get_eigen_DF | R Documentation |
Compute the optimal degree of freedom (df) and weighted degree of freedom (wdf) using 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 eigenSplines generated by get_eigen_spline
.
The degree of freedom (df) is obtained by averaging the optimal df across each eigenSpline.
The weighted degree of freedom (wdf) is obtained by weighting the optimal df in each eigenSpline by the percentage of variance explained by each eigenSpline, before summing the optimal dfs (variance sums to 100%).
get_eigen_DF(eigen)
eigen |
A list of eigenSpline parameters as generated by |
A list: answer$df
a vector of optimum df by CV, GCV, AIC, BIC, AICc. answer$wdf
a vector of weighted optimum df by CV, GCV, AIC, BIC, AICc.
Graphical implementation with santaR_start_GUI
Other DFsearch:
get_eigen_DFoverlay_list()
,
get_eigen_spline()
,
get_param_evolution()
,
plot_nbTP_histogram()
,
plot_param_evolution()
## 8 subjects, 8 time-points, 3 variables inputData <- acuteInflammation$data[,1:3] ind <- acuteInflammation$meta$ind time <- acuteInflammation$meta$time 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 PC4 PC5 PC6 # R2 0.8924 0.0848 0.01055 0.006084 0.0038 0.002362 # Cumulative R2 0.8924 0.9772 0.98775 0.993838 0.9976 1.000000 get_eigen_DF(eigen) # $df # CV GCV AIC BIC AICc # 3.362581 4.255487 3.031260 2.919159 2.172547 # $wdf # CV GCV AIC BIC AICc # 2.293130 2.085212 6.675608 6.671545 4.467724
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.