r - glmmLasso error and warning -


i trying perform variable selection in generalized linear mixed model using glmmlasso, coming error , warning, can not resolve. dataset unbalanced, participants (ptno) having more samples others; no missing data. dependent variable binary, other variables (beside id variable ptno) continous. suspect generic happening, fail see , have not found solution in documentation or on web. code, adapted glmmlasso soccer example is:

glm8 <- glmmlasso(group~ndufv2_dctabl+gper1_dctabl+ esr1_dctabl+esr2_dctabl+klf12_dctabl+sp4_dctabl+sp1_dctabl+  pgam1_dctabl+ank3_dctabl+rasgrp1_dctabl+akt1_dctabl+nudt1_dctabl+                   polg_dctabl+   adarb1_dctabl+ogg_dctabl+ pde4b_dctabl+  gsk3b_dctabl+ apoe_dctabl+  mapk6_dctabl, rnd = list(ptno=~1),       family = poisson(link = log), data = stackdata, lambda=100,      control = list(print.iter=true,start=c(1,rep(0,29)),q.start=0.7))

the error message displayed below. specficially, not believe there nas in dataset , unsure meaning of warning regarding factor variable.

iteration 1 error in grad.lasso[b.is.0] <- score.beta[b.is.0] - lambda.b * sign(score.beta[b.is.0]) : nas not allowed in subscripted assignments in addition: warning message: in ops.factor(y, mu) : ‘-’ not meaningful factors

an abbreviated dataset containing necessary variables available in r format , can downladed here. hope can guided bit how go on analysis. please let me know if there wrong dataset or cannot download it. appreciated.

just follow on @kristofersen comment above. indeed start vector messes analysis up.

if run

glm8 <- glmmlasso(group~ndufv2_dctabl+gper1_dctabl+ esr1_dctabl+esr2_dctabl+klf12_dctabl+sp4_dctabl+sp1_dctabl+  pgam1_dctabl+ank3_dctabl+rasgrp1_dctabl+akt1_dctabl+nudt1_dctabl+                   polg_dctabl+   adarb1_dctabl+ogg_dctabl+ pde4b_dctabl+  gsk3b_dctabl+ apoe_dctabl+  mapk6_dctabl,                    rnd = list(ptno=~1),                    family = binomial(),                    data = stackdata,                    lambda=100,                        control = list(print.iter=true))

then fine , dandy (i.e., converges , produces solution). have copied example poisson regression , need tweak code situation. have no idea whether output makes sense.

quick note: ran binomial distribution in code above since outcome binary. if makes sense estimate relative risks poisson may reasonable (and converges), need recode outcome 2 groups defined 1 , 2 , mess poisson regression.

in other words 

stackdata$group <- stackdata$group-1

before run analysis.


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