fit_XGBoost {PoweREST}R Documentation

Fit with XGBoost

Description

This function estimates the power values based on XGBoost under 3-dimensional monotone constraints upon avg_log2FC, avg_PCT and replicates. This function is recommended when there exist crossings between power surfaces fitted by 'fit_powerest' and used for estimating local power values.

Usage

fit_XGBoost(power,avg_log2FC,avg_PCT,replicates,filter_zero=TRUE,
max.depth=6,eta=0.3,nround=100)

Arguments

power

The raw power values.

avg_log2FC

The corresponding log2FC values.

avg_PCT

The corresponding PCT values.

replicates

The corresponding replicates number.

filter_zero

Whether the user would like to filter to remove the power values being 0. Default=TRUE.

max.depth

Maximum depth of a tree. Default=6.

eta

control the learning rate: scale the contribution of each tree by a factor of 0 < eta < 1 when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Default=0.3.

nround

Max number of boosting iterations.

Value

A object of class 'xgb.Booster'. More information about the content of a 'xgb.Booster' object can be found at the document of R package xgboost.

Author(s)

Lan Shui lshui@mdanderson.org

Examples

data(power_example)
# Fit the local power surface of avg_log2FC_abs between 1 and 2
avg_log2FC_abs_1_2<-dplyr::filter(power_example,avg_log2FC_abs>1 & avg_log2FC_abs<2)
# Fit the model
bst<-fit_XGBoost(power_example$power,avg_log2FC=power_example$avg_log2FC_abs,
avg_PCT=power_example$mean_pct,replicates=power_example$sample_size)


[Package PoweREST version 0.1.0 Index]