r - Why am I getting NA's for sigma in this gamlss call? -


the following question asked michael barton on cross validated , rejected because deemed computer question. regardless, think question interesting , wondering if can answered here.

the original post here.

i fitting gamlss model call:

gamlss(formula = image_name + random(biological_source_name) - 1,        sigma.formula = biological_source_name - 1,        family = "nbi",        data = na.omit(data))

after 3 iterations error:

gamlss-rs iteration 1: global deviance = 3814  gamlss-rs iteration 2: global deviance = 7760 gamlss-rs iteration 3: global deviance = 7756  in digamma(y + (1/sigma)) : nans produced in digamma(1/sigma) : nans produced in digamma(y + (1/sigma)) : nans produced in digamma(1/sigma) : nans produced error in glim.fit(f = sigma.object, x = sigma.x, y = y, w = w, fv = sigma,  :    na's in working vector or weights parameter sigma

this suggests me estimated sigma of categorical predictors going 0. correct?

any suggestions on how go resolving this?

i contacted authors regarding this. issue negative binomial able model on dispersion, whereas data contains both under- , over-dispersed output variables, between different dependent variable groups. results in error sigma going 0.

the problem data underdispered. , sigma goes 0 , derivatives produced na’s. try fit double poisson dpo() in specific data set.

as recommended 1 of authors, distribution such double poisson allows fitting because standard deviation can modelled being both more or less mean. when using distribution, solved above problem me , able fit model.

gamlss(formula = metric ~ image_name + random(biological_source_name) - 1,    sigma.formula = ~ biological_source_name - 1,    family = "dpo",    data = na.omit(data))

note use of dpo in above example.


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