Error in R: multi effects models - r

Question

I'm having a few issue's I'd appreciate some help with.
head(new.data)
WSZ_Code Treatment_Code Year Month TTHM CL2_FREE BrO3 Colour PH TURB seasons
1 2 3 1996 1 30.7 0.35 0.5000750 0.75 7.4 0.055 winter
2 6 1 1996 2 24.8 0.25 0.5001375 0.75 6.9 0.200 winter
3 7 4 1996 2 60.4 0.05 0.5001375 0.75 7.1 0.055 winter
4 7 4 1996 2 58.1 0.15 0.5001570 0.75 7.5 0.055 winter
5 7 4 1996 3 62.2 0.20 0.5003881 2.00 7.6 0.055 spring
6 5 2 1996 3 40.3 0.15 0.5003500 2.00 7.7 0.055 spring
library(nlme)
> mod3 <- lme(TTHM ~ CL2_FREE, random= ~ 1| Treatment_Code/WSZ_Code, data=new.data, method ="ML")
> mod3
Linear mixed-effects model fit by maximum likelihood
Data: new.data
Log-likelihood: -1401.529
Fixed: TTHM ~ CL2_FREE
(Intercept) CL2_FREE
54.45240 -40.15033
Random effects:
Formula: ~1 | Treatment_Code
(Intercept)
StdDev: 0.004156934
Formula: ~1 | WSZ_Code %in% Treatment_Code
(Intercept) Residual
StdDev: 10.90637 13.52372
Number of Observations: 345
Number of Groups:
Treatment_Code WSZ_Code %in% Treatment_Code
4 8
> plot(augPred(mod3))
Error in plot(augPred(mod3)) :
error in evaluating the argument 'x' in selecting a method for function 'plot': Error in sprintf(gettext(fmt, domain = domain), ...) :
invalid type of argument[1]: 'symbol'
I'm not sure why I get this error. The ranef plot seems OK
plot(ranef(mod3))
But that only gives the value of the random intercepts, no TTHM predictions.
I'm looking for a way to plot the predictions like in a typical augPred which would show all the random effects for each zone. Hope that makes sense.

Answer 1

You need a groupedData object to use augPred. I hope this helps.
Best wishes #CSJCampbell
con <- textConnection("
WSZ_Code Treatment_Code Year Month TTHM CL2_FREE BrO3 Colour PH TURB seasons
2 3 1996 1 30.7 0.35 0.5000750 0.75 7.4 0.055 winter
6 1 1996 2 24.8 0.25 0.5001375 0.75 6.9 0.200 winter
7 4 1996 2 60.4 0.05 0.5001375 0.75 7.1 0.055 winter
7 4 1996 2 58.1 0.15 0.5001570 0.75 7.5 0.055 winter
7 4 1996 3 62.2 0.20 0.5003881 2.00 7.6 0.055 spring
5 2 1996 3 40.3 0.15 0.5003500 2.00 7.7 0.055 spring
")
new.data <- read.table(con, header = TRUE)
library(nlme)
new.data.grp <- groupedData(TTHM ~ CL2_FREE | Treatment_Code/WSZ_Code, data = new.data)
mod3 <- lme(TTHM ~ CL2_FREE, random= ~ 1| Treatment_Code/WSZ_Code, data=new.data.grp, method ="ML")
mod3
ap3 <- augPred(mod3)
plot(ap3)

Answer 2

I realize most are probably using ggplot2 and lme4 at this point, but I'm a bit crufty.
Here are a couple of things that I've found working with lists of response variables that are fit using lme().
So, I've been working with a number of response variables that I want to fit to a particular set of inputs. In short my code looks something like
mymodels = list()
for(resp in my_response_vars){
f = as.formula(paste(resp,paste(my_input_vars,collapse='+'),sep='~'))
mymodels[[resp]] = lme(fixed=f,random=~wave|group,method="ML",
data=mydata, na.action=na.exclude)
}
I've been successful in treating the entries in the resulting list as normal lme() objects. The problem comes when I want to plot predictions via augPred(). Specifically I get the following error,
Error in tapply(object[[nm]], groups, FUN[["numeric"]], ...) :
arguments must have same length
So, after much searching, I decided to have a look under the hood of augPred() via debug(). Here are some of the insights I came to... I'm not sure that these qualify as bugs or if they would require a patch, but I hope they can help others with similar problems.
When calling augPred() the function looks for the name of the data that was used in the original lme() call, then inherits this object from the parent.frame() via a call to eval(). I'm not sure if this defaults to the object frame or the global, but, when I change this to data = object$data in the debug, things work. So, ostensibly, if you have used a subset of these data in your model, it will call on the full set of data.
The above causes issues if one response has missing values and you are interested in one that does not. Since it includes everything in the data.frame as part of an eventual call to gsummary() the missing values in the non-response variable will throw a wrench into things.
So, missing values mess things up. I have defaulted to making a temporary data.frame with the columns of interest, then running complete.cases() on this prior to fitting the lme() model.
mymods = list()
for(resp in my_response_vars){
f = as.formula(paste(resp,paste(my_input_vars,collapse='+'),sep='~'))
v2keep = all.vars(f) # grab terms
smdat = mydata[,c(v2keep,'group')] # include group
smdat=smdat[complete.cases(smdat),] # scrub missing
tmpmod = lme(fixed=f, random=~wave|group,
method='ML', data=smdat)
mymods[[resp]] = tmpmod
# include augPred() call here
}
If you are not including a primary argument in your call to augPred() it will require that your data.frame is a groupedData() object.
So, if you are running into the arguments must have the same length error, try: subsetting your data first under a different name, make sure to clear out missing rows explicitly prior to fitting your model.