PsychModels function

Fit Multiple Psychometric Functions with Generalized Linear Models (GLM)

The function fits psychometric functions to data using glm for multiple groups. It supports the use of a binomial error distribution.

PsychModels(data, group_factors = NULL, formula, link = "probit")

Arguments

• data: a data frame containing the variables to be used in the model.
• group_factors: a character vector specifying the grouping variables in the dataset. If NULL, the model will be fit to the entire dataset without grouping.
• formula: the formula of the glm model. The response should consist of a binomial outcome (e.g., cbind(yes, no)).
• link: the link function. A character string specifying the link function to be used. By default, "probit" is used. See glm for available link functions.

Details

This function allows the fitting of psychometric functions to grouped data. If grouping variables are provided through group_factors, separate models are fit to each group. The function returns a list of models, one for each group, where the model for each group is fitted using the specified formula and link.

The models are returned as a named list, with each list element containing the fitted GLM model and the associated group-level information.

Examples

model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X,
data = simul_data,
group_factors = "Subject")

model_list_vibro <- PsychModels(vibro_exp3,
group_factors = c("subject", "vibration"),
formula = cbind(faster, slower) ~ speed)

See Also

glm, PsychParameters