In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("linear_reg", "gls")

Tuning Parameters

This model has no tuning parameters.

Translation from parsnip to the original package

r uses_extension("linear_reg", "gls", "regression")

library(multilevelmod)

linear_reg() %>% 
  set_engine("gls") %>% 
  set_mode("regression") %>% 
  translate()

Preprocessing requirements

There are no specific preprocessing needs. However, it is helpful to keep the clustering/subject identifier column as factor or character (instead of making them into dummy variables). See the examples in the next section.

Other details

The model can accept case weights.

With parsnip, we suggest using the fixed effects formula method when fitting, but the details of the correlation structure should be passed to set_engine() since it is an irregular (but required) argument:

library(tidymodels)
# load nlme to be able to use the `cor*()` functions
library(nlme)

data("riesby")

linear_reg() %>% 
  set_engine("gls", correlation =  corCompSymm(form = ~ 1 | subject)) %>% 
  fit(depr_score ~ week, data = riesby)

When using tidymodels infrastructure, it may be better to use a workflow. In this case, you can add the appropriate columns using add_variables() then supply the typical formula when adding the model:

library(tidymodels)

gls_spec <- 
  linear_reg() %>% 
  set_engine("gls", correlation =  corCompSymm(form = ~ 1 | subject))

gls_wflow <- 
  workflow() %>% 
  # The data are included as-is using:
  add_variables(outcomes = depr_score, predictors = c(week, subject)) %>% 
  add_model(gls_spec, formula = depr_score ~ week)

fit(gls_wflow, data = riesby)