interaction_p_value {clinpubr} | R Documentation |
Calculate interaction p-value
Description
This function calculates the interaction p-value between a predictor and a group variable in a linear, logistic, or Cox proportional hazards model.
Usage
interaction_p_value(
data,
y,
predictor,
group_var,
time = NULL,
covars = NULL,
rcs_knots = NULL
)
Arguments
data |
A data frame. |
y |
A character string of the outcome variable. The variable should be binary or numeric and determines the type of model to be used. If the variable is binary, logistic or Cox regression is used. If the variable is numeric, linear regression is used. |
predictor |
A character string of the predictor variable. |
group_var |
A character string of the group variable. The variable should be categorical. If a numeric variable is provided, it will be split by the median value. |
time |
A character string of the time variable. If |
covars |
A character vector of covariate names. |
rcs_knots |
The number of rcs knots. If |
Value
A numerical, the interaction p-value
Examples
data(cancer, package = "survival")
interaction_p_value(
data = cancer, y = "status", predictor = "age", group_var = "sex",
time = "time", rcs_knots = 4
)