CrossGEE {CrossCarry}

Run a GEE model for data from a crossover experiment

Description

Provides a GEE model for the data of a crossover design with S sequences of T periods. There must be one observation of each experimental unit in each period.

Usage

CrossGEE(
  response,
  period,
  treatment,
  id,
  carry,
  covar = NULL,
  data,
  family = gaussian(),
  correlation = "independence",
  formula = NULL
)

Arguments

response

A character string specifying the name of the response variable of the crossover experimental design

period

A character string specifying the name of vector with the observation period of the responses of the crossover experimental design

treatment

A character string specifying the name of vector with the treatment applied at each observation of the crossover experimental design

id

A character string specifying the name of vector which identifies the experimental units. The length of ‘id’ should be the same as the number of observations. Data are assumed to be sorted so that observations on each cluster appear as contiguous rows in data. If data is not sorted this way, the function will not identify the clusters correctly. If data is not sorted this way, a warning will be issued.

carry

A vector of character string specifying the name set of dummy variables that indicates the treatment applied in the previous period of each experimental unit. They must be 0 in period 1

covar

A vector of character string specifying the name of possible covariates of the crossover experimental design

data

A data frame with all the variables of the crossover experimental design

family

See corresponding documentation to glm

correlation

a character string specifying the correlation structure. The following are permitted: "independence", "exchangeable", "ar1" and "unstructured"

formula

A formula related the response variable with the explanatory variables. If it is NULL, formula response~period+treatment+carry+covar will be evaluated

Value

QIC The QIC of the models: The model are fitted by geeglm

model The model fitted by geeglm.

Source

https://doi.org/10.1111/stan.12295

References

Cruz, N. A., López Pérez, L. A., & Melo, O. O. (2023). Analysis of cross-over experiments with count data in the presence of carry-over effects. Statistica Neerlandica, 77(4), 516-542.

Examples

data(Water)
model <- CrossGEE(response="LCC", covar=c("Age"), period="Period",
                  treatment = "Treatment", id="ID", carry="Carry_Agua",
                  family=gaussian(),correlation ="ar1" ,data=Water)

model$QIC
model$model

summary(model$model)