Hi all,
I found this (very) old post which had a similar problem but went without an
answer (https://stat.ethz.ch/pipermail/r-help/2004-October/058325.html)
I am trying to fit a power curve to model some observations in an nlme.
However, I know some observations to be less reliable than others (reliability
of each OBSID reflected in the WEIV in the dummy data), relatively independent
of variance, and I quantified this beforehand and wish to include it as weights
in my model. Moreover, I know a part of my variance is correlated with my
independent variable so I cannot use directly the variance as weights.
This is my model:
coeffs_start = lm(log(DEPV)~log(INDV), filter(testdummy10,DEPV!=0))$coefficients
nlme_fit <- nlme(DEPV ~ a*INDV^b,
data = testdummy10,
fixed=a+b~ 1,
random = a~ 1,
groups = ~ PARTID,
start = c(a=exp(coeffs_start[1]), b=coeffs_start[2]),
verbose = F,
method="REML",
weights=varFixed(~WEIV))
This is some sample dummy data (I know it is not a great fit but it's fake data
anyway) :
https://github.com/FlorianLeprevost/dummydata/blob/main/testdummy10.csv
This runs well without the "weights" argument, but when I add it I get this
error and I am not sure why because I believe it is the correct syntax:
Error in recalc.varFunc(object[[i]], conLin) :
dims [product 52] do not match the length of object [220]
In addition: Warning message:
In conLin$Xy * varWeights(object) :
longer object length is not a multiple of shorter object length
Thanks in advance!
Best
Florian
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