Usage
reg.fit(y, dataset, event = NULL, reps = NULL, group = NULL, slopes = FALSE,
reml = FALSE, model = NULL, robust = FALSE, xnew = NULL)
Arguments
y
The target (dependent) variable. It can be a numerical variable, factor, ordinal factor, percentages, matrix, or time to event.
If the values are proportions or percentages, i.e. strictly within 0 and 1 they are mapped into R using log( target/(1 - targ
dataset
The indendent variable(s). It can be a vector, a matrix or a dataframe with continuous only variables, a data frame with mixed or only categorical variables.
event
This is NULL unless you have time to event data (survival regression).
reps
This is NULL unless you have time measurements (longitudinal data).
group
This is NULL unless you have grouped (or clustered) data or longitudinal data (is the latter case the arugment reps is required).
slopes
This is for the longitudinal data only, TRUE or FALSE. Should random slopes be added or not?
reml
This is for the longitudinal or grouped data only, TRUE or FALSE. If TRUE, REML will be used, otherwise ML will be used.
model
The type of model you want to use. It can be specified by the user or left NULL, if other correct arguments have been passed.
Poissible values (apart from NULL) are: "gaussian" (default), "binary", "multinomial", "poisson", "ordinal", "Cox", "weibull",
robust
A boolean variable. If TRUE robust models will be returned. Currently this is supported by models "gaussian", "poisson" and "binary".
xnew
If you have new data whose target values you want to predict put it here, otherwise leave it blank.