details_linear_reg_gee | R Documentation |

`gee::gee()`

uses generalized least squares to fit different types of models
with errors that are not independent.

For this engine, there is a single mode: regression

This model has no formal tuning parameters. It may be beneficial to
determine the appropriate correlation structure to use, but this
typically does not affect the predicted value of the model. It *does*
have an effect on the inferential results and parameter covariance
values.

The **multilevelmod** extension package is required to fit this model.

library(multilevelmod) linear_reg() %>% set_engine("gee") %>% set_mode("regression") %>% translate()

## Linear Regression Model Specification (regression) ## ## Computational engine: gee ## ## Model fit template: ## multilevelmod::gee_fit(formula = missing_arg(), data = missing_arg(), ## family = gaussian)

`multilevelmod::gee_fit()`

is a wrapper model around `gee::gee()`

.

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.

The model cannot accept case weights.

Both `gee:gee()`

and `gee:geepack()`

specify the id/cluster variable
using an argument `id`

that requires a vector. parsnip doesn’t work that
way so we enable this model to be fit using a artificial function
`id_var()`

to be used in the formula. So, in the original package, the
call would look like:

gee(breaks ~ tension, id = wool, data = warpbreaks, corstr = "exchangeable")

With parsnip, we suggest using the formula method when fitting:

library(tidymodels) linear_reg() %>% set_engine("gee", corstr = "exchangeable") %>% fit(breaks ~ tension + id_var(wool), data = warpbreaks)

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 GEE formula when adding the model:

library(tidymodels) gee_spec <- linear_reg() %>% set_engine("gee", corstr = "exchangeable") gee_wflow <- workflow() %>% # The data are included as-is using: add_variables(outcomes = breaks, predictors = c(tension, wool)) %>% add_model(gee_spec, formula = breaks ~ tension + id_var(wool)) fit(gee_wflow, data = warpbreaks)

The `gee::gee()`

function always prints out warnings and output even
when `silent = TRUE`

. The parsnip `"gee"`

engine, by contrast, silences
all console output coming from `gee::gee()`

, even if `silent = FALSE`

.

Also, because of issues with the `gee()`

function, a supplementary call
to `glm()`

is needed to get the rank and QR decomposition objects so
that `predict()`

can be used.

The underlying model implementation does not allow for case weights.

Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models.

*Biometrika*, 73 13–22.Zeger, S.L. and Liang, K.Y. (1986) Longitudinal data analysis for discrete and continuous outcomes.

*Biometrics*, 42 121–130.

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