object ‘group’ not found

For example, I have tried many variations of the following line to reproduce the exam data, bu nothing works.

predictor.min <- tapply(model$data[[coefficient]], model$data[[group]], min)

Changed into:
tapply(model.01$Exam[[standLRT]], model.01$Exam[[school]], min)

I have tried many variations of this, ie 1) not changing anything, removing $, or removing $Exam, keeping the words coefficients and group etc.

Could you maybe provide an example without the function part that allows one to go step by step or alternatively, provide a bit more guidance as to what information in teh script needs to be changed to modify it to other data sets? Much appreciated!!

As I’ve already told you, thanks a lot for your R sessions. Really nice!

Here is my proposition of the same function but for lmer.
(note that it assumes that the “coefficient variable” is always in second position in the formula, i.e. after the tilde ~ , and the group variable in last position… cf. objects a and b in function code!)

I hope it helps.

Enjoy,

Julie

library(mlmRev)
data(Exam)
library(lme4)

visualize.lmer <- function (model, coefficient, group,…)
{
r <- ranef(model)
f <- fixef(model)
attributes(r)[[1]] <- "r1"
r1 <- ((r)$r1)[,1]
attributes(r)[[1]] <- "r2"
r2 <- ((r)$r2)[,2]
effects <- data.frame(r1+f[1], r2+f[2])
number.lines <- nrow(effects)
a<- unlist((attributes(model)$frame[2]))
n <- ncol(attributes(model)$frame)
b<- unlist((attributes(model)$frame[n]))
predictor.min <- tapply(a,b,min)
predictor.max <- tapply(a,b,max)
outcome.min <- min(attributes(model)$eta)
outcome.max <- max(attributes(model)$eta)
plot (c(min(predictor.min),max(predictor.max)),c(outcome.min,outcome.max),
type="n",…)
for (i in 1:number.lines)
{
expression <- function(x) {effects[i,1] + (effects[i,2] * x) }
curve(expression, from=predictor.min[i], to=predictor.max[i], add=TRUE)
}
}

par(mar=c(2.3, 2.0, 2.5, 0.3), bg="white", las=2)
layout(matrix(c(1,2), nrow=1, ncol=2, byrow=T), heights=c(3,3), widths=c(3,3))

model.b <- lmer (normexam ~ standLRT + schavg + (1 + standLRT|school), data=Exam)
summary(model.b)

visualize.lmer(model.b, "standLRT", "school",xlab="Student test at school-entry", ylab="Result on Exam", main="Exam results for 65 schools")

model.01 <-lme(fixed=normexam ~standLRT + schavg, data = Exam, random=~standLRT|school)
summary(model.01)

visualize.lme(model.01, "standLRT", "school", xlab="Student test at school-entry", ylab="Result on Exam", main="Exam results for 65 schools")

I would really like to use the lmer function instead of lme.

There seems to be a problem with the expression function near the bottom. The code “curve(expression,” does not send anything to the expression function. It also crashed when I didn’t have $school after the ‘r’ in the dataframe section. And, finally, the font selected causes a problem with R because of the smart quotes.

But otherwise this is helpful.