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			43 lines
		
	
	
	
		
			1.3 KiB
		
	
	
	
		
			Text
		
	
	
	
	
	
|  | % Generated by roxygen2: do not edit by hand | ||
|  | % Please edit documentation in R/method_random_forest.R | ||
|  | \name{random_forest} | ||
|  | \alias{random_forest} | ||
|  | \title{Predict scores using a random forest.} | ||
|  | \usage{ | ||
|  | random_forest( | ||
|  |   id = "rforest", | ||
|  |   name = "Random forest", | ||
|  |   description = "Assessment by random forest", | ||
|  |   seed = 180199, | ||
|  |   n_models = NULL, | ||
|  |   control_ratio = 0.75 | ||
|  | ) | ||
|  | } | ||
|  | \arguments{ | ||
|  | \item{id}{Unique ID for the method and its results.} | ||
|  | 
 | ||
|  | \item{name}{Human readable name for the method.} | ||
|  | 
 | ||
|  | \item{description}{Method description.} | ||
|  | 
 | ||
|  | \item{seed}{The seed will be used to make the results reproducible.} | ||
|  | 
 | ||
|  | \item{n_models}{This number specifies how many sets of training data should | ||
|  | be created. For each set, there will be a model trained on the remaining | ||
|  | training data and validated using this set. For non-training genes, the | ||
|  | final score will be the mean of the result of applying the different | ||
|  | models. There should be at least two training sets. The analysis will only | ||
|  | work, if there is at least one reference gene per training set. By default, | ||
|  | one model per reference gene will be used.} | ||
|  | 
 | ||
|  | \item{control_ratio}{The proportion of random control genes that is included | ||
|  | in the training data sets in addition to the reference genes. This should | ||
|  | be a numeric value between 0.0 and 1.0.} | ||
|  | } | ||
|  | \value{ | ||
|  | An object of class \code{geposan_method}. | ||
|  | } | ||
|  | \description{ | ||
|  | Predict scores using a random forest. | ||
|  | } |