
  .gifi_princals_options_list = list("noaddpars"),
  .gifi_trans_options_list = list("noaddpars")
)

Arguments

data	A dataframe
formula	A quoted model formula
engine	The engine for fitting the model. Options are "stats" or"lme4".
.prc_eng_list	A list of prc_eng values (see swaprinc documentation)
.pca_varlist	A list of pca_vars (see swaprinc documentation)
.n_pca_list	A list of n_pca_components (see swaprinc documentation)
.lpca_center_list	A list of lpca_center values (see swaprinc documentation)
.lpca_scale_list	A list of lpca_scale values (see swaprinc documentation)
.lpca_undo_list	A list of lpca_undo values (see swaprinc documentation)
.gifi_transform_list	A list of gifi_transform values (see swaprinc documentation)
.gifi_trans_vars_list	A list of gifi_trans_vars values (see swaprinc documentation)
.gifi_trans_dims_list	A list of gifi_trans_dims values (see swaprinc documentation)
.no_tresp_list	A list of no_tresp values (see swaprinc documentation)
.miss_handler_list	A list of miss_handler values (see swaprinc documentation)
.model_options_list	A list of model_options (see swaprinc documentation)
.prcomp_options_list	A list of prcomp_options (see swaprinc documentation)
.gifi_princals_options_list	A list of gifi_princals_options (see swaprinc documentation)
.gifi_trans_options_list	A list of gifi_trans_options (see swaprinc documentation)

Value

A list containing a list of fitted models and a comparison metrics data frame.

Examples

# Load the iris dataset
data(iris)

# Define the formula
formula <- "Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width"

# Define the pca_varlist
pca_varlist <- list(c("Sepal.Width", "Petal.Length"),
                   c("Sepal.Width", "Petal.Width"))

# Define the n_pca_list
n_pca_list <- list(2, 2)

# Set scaling values
lpca_center_list <- list("none", "none")
lpca_scale_list <- list("none", "none")
lpca_undo_list <- list(FALSE, FALSE)

# Run compswap
compswap_results <- compswap(data = iris,
                            formula = formula,
                            engine = "stats",
                            .pca_varlist = pca_varlist,
                            .n_pca_list = n_pca_list,
                            .lpca_center_list = lpca_center_list,
                            .lpca_scale_list = lpca_scale_list,
                            .lpca_undo_list = lpca_undo_list)

---

Swap in Principal Components

Description

Compare a regression model using raw variables with another model where principal components are extracted from a subset of the raw independent variables, and a user-defined number of these principal components are then used to replace the original subset of variables in the regression model.

Usage

swaprinc(
  data,
  formula,
  engine = "stats",
  prc_eng = "stats",
  pca_vars,
  n_pca_components,
  norun_raw = FALSE,
  lpca_center = "none",
  lpca_scale = "none",
  lpca_undo = FALSE,
  gifi_transform = "none",
  gifi_trans_vars,
  gifi_trans_dims,
  no_tresp = FALSE,
  miss_handler = "none",
  model_options = "noaddpars",
  prcomp_options = "noaddpars",
  gifi_princals_options = "noaddpars",
  gifi_trans_options = "noaddpars"
)

Arguments

data	A dataframe
formula	A quoted model formula
engine	The engine for fitting the model. Options are 'stats' or 'lme4'.
prc_eng	Then engine or extracting principal components. Options are 'stats', 'Gifi', and 'stats_Gifi'. The stats_Gifi engine uses tidyselect::where(is.numeric) to select the pca_vars for stats::prcomp and -tidyselect::where(is.numeric) to select the pca_vars for Gifi::princals. Read Rossiter (2021) for more on princals.
pca_vars	Variables to include in the principal component analysis. These variables will be swapped out for principal components
n_pca_components	The number of principal components to include in the model. If using a complex prc_eng (i.e., stats_Gifi) then provide a named vector (i.e., n_pca_components = c("stats" = 2, "Gifi" = 3)).
norun_raw	Include regression on raw variables if TRUE, exclude if FALSE.
lpca_center	Center data as in the Step-by-Step PCA vignette (Harvey & Hanson, 2022). Only numeric variables will be included in the centering. Parameter takes values 'all' to center raw and pca variables, 'raw' to only center variables for the raw variable model fitting, 'pca' to only center pca_vars before pca regression model fitting, and 'none' to skip lpca centering.
lpca_scale	Scale data as in the Step-by-Step PCA vignette. Only numeric variables will be included in the scaling. Parameter takes values 'all' to scale raw and pca variables, 'raw' to only scale variables for the raw variable model fitting, 'pca' to only scale pca_vars before pca regression model fitting, and 'none' to skip lpca scaling.
lpca_undo	Undo centering and scaling of pca_vars as in the Step-by-Step PCA vignette.
gifi_transform	Use Gifi optimal scaling to transform a set of variables. Parameter takes values 'none', 'all', 'raw', and 'pca'
gifi_trans_vars	A vector of variables to include in the Gifi optimal scaling transformation
gifi_trans_dims	Number of dimensions to extract in the Gifi optimal scaling transformation algorithm
no_tresp	When set to TRUE, no_tresp (No transform response) will exclude the response variable from from pre-modeling and pre-pca transformations. Specifically, setting no_tresp to TRUE will exclude the response variable from the transformation specified in lpca_center and lpca_scale.
miss_handler	Choose how swaprinc handles missing data on the input data. Default is 'none'. Use 'omit' for complete case analysis.
model_options	Pass additional arguments to statistical modeling functions (i.e., stats::lm, stats::glm, lme4::lmer, lme4::glmer) Default is 'noaddpars' (no additional parameters)
prcomp_options	Pass additional arguments to stats::prcomp for prc_eng = 'stats' and prc_eng = 'stats_Gifi' call. Default is 'noaddpars' (no additional parameters)
gifi_princals_options	Pass additional arguments to Gifi::princals for prc_eng = 'Gifi' and prc_eng = 'stats_Gifi' call. Default is 'noaddpars' (no additional parameters)
gifi_trans_options	Pass additional arguments to Gifi::princals for gifi_transform. Default is 'noaddpars' (no additional parameters)

Value

A list with fitted models

References

1. Rossiter, D. G. Nonlinear Principal Components Analysis: Multivariate Analysis with Optimal Scaling (MVAOS). (2021) https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html

2. Harvey, D. T., & Hanson, B. A. Step-by-Step PCA. (2022) https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf

Examples

data(iris)
res <- swaprinc(iris,
"Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width",
pca_vars = c("Sepal.Width", "Petal.Length", "Petal.Width"),
n_pca_components = 2)

---