Returns a dataframe with the model summary and global p-value for multi-level variables.
mvsum(
model,
data,
digits = getOption("reportRmd.digits", 2),
showN = TRUE,
showEvent = TRUE,
markup = TRUE,
sanitize = TRUE,
nicenames = TRUE,
CIwidth = 0.95,
vif = TRUE
)fitted model object
dataframe containing data
number of digits to round to
boolean indicating sample sizes should be shown for each comparison, can be useful for interactions
boolean indicating if number of events should be shown. Only available for logistic.
boolean indicating if you want latex markup
boolean indicating if you want to sanitize all strings to not break LaTeX
boolean indicating if you want to replace . and _ in strings with a space.
width for confidence intervals, defaults to 0.95
boolean indicating if the variance inflation factor should be included. See details
Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.
If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.
VIF for competing risk models is computed by fitting a linear model with a dependent variable comprised of the sum of the model independent variables and then calculating VIF from this linear model.
John Fox & Georges Monette (1992) Generalized Collinearity Diagnostics, Journal of the American Statistical Association, 87:417, 178-183, DOI: 10.1080/01621459.1992.10475190
John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage.