在R中执行插入符号调整时出错

Question

I am building a predictive model with caret/R and I am running into the following problems:

1. 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.

2. 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

Answer 1

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.