prediction on new features using a pltr tree and the name of the confounding variable
Usage
predict_pltr(xtree, xdata, Y.name, X.names, newdata, type = "response",
family = 'binomial', thresshold = seq(0.1, 0.9, by = 0.1))
Arguments
xtree
a tree obtained with the pltr procedure
xdata
the dataframe used to learn the pltr model
Y.name
the name of the main variable
X.names
the names of the confounding variables
newdata
the new data with all the predictors and the dependent variable
type
the type of prediction
family
the glm family considered
thresshold
the thresshold value to consider for binary prediction. It could be a vector, helping to compute the AUC
Value
A list of two element
predict_glm
the predicted vector, depending on the family used. For the binomial family with a vector of thresshold, a matrix with each column corresponding to a thresshold value
ERR_PRED
either the prediction error of the pltr procedure on the test set or a vector of prediction error when the family is binomial with a vector of thresshold values
Author(s)
Cyprien Mbogning
References
Mbogning, C., Perdry, H., Toussile, W., Broet, P.: A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities. Journal of Clinical Bioinformatics 4:6, (2014)