On 24/09/15 01:57, Rory Wilson wrote:
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
First a pedantic quibble. The foregoing call to lmer() would be better
rendered as:
mod1 <- lmer(beta ~ expData + RIN + sample_storage_time
+ (1|p_amplification), data=techVar)
I.e. Use the "data" argument (!!!) and put *spaces* in your code!
Second, can you not extract a relatively small subset of your data set
which demonstrates the problem and make that cut-down data set available?
<SNIP>
cheers,
Rolf Turner
--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
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