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Plot the evolution of different fitting parameters across all possible df for each eigenSpline

Source: R/df_search.R
plot_param_evolution.Rd

Plot the evolution of 5 different fitting metrics (CV: Cross-Validation, GCV: Generalised Cross-Validation, AIC: Akaike Information Criterion, BIC: Bayesian Information Criterion, AICc: Akaike Information Criterion Corrected for small sample size) over all possible df for each eigenSpline generated by get_param_evolution.

Usage

plot_param_evolution(paramSpace, scaled = FALSE)

Arguments

paramSpace

A list of n matrices (n being the number or eigenSplines) as generated by plot_param_evolution. Each matrix of fitting parameters has as rows different fitting metrics, as columns different df values.

scaled

(bool) If TRUE, the value of each eigenSpline fitting parameter are scaled between 0 and 1. Default is TRUE.

Value

A list of ggplot2 plotObjects, one plot per fitting parameters. All results can be plotted using do.call(grid.arrange, returnedResult)

See also

Graphical implementation with santaR_start_GUI

Other DFsearch: get_eigen_DF(), get_eigen_DFoverlay_list(), get_eigen_spline(), get_param_evolution(), plot_nbTP_histogram()

Examples

## 8 subjects, 4 time-points, 3 variables
inputData  <- acuteInflammation$data[0:32,1:3]
ind        <- acuteInflammation$meta$ind[0:32]
time       <- acuteInflammation$meta$time[0:32]
eigen      <- get_eigen_spline(inputData, ind, time, nPC=NA, scaling="scaling_UV",
                               method="nipals", verbose=TRUE, centering=TRUE, ncores=0)
#> nipals calculated PCA
#> Importance of component(s):
#>                  PC1     PC2      PC3
#> R2            0.9272 0.06606 0.006756
#> Cumulative R2 0.9272 0.99324 1.000000
#> total time: 0.01 secs
paramSpace <- get_param_evolution(eigen, step=0.25)
plotList   <- plot_param_evolution(paramSpace, scaled=TRUE)
plotList[1]
#> [[1]]

#> 
#do.call(grid.arrange, plotList )