get_model: Calculate a prediction model

In ParkerICI/kumquat: Identification of stratifying subpopulations in Flow Cytometry Data (based on the Citrus package)

Description

This function is the main wrapper around the Citrus model building functionality

Usage

get_model(features, endpoint, model.type)

Arguments

endpoint	A vector of length equal to nrow(features) containing the values to be used as prediction endpoints. If this vertor is numeric a model of family "continuous" is generated, otherwise if it is a character or factor vector the model will be of family "classification"
model.type	The type of model, either "pamr", "sam" or "glmnet"

Value

A citrus.regressionResult object with the following properties:

regularizationThresholds	Regularization thresholds used to constrain all constructed models. Not applicable for sam models.
foldModels	A citrus.endpointModel constructed from each independent fold feature set. NULL if nFolds = 1.
finalModel	A citrus.endpointModel constructed from features derived from the clustering of all samples together.
thresholdCVRates	Matrix containing the average error rate and standard error of models at each regularization threshold. FDR also reported where possible.
cvMinima	Values and indicies of pre-selected cross-validation error-rate thresholds.
differentialFeatures	Non-zero model features and corresponding clusters from the finalModel constrained by cvMinima.
modelType	Type of model fit on data.
family	Family of regression model.
labels	Endpoint labels of analyzed samples.

ParkerICI/kumquat documentation built on Dec. 18, 2021, 6:40 a.m.