Hello,

I used the combat to reduce the batch effect in the expression data. If I run using the parametric model, the output is right. however, if I run using the non parametric, the output is NaN.

df_combat  <- ComBat(dat=datap,batch=batch,mod=mod,par.prior=TRUE,prior.plots=FALSE,mean.only=TRUE)

the output is correct.

df_combat  <- ComBat(dat=datap,batch=batch,mod=mod,par.prior=FALSE,mean.only=FALSE)

the output is NaN.

# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 


> sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=zh_CN.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=zh_CN.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=zh_CN.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] sva_3.50.0          BiocParallel_1.30.4 genefilter_1.78.0  
[4] mgcv_1.9-0          nlme_3.1-163       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.11            compiler_4.2.1         GenomeInfoDb_1.35.16  
 [4] XVector_0.36.0         zlibbioc_1.42.0        bitops_1.0-7          
 [7] tools_4.2.1            bit_4.0.5              annotate_1.74.0       
[10] RSQLite_2.3.1          memoise_2.0.1          lattice_0.21-8        
[13] png_0.1-8              rlang_1.1.1            Matrix_1.6-1          
[16] DBI_1.1.3              cli_3.6.1              parallel_4.2.1        
[19] fastmap_1.1.1          GenomeInfoDbData_1.2.9 httr_1.4.7            
[22] Biostrings_2.64.1      S4Vectors_0.36.2       vctrs_0.6.3           
[25] IRanges_2.32.0         locfit_1.5-9.8         stats4_4.2.1          
[28] bit64_4.0.5            grid_4.2.1             Biobase_2.56.0        
[31] R6_2.5.1               AnnotationDbi_1.58.0   survival_3.5-7        
[34] XML_3.99-0.14          limma_3.52.4           edgeR_3.38.4          
[37] blob_1.2.4             matrixStats_1.0.0      codetools_0.2-19      
[40] splines_4.2.1          BiocGenerics_0.44.0    KEGGREST_1.36.3       
[43] xtable_1.8-4           RCurl_1.98-1.12        cachem_1.0.8          
[46] crayon_1.5.2          
> q()