Compute the value of different fitting metrics over all possible df for each eigenSpline
Source:R/df_search.R
get_param_evolution.Rd
Compute the value of 5 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_eigen_spline
. The resulting matrix of fitting parameter values can be plotted using plot_param_evolution
.
Arguments
- eigen
A list of eigenSpline parameters as generated by
get_eigen_spline
, containingeigen$matrix
,eigen$variance
,eigen$model
andeigen$countTP
.- step
(float) The df increment employed to cover the range of df. Default steps of 0.1
Value
A list of n matrices (n being the number or eigenSplines). Each matrix of fitting parameters has as rows different fitting metrics, as columns different df values.
See also
Graphical implementation with santaR_start_GUI
Other DFsearch:
get_eigen_DF()
,
get_eigen_DFoverlay_list()
,
get_eigen_spline()
,
plot_nbTP_histogram()
,
plot_param_evolution()
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
# 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: 0.02 secs
get_param_evolution(eigen, step=1)
#> [[1]]
#> 2 3 4
#> Penalised_residuals(CV) 103.55727 141.55548 267.197267
#> Penalised_residuals(GCV) 90.84612 122.03917 198.953021
#> AIC 185.57835 67.02707 8.000000
#> BIC 184.35094 65.18611 5.545177
#> AICc 197.57835 95464.81688 -32.000000
#>
#> [[2]]
#> 2 3 4
#> Penalised_residuals(CV) 0.2257652 6.401150e-01 1.512174
#> Penalised_residuals(GCV) 0.3034771 6.647154e-01 1.173309
#> AIC 4.6062841 6.331849e+00 8.000000
#> BIC 3.3788728 4.490887e+00 5.545177
#> AICc 16.6062865 9.540412e+04 -32.000000
#>
#> [[3]]
#> 2 3 4
#> Penalised_residuals(CV) 0.8338811 9.171538e-01 1.484069
#> Penalised_residuals(GCV) 0.6607046 7.148925e-01 1.105211
#> AIC 5.3094592 6.354912e+00 8.000000
#> BIC 4.0820479 4.513949e+00 5.545177
#> AICc 17.3094616 9.540414e+04 -32.000000
#>
# [[1]]
# 2 3 4
# Penalised_residuals(CV) 103.55727 141.55548 267.197267
# Penalised_residuals(GCV) 90.84612 122.03917 198.953021
# AIC 185.57835 67.02707 8.000000
# BIC 184.35094 65.18611 5.545177
# AICc 197.57835 95464.81688 -32.000000
#
# [[2]]
# 2 3 4
# Penalised_residuals(CV) 0.2257652 6.401150e-01 1.512174
# Penalised_residuals(GCV) 0.3034771 6.647154e-01 1.173309
# AIC 4.6062841 6.331849e+00 8.000000
# BIC 3.3788728 4.490887e+00 5.545177
# AICc 16.6062865 9.540412e+04 -32.000000
#
# [[3]]
# 2 3 4
# Penalised_residuals(CV) 0.8338811 9.171538e-01 1.484069
# Penalised_residuals(GCV) 0.6607046 7.148925e-01 1.105211
# AIC 5.3094592 6.354912e+00 8.000000
# BIC 4.0820479 4.513949e+00 5.545177
# AICc 17.3094616 9.540414e+04 -32.000000