sbpiper (version 1.9.0)

parameter_pca_analysis: PCA for the parameters. These plots rely on factoextra fviz functions.

Description

PCA for the parameters. These plots rely on factoextra fviz functions.

Usage

parameter_pca_analysis(model, filename, plots_dir, best_fits_percent = 50,
  label.ind = "all", select.ind = NULL, repel.ind = TRUE,
  label.var = "all", select.var = NULL, repel.var = TRUE)

Arguments

model

the model name

filename

the filename containing the fits sequence

plots_dir

the directory to save the generated plots

best_fits_percent

the percent of best fits to analyse.

label.ind

parameter `label` passed to factoextra::fviz_pca_ind(). Labels shown if <= 75 and select.ind is NULL.

select.ind

parameter `select.ind` passed to factoextra::fviz_pca_ind().

repel.ind

parameter `repel` passed to factoextra::fviz_pca_ind()

label.var

parameter `label` passed to factoextra::fviz_pca_var().

select.var

parameter `select.var` passed to factoextra::fviz_pca_var().

repel.var

parameter `repel` passed to factoextra::fviz_pca_var() dir.create(file.path("pe_datasets")) dir.create(file.path("pe_plots")) data(insulin_receptor_best_fits) write.table(insulin_receptor_best_fits, file=file.path("pe_datasets", "best_fits.csv"), row.names=FALSE) # generate the global statistics for the parameter estimation pe_ds_preproc(filename=file.path("pe_datasets", "best_fits.csv"), param.names=c('k1', 'k2', 'k3'), logspace=TRUE, all.fits=FALSE) parameter_pca_analysis(model="ir_beta", filename=file.path("pe_datasets", "best_fits_log10.csv"), plots_dir="pe_plots", best_fits_percent=50)