I've been trying to fit multiple GAMs using the package mgcv
within a function, and crudely select the most appropriate model through model selection procedures. But my function runs the first model then doesn't seem to recognise the input data dat
again.
I get the error
Error in is.data.frame(data) : object 'dat' not found.
I think this is a scoping problem and I've looked here , and here for help but cannot figure it out.
Code and data are as follows (hopefully reproducible): https://github.com/cwaldock1/Help/blob/master/test_gam.csv
library(mgcv)
# Function to fit multiple models
best.mod <- function(dat) {
# Set up control structure
ctrl <- list(niterEM = 0, msVerbose = TRUE, optimMethod="L-BFGS-B")
# AR(1)
m1 <- get.models(dredge(gamm(Temp ~ s(Month, bs = "cc") + s(Date, bs = 'cr') + Year,
data = dat, correlation = corARMA(form = ~ 1|Year, p = 1),
control = ctrl)), subset=1)[[1]]
# AR(2)
m2 <- get.models(dredge(gamm(Temp ~ s(Month, bs = "cc") + s(Date, bs = 'cr') + Year,
data = dat, correlation = corARMA(form = ~ 1|Year, p = 2),
control = ctrl)), subset=1)[[1]]
# AR(3)
m3 <- get.models(dredge(gamm(Temp ~ s(Month, bs = "cc") + s(Date, bs = 'cr') + Year,
data = dat, correlation = corARMA(form = ~ 1|Year, p = 3),
control = ctrl)), subset = 1)[[1]]
### Select best model to work with based on unselective AIC criteria
if(AIC(m2$lme) > AIC(m1$lme)){mod = m1}else{mod = m2}
if(AIC(mod$lme) > AIC(m3$lme)){mod = m3}else{mod = mod}
return(mod$gam)
}
mod2 <- best.mod(dat = test_gam)
Any help would be greatly appreciated.
Thanks, Conor
get.models
evaluates in model's formula
environment, which in gamm
is (always?) .GlobalEnv
, while it should be function's environment (ie sys.frames(sys.nframe())
).
So, instead of
get.models(ms, 1)
use
eval(getCall(ms, 1))
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