growthTrendR (version 0.2.2)

sterm_imp: variable importance of smooth terms in a GAM model

Description

Evaluates the relative influence of each smooth term in a GAM model by computing its contribution to the fitted values using the linear predictor matrix (type = "lpmatrix"). Three summary methods are available: sum of squares, variance, and mean absolute value across all observations. #'

Usage

sterm_imp(gam_model, method = c("ssq", "var", "meanabs"))

Value

A data.table with columns:

var

Name of the smooth term.

importance_pct

Relative importance as a percentage.

method

The method used for calculating the importance.

Arguments

gam_model

A GAM model object.

method

A character string specifying the method to compute importance. One of "ssq", "var", or "meanabs".

Examples

# loading processed data
dt.samples_trt <- readRDS(system.file("extdata", "dt.samples_trt.rds", package = "growthTrendR"))
# climate
dt.clim <- data.table::fread(system.file("extdata", "dt.clim.csv", package = "growthTrendR"))
# pre-data for model
dt.samples_clim <- prepare_samples_clim(dt.samples_trt, dt.clim)
dt.m <- dt.samples_clim[ageC >1]
# using gamm_spatial model as an example
m.sp <-gamm_spatial(data = dt.m, resp_scale = "resp_log",
       m.candidates = "bai_cm2 ~ log(ba_cm2_t_1) + s(ageC) + s(FFD)")

dt.m[, uid_site.fac:= as.factor(as.character(uid_site))]
dt.imp <- sterm_imp(m.sp$model$gam)