I'm getting a strange error when I run an lmer function in r.
I've tried changing the variable types (all of them are numeric or factor) and removing the NA before analysis, but nothing seems to work.
model_1 <- lmer(Q14 ~ gender * time + (1|OMID), data=data)
summary(model_1)
Specifically, my error message reads:
Error in as(value, fieldClass, strict = FALSE) :
internal problem in as(): “labelled” is(object, "numeric") is TRUE, but the metadata asserts that the 'is' relation is FALSE
Not sure why this is happening, but I can't seem to find any answers for it. Any help would be appreciated.
Thanks!
I think lmer has a problem with 'labelled' data. If you un-label the predictors it should work fine.
I had the same error: the code for the lme-formula worked perfectly fine and one day I encountered that error. The solution in my case was simply to restart the R session, reload the data - in my case from SPSS via library("haven")::read.sps and after that load library("lme4") and execute the lme-formula.
So, if the formula worked before without any error, maybe just clean the project environment and re-run the most crucial code without any additional packages loaded. Maybe it's just some "cross-contamination" between packages or an unwanted effect of any package on the dataframe.
Related
I was trying to do a QCA analysis of sufficeny in RStudio 4.1.3. using truthTable command. By running the code i get Error: wrong outcome specification. The outcome is written correctly. When I do an analysis of necessity with superSubset code everything works fine. Everything is coded numeric but it does not work with bivariate callibration either.
> ttCO21 <- truthTable( data = mydata, outcome = "CO21",
+ conditions = "GENDG1, FDI1, GDP1",
+ sort.by="incl, n", show.cases = TRUE, complete = TRUE)
**Error: Incorrect outcome specification.**
I had the same issue today and the solution was to delete any unnecessary columns (or create a new dataframe). I left only the calibrated conditions and outcome, because when I found this error, the raw data was in the same df, even if was not using it in the truthTable() command.
Because of the 3 hours i wasted on this problem i'll share my situation and solution. I had the exact same problem. creating a new df did not help, nor did removing unnecessary colums (wouldn't make sense anyway). Completely reinstalling en reloading the QCA package did work for me. Hope this helps someone.
you can try "comma" instead of "dot" (in “decimal") when you import file
I am trying to use some very simple data in rdplot but I keep getting the error: Error in seq.default(x_min, c, jump_l) : invalid '(to - from)/by' I found this response from a diffrent post on Stack Overflow but can seem to apply the fix to the rdplot function. does anyone know how can this be fixed?
The actual code I am using is:
library(rdrobust)
rdplot(y = dt$treated, x = dt$score)
Reproducibility:
Here is a sample of my data, as I've said it is fairly common data. So far I have found a couple of things:
There are multiple subsets of the observations that seem to cause problems
Changing the nbins or binselect arguments from their default will fix the problem
The first time you get an error is on the set 1:1463, however using only observations 2:1464 is OK.
The recent rdrobust version 1.0.1 should take care of this error.
I am using the MVN package, in R, to find whether each class of the dataset iris is multivariate normal or not.
I used the below code earlier in the day and generated results from it. However, I went to revisit it and now keep getting the following error message:
Error in mvn(data = iris[Species == m[1], 1:4], mvnTest = c("mardia")) :
argument "modelName" is missing, with no default
Can not figure out what this means and how to fix it !
Code:
#Mardia's Test
SM<-mvn(data=iris,subset="Species", mvnTest="mardia")
SM$multivariateNormality
SetosaPlot<-mvn(data=iris, subset="Species", multivariatePlot="qq")
You loaded the mclust package. When you did so you should have seen a warning
The following object is masked from ‘package:MVN’: mvn
So now mvn() is calling mclust::mvn() (i.e. the mvn function in the mclust package) rather than MVN::mvn().
In general you can make sure you get the version from the MVN package by using
MVN::mvn(data=iris, subset="Species", multivariatePlot="qq")
If you want to know where R is finding mvn, try find("mvn")
In general, to resolve these kinds of problems you should start a clean R session, so that you know you're starting with no packages loaded.
(By the way, no real data set is ever truly multivariate normal; you're not testing "whether it is MVN or not", but rather whether it is close enough to MVN that you can't reject the null hypothesis of multivariate normality ...)
Using the following code
trnCtrl=trainControl(method='repeatedCV', number=10, repeats=5)
I get the error
Warning message: repeats has no meaning for this resampling method.
Even stranger, it worked fine until I did a reinstall of Caret. Note that I have already removed R, and reinstalled all packages 2x...
I appreciate that this is an old question but after looking through the Caret source code all I can deduce is that the problem is occurring when declaring the sampling method.
The following snippet has been pulled directly from the Caret repo:
if(!is.na(repeats) & !(method %in% c("repeatedcv", "adaptive_cv")))
warning("`repeats` has no meaning for this resampling method.", call. = FALSE)
My guess is the condition is failing when evaluating the method. You've specified the method as repeatedCV whereas it's actually named repeatedcv.
Again I appreciate that this question is old and it could well be the name has changed since 2013. In which case, ignore this answer.
I also got the same error and the reason was I typed "repeatecv", wrong method.
When I changed to right name, "repeatedcv" it worked.
Try,
trnCtrl=trainControl(method='repeatedcv', number=10, repeats=5)
I am running a multi-level model. I use the following commands with validatedRS6 as the outcome, random as the predictor and clustno as the random effects variable.
new<-as.data.frame(read.delim("BABEX.dat", header=TRUE))
install.packages("lme4")
library(lme4)
model1<- glmer(validatedRS6 ~ random + (1|clustno), data=new, family=binomial("logit"), nAGQ = 1L)
However, I get the following error
Error in do.call(new, c(list(Class = "glmResp", family = family), ll[setdiff(names(ll), :
'what' must be a character string or a function
I have absolutely no idea what has gone wrong and have searched the internet. I am sorry but I cannot provide the data as it is from an intervention which has yet to be published.
(expanded from comment).
Congratulations, you found a bug in lme4! This is fixed now:
https://github.com/lme4/lme4/commit/9c12f002821f9567d5454e2ce3b78076dabffb54
It is caused by having a variable called new in the global environment (deep in the guts of the code, lme4 uses do.call(new,...) and finds your variable new rather than the built-in function new).
You can install a patched version from Github using devtools::install_github() (but you'll need compilation tools etc.). Alternately, there is a very simple workaround -- just call your variable anything other than new (you can't just copy it, i.e. new2 <- new -- you also have to make sure the old version is removed (rm("new"))).