VCA (version 1.5.1)

checkData: Check Random Model for Given Dataset.

Description

This function is intended to check a variance component analysis either before or after performing it. This is particularily important for less experienced users who my not exactly know where error messages come from. External software using functions anovaVCA or remlVCA also via function fitVCA may also benefit from more user-friendly error messages.

Usage

checkData(form, Data)

Value

(list) of length equal to the number of terms in 'form' each element being a list of messages with identified, potential problems.

Arguments

form

(formula) describing the model to be analyzed

Data

(data.frame) with all variables used in 'form'

Author

Andre Schuetzenmeister

Examples

if (FALSE) {
data(dataEP05A2_1)
dat0 <- dataEP05A2_1[1:16,]
# everything should be ok
checkData(y~day/run, dat0)
# data identical response for all obs 
dat1 <- dat0
dat1$y <- dat1[1,"y"]
remlVCA(y~day/run, dat1)
checkData(y~day/run, dat1)
# now factor-levels have identical values
dat2 <- dat0
dat2$y <- dat2$y[rep(seq(1,7,2), rep(2,4))] 
checkData(y~day/run, dat2)
remlVCA(y~day/run, dat2, quiet=TRUE)
# indistinguishable terms are also problematic
dat3 <- data.frame(	y=rnorm(8,10),
						day=paste("day",rep(c(1,2),c(4,4))), 
						run=rep(c(2,1), c(4,4)))
checkData(y~day/run, dat3)
anovaVCA(y~day/run, dat3)
# no replicates, thus, no error variability
dat4 <- dat0[seq(1,15,2),]
dat4$day <- factor(dat4$day)
dat4$run <- factor(dat4$run)
checkData(y~day/run, dat4)
remlVCA(y~day/run, dat4)
}