I am currently wrestling my way through the R2MLwin manual, examples online etc to familiarize myself with the package.
I try to apply the info in the manual to my own data.
So far, I have been able to google myself out of any other issues, but I am stuck with this one.
I hope you can provide some insights.
I am trying out the following formula:
Code: Select all
Form4 <-vicAlloC3 ~ 1 + kiva + (1 + kiva |mlwinschool) + (1|mlwinclass) + (1|mlwinID)
startingvaluesM4 <- list(FP.b = model4IGLS@FP,
FP.v=model4IGLS@FP.cov,
RP.b=list(0.024,0.001,0.001,0.08,1.78),
RP.v = model4IGLS@RP.cov)
model4b <- runMLwiN(Formula = Form4, data = evaldata,
estoptions = list(EstM = 1,
startval=startingvaluesM4,
resi.store.levs = 3)
)
predLines(model4b, xname = "kiva", lev = 3,
probs = c(0.025, 0.975), legend = FALSE)
I have already tried the following things (I realize that these probably don't make sense, but as I am a novice in R, these are the trial and error things that made sense to me):Error in valid.viewport(x, y, width, height, just, gp, clip, xscale, yscale, :
invalid 'xscale' in viewport
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
3: In order(as.numeric(x)) : NAs introduced by coercion
4: In split(as.numeric(x)[ord], gg[ord]) : NAs introduced by coercion
5: In min(x) : no non-missing arguments to min; returning Inf
6: In max(x) : no non-missing arguments to max; returning -Inf
7: In min(x) : no non-missing arguments to min; returning Inf
8: In max(x) : no non-missing arguments to max; returning -Inf
1. Changing the predictor does not help (I thought the problem might be that kiva is a factor)
2. Only running the model with 2 levels instead of 3 does not help
3. If I use the example from the manual, I do get the right figure (thus: it is not something with my R version or R2MLwin version or whatever)
4. Doing this stuff at level 2 instead of level 3 does not help.
5. Googling the error does not help (it seems to be a very general error)
Please note that I use the starting values because IGLS gives me a starting value of 0 for one of the variances and a negative one for another one.
It might have something to do with that?
I would appreciate your input very much!