Hi,

I have the same problem that the one observed in this question DMRcate Error in if (nsig == 0) { : missing value where TRUE/FALSE needed, however my data has not NA values both in M-values matrix and after fitting model with limma

This is what I have done:

Error in if (nsig == 0) { : missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In logit2(assay(object, "Beta")) : NaNs produced
2: In logit2(assay(object, "Beta")) : NaNs produced
3: Partial NA coefficients for 72970 probe(s)
This is what I have done:

design2 <- model.matrix(~0+location + sex + age)
cm2 <- makeContrasts( 
          ProximalvsDistal = proximal - distal,
          ProximalvsRectum = proximal - rectum,
          DistalvsRectum = distal - rectum,
          levels=design2)

fit2 <- lmFit(MVals, design2)
fit2 <- contrasts.fit(fit2, cm2)
efit2 <- eBayes(fit2, robust = TRUE)

naind <- is.na(rowSums(efit2$p.value))
naind[which(naind=='TRUE')]
#named logical(0)

Also I have checked that my M-values matrix has no NAs

apply(is.na(MVals), 2, which)
#integer(0)

Any suggestions would be much appreciated

Thanks for reposting. I already answered on the old post, but here it is again.

You don't show how you got your MVals object, but this part:

1: In logit2(assay(object, "Beta")) : NaNs produced
2: In logit2(assay(object, "Beta")) : NaNs produced

comes from getM, and indicates that the GenomicRatioSet that you used for cpg.annotate is problematic because it has beta values outside of (0,1)

> cbind(seq(-0.1, 1.1, 0.1), logit2(seq(-0.1, 1.1, 0.1)))
      [,1]          [,2]
 [1,] -0.1           NaN
 [2,]  0.0          -Inf
 [3,]  0.1 -3.169925e+00
 [4,]  0.2 -2.000000e+00
 [5,]  0.3 -1.222392e+00
 [6,]  0.4 -5.849625e-01
 [7,]  0.5  5.551115e-16
 [8,]  0.6  5.849625e-01
 [9,]  0.7  1.222392e+00
[10,]  0.8  2.000000e+00
[11,]  0.9  3.169925e+00
[12,]  1.0           Inf
[13,]  1.1           NaN
Warning messages:
1: In logit2(seq(-0.1, 1.1, 0.1)) : NaNs produced
2: In logit2(seq(-0.1, 1.1, 0.1)) : NaNs produced

And if you have a group that is all NA or NaN, you get that error from lmFit

> z <- matrix(rnorm(1000), 100)
> z[3,1:5] <- NaN
> zz <- lmFit(z, model.matrix(~gl(2,5)))
Warning message:
Partial NA coefficients for 1 probe(s)