I'm using the nlme package to create a generalized least squares model. Here's a reproducible example with a generated dataset:
# Load necessary library
library(nlme)
# Generate some data
set.seed(123)
my_data <- data.frame(
y = rnorm(100),
x = runif(100),
z = factor(rep(1:2, each = 50)),
g = factor(rep(1:2, each = 50)),
h = factor(rep(1:2, 50))
)
# Create the model
mdl <- gls(y ~ x * z,
weights = varIdent(form = ~ 1 | g * h),
data = my_data)
# I can retrieve the model formula like this
model_formula <- formula(mdl)
In this code, I can use formula(mdl) to get the model formula. However, I can't find a way to retrieve the weights argument from the mdl object. How can I do this?
To retrieve the call:
as.list(mdl$call)$weights
# varIdent(form = ~1 | g * h)
To retrieve the weights you can use the nlme::varWeights() function:
The inverse of the standard deviations corresponding to the variance function structure represented by object are returned.
varWeights(mdl$modelStruct)
# 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2
# 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094
# 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2 1*1 1*2
# 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094 1.0000000 0.8064094
# etc