PCA for the parameters. These plots rely on factoextra fviz functions.
1 2 3 | 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) |
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