[R] [FORGED] Error from lme4: "Error: (p <- ncol(X)) == ncol(Y) is not TRUE"

[Rory Wilson]

In reply to Rolf Turner and Jean Adams who have been helping me:
This does appear to be an issue with NA values in the non-factor variables. In the (non-reproducible) example below, we can see that removing the NAs solves the problem. However, from what I can see to this point, there does not seem be be rhyme nor reason to why the issue is taking place. A slight modification to Rolf Turner's code (introducing some NAs) shows that, in general, NAs are not a problem for lmer (indeed, it just runs na.omit as default).
Examining which factors are affected by the removal of the NAs shows no discernible pattern - no factors disappeared, none became "1" or anything of this nature.I will be able to proceed just by performing the na.omit beforehand, but it is curious. 
Thanks for your help everyone (especially Rolf Turner and Jean Adams)!

mod1<-lmer(beta~expData+techVar$RIN+techVar$sample_storage_time+(1|techVar$p_amplification))#Error: (p <- ncol(X)) == ncol(Y) is not TRUE
mod1<-lm(beta~expData+techVar$RIN+techVar$sample_storage_time+techVar$p_amplification)#No error given.
  #trial with eliminating NAs  elimSamps<-which(is.na(beta))  length(elimSamps) #[1] 4
  #eliminate the NAs from all vectors  betaVals<-beta[-elimSamps]  expD<-expData[-elimSamps])  techRIN<-techVar$RIN[-elimSamps]  techTime<-techVar$sample_storage_time[-elimSamps]  techPlate<-factor(techVar$p_amplification[-elimSamps])

mod1<-lmer(betaVals~expD+techRIN+techTime+(1|techPlate))
summary(mod1)#Linear mixed model fit by REML ['lmerMod']#Formula: betaVals ~ expD + techRIN + techTime + (1 | techPlate)##REML criterion at convergence: -1701.7##Scaled residuals:#    Min      1Q  Median      3Q     Max#-2.3582 -0.6996 -0.1085  0.6079  4.6743##Random effects:# Groups    Name        Variance  Std.Dev.# techPlate (Intercept) 1.645e-05 0.004056# Residual              4.991e-03 0.070644#Number of obs: 709, groups:  techPlate, 29##Fixed effects:#             Estimate Std. Error t value#(Intercept) 2.159e-01  9.871e-02   2.188#expD        2.330e-03  1.498e-02   0.156#techRIN     5.096e-03  4.185e-03   1.218#techTime    1.399e-06  1.565e-05   0.089##Correlation of Fixed Effects:#         (Intr) expD   tchRIN#expD     -0.919#techRIN  -0.272 -0.103#techTime -0.206  0.063  0.021

summary(techPlate)# plate01  plate02  plate03  plate04  plate05  plate06  plate07  plate09#       1       22       34       28       31       28       32       10#plate09a  plate10  plate11  plate13  plate14  plate15  plate16  plate17#      16       17       15        4       52       55       41       33# plate18  plate19  plate20  plate21  plate22  plate23  plate24  plate25#      50       42       21       50       13       22       17        7# plate26  plate27  plate28  plate30  plate32#      25       21        5       13        4
summary(techVar$p_amplification)# plate01  plate02  plate03  plate04  plate05  plate06  plate07  plate09#       1       22       34       28       31       28       32       10#plate09a  plate10  plate11  plate13  plate14  plate15  plate16  plate17#      17       17       15        4       53       55       41       33# plate18  plate19  plate20  plate21  plate22  plate23  plate24  plate25#      50       42       21       50       13       22       17        8# plate26  plate27  plate28  plate30  plate32#      25       21        5       14        4

#Counter-example, where it functions fine#Example from Rolf Turner
set.seed(42)f <- factor(sample(1:29,713,TRUE))x <- seq(0,1,length=713)y <- rnorm(713)require(lme4)
x[sample(1:713,4,replace=F)]<-NA
fit <- lmer(y ~ x + (1|f))#No error message given
  
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