Error in matrix(NA, nrow = nfolds, ncol = numi2) : invalid 'ncol' value (too large or NA)

[seasonzhao]

Error in matrix(NA, nrow = nfolds, ncol = numi2) : invalid 'ncol' value (too large or NA)

what should I do?
Does anyone encounter this error?

[eacho111]

Error in matrix(NA, nrow = nfolds, ncol = numi2) : invalid 'ncol' value (too large or NA)

what should I do? Does anyone encounter this error?

Hello!I seem to find this issue. Maybe you have to make sure your phenotype data is conbined with c(1,0).If your phenotype data type is factor, run the next code :phenotype <- as.numeric(as.character(phenotype)) to make sure data with c(1,0).Because this package code will check the data,if both 0 and 1 are existing, code will go on.

[DylanGuo1101]

Error in matrix(NA, nrow = nfolds, ncol = numi2) : invalid 'ncol' value (too large or NA)
what should I do? Does anyone encounter this error?

Hello!I seem to find this issue. Maybe you have to make sure your phenotype data is conbined with c(1,0).If your phenotype data type is factor, run the next code :phenotype <- as.numeric(as.character(phenotype)) to make sure data with c(1,0).Because this package code will check the data,if both 0 and 1 are existing, code will go on.

It really works!

[zzj2024]

I do the transformation as the above code:phenotype <- as.numeric(as.character(phenotype)), However, it did not work with the same error:Error in matrix（NA,nrow =nfolds,ncol=numi2):

[zzj2024]

Here are my codes:

 phenotype
[1] 0 0 0 1 1 1
> names(phenotype) <- group$sample
> phenotype
 C2  C6 C10  T8  T6  T2 
  0   0   0   1   1   1 
> class(phenotype)
[1] "numeric"
> head(data2)
             C2       C6      C10       T8       T6       T2
IFI44L 3.868521 3.895496 3.602317 3.736100 4.412282 4.325066
HOMER2 3.251451 2.544758 3.218017 2.606756 2.402774 2.501470
FMO5   4.016217 3.877928 3.887904 4.181121 4.837627 4.164108
SEMA3G 3.420420 3.548644 3.634122 3.166506 3.925167 3.990411
SBSPON 3.541989 3.262744 3.714089 3.631794 3.841615 3.672471
SMIM33 3.636768 3.232989 2.939847 3.394350 3.782611 1.927010