RF_Pred_Regression: Predict continuous variables based on a taxa read count table...
In wdl2459/ConQuR: Batch Effects Removal for Microbiome Data in Large-Scale Epidemiology Studies via Conditional Quantile Regression
View source: R/supporting_functions.R
RF_Pred_Regression R Documentation
Predict continuous variables based on a taxa read count table by random forest
Description
Predict continuous variables based on a taxa read count table by random forest
Usage
RF_Pred_Regression(TAX, variable, fold = 5, seed = 2020)
Arguments
TAX
The taxa read count table, samples (row) by taxa (col).
variable
The continuous variable to predict.
fold
The number of folds; default is 5.
seed
The seed to generate fold indices for samples; default is 2020.
Value
A list
pred - A table summarizing the predicted and true values for all samples.
rmse_across_fold - RMSEs across folds.
wdl2459/ConQuR documentation built on Aug. 28, 2022, 6:08 a.m.
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View source: R/supporting_functions.R
| RF_Pred_Regression | R Documentation |
Predict continuous variables based on a taxa read count table by random forest
Description
Predict continuous variables based on a taxa read count table by random forest
Usage
RF_Pred_Regression(TAX, variable, fold = 5, seed = 2020)
Arguments
TAX |
The taxa read count table, samples (row) by taxa (col). |
variable |
The continuous variable to predict. |
fold |
The number of folds; default is 5. |
seed |
The seed to generate fold indices for samples; default is 2020. |
Value
A list
pred - A table summarizing the predicted and true values for all samples.
rmse_across_fold - RMSEs across folds.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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