lmcv: LOOCV
In MLRMPA: A package for Multilinear Regression Model Population Analysis

Description Usage Arguments Details Value See Also Examples

The Leave-One-Out (LOO) and Leave-Group-Out (LGO) Cross-Validation for Multiple Linear Regression.

1	lmcv(obj, ng = NULL, random = FALSE)

`obj`	obj: the model of MLR
`ng`	ng: number of group, if missing, do LOO
`random`	random: logical, if TRUE, do random CV

The predictive ability of the developed model can be illustrated using leave-one-out (LOO) and leave-group-out (LGO) cross-validation procedures.

`q.squared`	cross-validation relation coefficient
`SDEP`	standard deviation of error of predictions

lm

u <- c(5,10,15,20,30,40,60,80,100)
lot1 <- c(118,58,42,35,27,25,23,20,18)
lot2 <- c(69,35,26,21,18,16,13,12,6)
data <- data.frame(cbind(u,lot1,lot2))
lm <- lm(u~.,data=data)
y.pre<-fitted(lm)
y<-u
Rsquared(y.pre,y)
summary(lm)$r.squared
LOOCV<-lmcv(lm)
LGOCV<-lmcv(lm,5)
print(LOOCV)
print(LGOCV)