Some predictor variables are on very different scales: consider rescaling; lmer
<p>My data</p>

<pre><code>&gt; str(dataCCLM)
'data.frame':   78 obs. of  10 variables:
 $ ID           : Factor w/ 39 levels "A1","A10","A11",..: 1 1 12 12 23 23 34     34 35 35 ...
 $ species      : Factor w/ 1 level "c": 1 1 1 1 1 1 1 1 1 1 ...
 $ d2H          : num  -91.5 -72.2 -66.3 -85.6 -57 ...
 $ tissue       : Factor w/ 2 levels "feather","liver": 2 1 2 1 2 1 2 1 2 1    ...
 $ d2HS         : num  -72.2 -72.2 -85.6 -85.6 -76.8 ...
 $ d2HL         : num  -91.5 -91.5 -66.3 -66.3 -57 -57 -72 -72 -87.8 -87.8 ...
 $ guild        : int  1 1 1 1 1 1 1 1 1 1 ...
 $ numerical.day: int  8 8 9 9 21 21 22 22 23 23 ...
 $ Elevation.m. : int  372 372 352 352 115 115 39 39 39 39 ...
 $ locality     : Factor w/ 13 levels "Bosque Cachil",..: 5 5 6 6 7 7 7 7 7 7 ...
&gt; 
</code></pre>

<p>I am attempting to create linear mixed effect models with ID as random factor</p>

<pre><code>mod.EL.OD &lt;-lmer(d2H ~ Elevation.m. + numerical.day + (1|ID), data = dataCCLM, REML=F)
summary(mod.EL.OD)
</code></pre>

<p>I keep getting this scaling error:</p>

<pre><code>&gt; mod.EL.OD &lt;-lmer(d2H ~ Elevation.m. + numerical.day + (1|ID), data = dataCCLM, REML=F)
Warning message:
Some predictor variables are on very different scales: consider rescaling 
&gt; summary(mod.EL.OD)
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: d2H ~ Elevation.m. + numerical.day + (1 | ID)
   Data: dataCCLM

     AIC      BIC   logLik deviance df.resid 
   649.6    661.4   -319.8    639.6       73 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.5083 -0.5624  0.0381  0.6384  2.9054 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept)   8.72    2.953  
 Residual             204.70   14.307  
Number of obs: 78, groups:  ID, 39

Fixed effects:
                Estimate Std. Error t value
(Intercept)   -77.950113   3.770435 -20.674
Elevation.m.   -0.001115   0.001161  -0.961
numerical.day   0.056195   0.018016   3.119

Correlation of Fixed Effects:
            (Intr) Elvt..
Elevatin.m. -0.286       
numericl.dy -0.731 -0.283
fit warnings:
Some predictor variables are on very different scales: consider rescaling
&gt; 
</code></pre>

<p>I have followed some other forum advice and used this code to scale my data:</p>

<pre><code>pvars &lt;- c( "d2H",# Scaling numeric parameters
           "Elevation.m.","numerical.day",
           "d2HL","d2HS")

datscCC &lt;- dataCCLM
datscCC[pvars] &lt;- lapply(datscCC[pvars],scale)
</code></pre>

<p>Yet I still get scaling warning...
Is this model not fittable to my data or are there other rescaling steps I can take?</p>