I am trying to run the Dredge function of the MuMIn package and keep getting an error, saying "result is empty". I don't know why and cannot find information on the meaning of this error message.
As far as my study of this function and package, the below code "should" be correct. Basically, I have a General Linear Mixed Model being run and I want to use the dredge function to run a model selection procedure based on AICc.
options(na.action = "na.fail") # Require for dredge to run
glmm1 <- lmer(cpue_diff ~ year + p.afraid + s.frequency.monitoring + (1 | f1.name ), data = dat, REML=FALSE)
summary(glmm1)
model_dredge <- dredge(glmm1, beta = FALSE, evaluate = TRUE, rank = "AICc")
options(na.action = "na.omit") # set back to default
The error message is:
"Fixed term is "(Intercept)" Error in dredge(glmm1, beta = FALSE,
evaluate = TRUE, rank = "AICc") : result is empty"
Any ideas anyone what this message means and how to correct it?
Much appreciated!
Check the the dimensions of your data frame. I had the same error message and when I checked out my data frame, there was more than a thousand "extra rows" full of NAs.
After subsetting the data frame to include only the true rows, everything worked well with the dredge function.
Related
When trying to graph the conditional fixed effects of a glmmTMB model with two random intercepts in GGally I get the error:
There was an error calling "tidy_fun()". Most likely, this is because the
function supplied in "tidy_fun=" was misspelled, does not exist, is not
compatible with your object, or was missing necessary arguments (e.g. "conf.level=" or "conf.int="). See error message below.
Error: Error in "stop_vctrs()":
! Can't recycle "..1" (size 3) to match "..2" (size 2).`
I have tinkered with figuring out the issue and it seems to be related to the two random intercepts included in the model. I have also tried extracting the coefficient and standard error information separately through broom.mixed::tidy and then feeding the data frame into GGally:ggcoef() with no avail. Any suggestions?
# Example with built-in randu data set
data(randu)
randu$A <- factor(rep(c(1,2), 200))
randu$B <- factor(rep(c(1,2,3,4), 100))
# Model
test <- glmmTMB(y ~ x + z + (0 +x|A) + (1|B), family="gaussian", data=randu)
# A few of my attempts at graphing--works fine when only one random effects term is in model
ggcoef_model(test)
ggcoef_model(test, tidy_fun = broom.mixed::tidy)
ggcoef_model(test, tidy_fun = broom.mixed::tidy, conf.int = T, intercept=F)
ggcoef_model(test, tidy_fun = broom.mixed::tidy(test, effects="fixed", component = "cond", conf.int = TRUE))
There are some (old!) bugs that have recently been fixed (here, here) that would make confidence interval reporting on RE parameters break for any model with multiple random terms (I think). I believe that if you are able to install updated versions of both glmmTMB and broom.mixed:
remotes::install_github("glmmTMB/glmmTMB/glmmTMB#ci_tweaks")
remotes::install_github("bbolker/broom.mixed")
then ggcoef_model(test) will work.
I am very new to R, and this is my first time of encountering the eval() function. So I am trying to use the med and boot.med function from the following package: mma. I am using it to conduct mediation analysis. med and boot.med take in models such as linear models, and dataframes that specify mediators and predictors and then estimate the mediation effect of each mediator.
The author of the package gives the flexible option of specifying one's own custom.function. From the source code of med, it can be seen that the custom.function is passed to the eval(). So I tried insert the gbmt function as the custom function. However, R kept giving me error message: Error during wrapup: Number of trees to be used in prediction must be provided. I have been searching online for days and tried many ways of specifying the number of trees parameter n.trees, but nothing works (I believe others have raised similar issues: post 1, post 2).
The following codes are part of the source code of the med function:
cf1 = gsub("responseY", "y[,j]", custom.function[j])
cf1 = gsub("dataset123", "x2", cf1)
cf1 = gsub("weights123", "w", cf1)
full.model[[j]] <- eval(parse(text = cf1))
One custom function example the author gives in the package documentation is as follows:
temp1<-med(data=data.bin,n=2,custom.function = 'glm(responseY~.,data=dataset123,family="quasibinomial",
weights=weights123)')
Here the glm is the custom function. This example code works and you can replicate it easily (if you have mma installed and loaded). However when I am trying to use the gbmt function on a survival object, I got errors and here is what my code looks like:
temp1 <- med(data = data.surv,n=2,type = "link",
custom.function = 'gbmt(responseY ~.,
data = dataset123,
distribution = dist,
train_params = start_stop,
cv_folds=10,
keep_gbm_data = TRUE,
)')
Anyone has any idea how the argument about number of trees n.trees can be added somewhere in the above code?
Many thanks in advance!
Update: in order to replicate the example code, please install mma and try the following:
library("mma")
data("weight_behavior") ##binary x #binary y
x=weight_behavior[,c(2,4:14)]
pred=weight_behavior[,3]
y=weight_behavior[,15]
data.bin<-data.org(x,y,pred=pred,contmed=c(7:9,11:12),binmed=c(6,10), binref=c(1,1),catmed=5,catref=1,predref="M",alpha=0.4,alpha2=0.4)
temp1<-med(data=data.bin,n=2) #or use self-defined final function
temp1<-med(data=data.bin,n=2, custom.function = 'glm(responseY~.,data=dataset123,family="quasibinomial",
weights=weights123)')
I changed the custom.function to gbmt and used a survival object as responseY and the error occurs. When I use the gbmt function on my data outside the med function, there is no error.
I'm exploring the XGBoost in R.
After training the model, I wanted to see the feature-importance data.
xgb.importance(model = bst)
The above call shows the following error. What might be wrong?
Error in xgb.model.dt.tree(feature_names = feature_names, text = model_text_dump, : feature_names has less elements than there are features used in the model
PN - I checked the following section of the xgboost lib code, but still couldn't figure out the actual issue.
# assign feature_names when available
if (!is.null(feature_names)) {
if (length(feature_names) <= max(as.numeric(td$Feature), na.rm = TRUE))
stop("feature_names has less elements than there are features used in the model")
td[isLeaf == FALSE, Feature := feature_names[as.numeric(Feature) + 1] ]
}
Ref - https://github.com/dmlc/xgboost/blob/master/R-package/R/xgb.model.dt.tree.R
I see that the nfeatures variable of the trained model is same as the number of features passed to this model.
Dose your model have feature_names function?
Perhaps try xgb.importance(feature_names=colnames(bst$feature_names), model = bst). Works for me.
I am building a predictive model with caret/R and I am running into the following problems:
When trying to execute the training/tuning, I get this error:
Error in if (tmps < .Machine$double.eps^0.5) 0 else tmpm/tmps :
missing value where TRUE/FALSE needed
After some research it appears that this error occurs when there missing values in the data, which is not the case in this example (I confirmed that the data set has no NAs). However, I also read somewhere that the missing values may be introduced during the re-sampling routine in caret, which I suspect is what's happening.
In an attempt to solve problem 1, I tried "pre-processing" the data during the re-sampling in caret by removing zero-variance and near-zero-variance predictors, and automatically inputting missing values using a carets knn automatic imputing method preProcess(c('zv','nzv','knnImpute')), , but now I get the following error:
Error: Matrices or data frames are required for preprocessing
Needless to say I checked and confirmed that the input data set are indeed matrices, so I dont understand why I get this second error.
The code follows:
x.train <- predict(dummyVars(class ~ ., data = train.transformed),train.transformed)
y.train <- as.matrix(select(train.transformed,class))
vbmp.grid <- expand.grid(estimateTheta = c(TRUE,FALSE))
adaptive_trctrl <- trainControl(method = 'adaptive_cv',
number = 10,
repeats = 3,
search = 'random',
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
allowParallel = TRUE)
fit.vbmp.01 <- train(
x = (x.train),
y = (y.train),
method = 'vbmpRadial',
trControl = adaptive_trctrl,
preProcess(c('zv','nzv','knnImpute')),
tuneGrid = vbmp.grid)
The only difference between the code for problem (1) and (2) is that in (1), the pre-processing line in the train statement is commented out.
In summary,
-There are no missing values in the data
-Both x.train and y.train are definitely matrices
-I tried using a standard 'repeatedcv' method in instead of 'adaptive_cv' in trainControl with the same exact outcome
-Forgot to mention that the outcome class has 3 levels
Anyone has any suggestions as to what may be going wrong?
As always, thanks in advance
reyemarr
I had the same problem with my data, after some digging i found that I had some Inf (infinite) values in one of the columns.
After taking them out (df <- df %>% filter(!is.infinite(variable))) the computation ran without error.
I am doing just a regular logistic regression using the caret package in R. I have a binomial response variable coded 1 or 0 that is called a SALES_FLAG and 140 numeric response variables that I used dummyVars function in R to transform to dummy variables.
data <- dummyVars(~., data = data_2, fullRank=TRUE,sep="_",levelsOnly = FALSE )
dummies<-(predict(data, data_2))
model_data<- as.data.frame(dummies)
This gives me a data frame to work with. All of the variables are numeric. Next I split into training and testing:
trainIndex <- createDataPartition(model_data$SALE_FLAG, p = .80,list = FALSE)
train <- model_data[ trainIndex,]
test <- model_data[-trainIndex,]
Time to train my model using the train function:
model <- train(SALE_FLAG~. data=train,method = "glm")
Everything runs nice and I get a model. But when I run the predict function it does not give me what I need:
predict(model, newdata =test,type="prob")
and I get an ERROR:
Error in dimnames(out)[[2]] <- modelFit$obsLevels :
length of 'dimnames' [2] not equal to array extent
On the other hand when I replace "prob" with "raw" for type inside of the predict function I get prediction but I need probabilities so I can code them into binary variable given my threshold.
Not sure why this happens. I did the same thing without using the caret package and it worked how it should:
model2 <- glm(SALE_FLAG ~ ., family = binomial(logit), data = train)
predict(model2, newdata =test, type="response")
I spend some time looking at this but not sure what is going on and it seems very weird to me. I have tried many variations of the train function meaning I didn't use the formula and used X and Y. I used method = 'bayesglm' as well to check and id gave me the same error. I hope someone can help me out. I don't need to use it since the train function to get what I need but caret package is a good package with lots of tools and I would like to be able to figure this out.
Show us str(train) and str(test). I suspect the outcome variable is numeric, which makes train think that you are doing regression. That should also be apparent from printing model. Make it a factor if you want to do classification.
Max