parameter_pca_analysis: PCA for the parameters. These plots rely on factoextra fviz...
In pdp10/sbpiper: Data Analysis Functions for 'SBpipe' Package

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

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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)

`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)