cv.model: Cross-validation of fitted model

In filBe87/PKUss: Helper functions for species distribution modelling practicals of the Peking Unversity summer school 2019

Description

Cross-validation of fitted model

Usage

cv.model(model, strata = numeric(), K = numeric(), data = model$data)

Arguments

model	a model object (GLM or GAM)
strata	(optional) vector of the same length as the number of rows in the 'data' data.frame with integers (1:n) corresponding to n pre-defined strata to be used
K	the number of folds/strata in block cross-validation for random generation (if strata vector is not supplied) Minimum the same number as starta, maxuimum nrow(df)/10
data	data.frame for evaluation. Must contain columns with the same names as were supplied to the model fitting function

Value

vector with cross-validation predictions for each observation (to be used for model evaluation)

Author(s)

Niklaus Zimmermann, Philipp Brun

Examples

# Fit a simple model
mod1=glm(X3740 ~ poly(bio_01,2) + poly(bio_01,2) + poly(forest_fraction,2),
data=obs.sel,family="binomial")

# Use basic cv.model set-up for cross-validation predictions
cvpr1=cv.model(mod1,K=5)

# Use cv.model for block cross-validation  with few blocks
par(mfrow=c(1,2))
strt1=make_blocks(df=obs.sel[,c("bio_01","bio_03")],nstrat=3,nclusters=3)
cvpr2=cv.model(mod1,strata=strt1)
# Quick comparison
plot(cvpr1[,2],cvpr2[,2],col=strt1,main="few blocks vs random")
lines(c(-1,2),c(-1,2),col="red")

# Use cv.model for block cross-validation with many blocks
strt2=make_blocks(df=obs.sel[,c("bio_01","bio_03")],nstrat=5,nclusters=15)
cvpr3=cv.model(mod1,strata=strt2)
# Quick comparison
plot(cvpr1[,2],cvpr3[,2],col=strt2,main="many blocks vs random")
lines(c(-1,2),c(-1,2),col="red")

filBe87/PKUss documentation built on June 29, 2019, 12:12 a.m.