Can´t fit random effects with fit_gam?

[darrennorris]

This is a most useful package;
I have just started using the package and I am having trouble fitting a gam with random effects.
With my data, being able to include such options in the formula enables superior GAM models, e.g. deviance explained for a simple GAM = 33.5%, and deviance explained for a GAM with random effects = 48.1%.
I used the code from the fit_gam help to illustrate the issue - see below.
fit_gam fails with: Sorry, but it was not possible to fit the model
Am I specifying the formula correctly?
Many thanks for any help,
Darren

data(abies)
abies2 <- part_random(
data = abies,
pr_ab = "pr_ab",
method = c(method = "kfold", folds = 5)
)

require(mgcv)
gam_test <- gam(pr_ab ~ s(aet) + s(landform, bs = "re"), data = abies2)
summary(gam_test)
gam_t2 <- fit_gam(
data = abies2,
response = "pr_ab",
predictors = c("aet"),
predictors_f = c("landform"),
select_pred = FALSE,
partition = ".part",
thr = "max_sens_spec",
fit_formula = stats::formula(pr_ab ~ s(aet) + s(landform, bs = "re"))
)

[sjevelazco]

Hi Darren
Thank you for reporting this problem. The issues seem to be related to predictors_f argument. If you declare landform as a continuous variable, it works.

gam_t2 <- fit_gam(
data = abies2,
response = "pr_ab",
predictors = c("aet", "landform"),
predictors_f = NULL,
select_pred = FALSE,
partition = ".part",
thr = "max_sens_spec",
fit_formula = stats::formula(pr_ab ~ s(aet) + s(landform, bs = "re"))
)