get.mf.object.glm: Fit a general linear model to a mobForest model
In RTIInternational/mobForest: Model Based Random Forest Analysis

This method computes predicted outcome for each observation in the data frame using the tree model supplied as an input argument.

1 2	get.mf.object.glm(object, main_model, partition_vars, data, new_test_data, ntree, family, prob_cutoff = 0.5)

`object`	A bootstrap model object created by bootstrap()
`main_model`	A model in character format.
`partition_vars`	A vector of partition variables.
`data`	A data frame containing the variables in the model.
`new_test_data`	A data frame representing test data for validating random forest model. This data is not used in the tree building process
`ntree`	Number of trees to be constructed in forest (default = 300).
`family`	A description of error distribution and link function to be used in the model. This parameter needs to be specified if generalized linear model is considered. The parameter "binomial()" is to be specified when logistic regression is considered and "poisson()" when Poisson regression is considered as the node model. The values allowed for this parameter are binomial() and poisson().
`prob_cutoff`	In case of logistic regression as a node model, the predicted probabilities for OOB cases are converted into classes (yes/no, high/low, etc as specified) based on this probability cutoff. If logistic regression is not considered as node model, the prob_cutoff = NULL. By default it is 0.5 when parameter not specified (and logistic regression considered).