Very useful package. I have a minor suggestion: in add_pi (and possibly other functions - I haven't checked), an error is thrown if tb does not include a column for the response variable. The actual values in the column are ignored, but the column has to be present. I'd guess this is due to some internal code similar to:
X <- formula(fit) %>% model.matrix(data = tb)
used to get the design matrix for simulation-based prediction intervals. Using a few more steps should eliminate the need to include the response. Something like:
chr_formula <- formula(fit) %>% deparse() %>% strsplit(' ') %>% getElement(1)
X <- as.formula(chr_formula[-1]) %>% model.matrix(data = tb)
I noticed this specifically with a Poisson GLM. add_ci did not require the response to be in tb.
Very useful package. I have a minor suggestion: in
add_pi(and possibly other functions - I haven't checked), an error is thrown iftbdoes not include a column for the response variable. The actual values in the column are ignored, but the column has to be present. I'd guess this is due to some internal code similar to:X <- formula(fit) %>% model.matrix(data = tb)used to get the design matrix for simulation-based prediction intervals. Using a few more steps should eliminate the need to include the response. Something like:
chr_formula <- formula(fit) %>% deparse() %>% strsplit(' ') %>% getElement(1)X <- as.formula(chr_formula[-1]) %>% model.matrix(data = tb)I noticed this specifically with a Poisson GLM.
add_cidid not require the response to be intb.