About warning

There are two issues here.

  - fixed-effect model matrix is rank deficient: this is not necessarily 
a problem, but you should try to understand what's going on.  There is a 
detailed StackOverflow post here:

https://stackoverflow.com/questions/37090722/lme4lmer-reports-fixed-effect-model-matrix-is-rank-deficient-do-i-need-a-fi#37094040

  You can look at your results to figure out which coefficients are 
missing, or you can use attr(getME(m,"X"), "col.dropped") to see which 
columns of the original model frame were dropped.  (It would be useful 
if I or someone would write a more detailed diagnostic function to 
report which columns of the original model matrix were collinear ...)

  - singular fit.

  This is a little harder. In some sense this means that your model is 
overfitted (there is sufficiently little data that the best estimate of 
the inter-group variance is zero).  Conclusions on the fixed effects are 
most likely OK.  *If* the singular component corresponds to the variance 
of an entire random-effect term being exactly zero (e.g. you have a 
random intercept model), then you'll get exactly the same results for 
the fixed effects, at least to the level of point estimates and Wald 
confidence intervals/p-values, if you drop the RE term.

   There is a lot more discussion in ?lme4::isSingular, for a start ...

About warning

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