predict_test_funbarRF: Prediction of species label for the query fungal barcode...

In funbarRF: Fungal Species Identification using DNA Barcode with Random Forest

Description

This function can be used for predicting the species labels for the fungal barcode sequences of the query set, using the model trained with reference barcode sequences.

Usage

predict_test_funbarRF (object1, object2, m_try = 10, n_tree = 500)

Arguments

object1	An object created by the function seq_funbarRF or seq_funbarRF_manual , with reference dataset as input.
object2	An object created by the function seq_funbarRF or seq_funbarRF_manual , with query dataset as input.
m_try	This parameter is required for randomForest. It represents the number (must be an integer) of variables to be randomly sampled at each split. Default value is 10.
n_tree	This is also a parameter for randomForest. It denotes the number (must be an integer) of tree-based classifiers to be built. This should not be set to too small, to ensure that every instance gets predicted at least a few times. Default is 500.

Value

A dataframe consisting of predicted species label for each sequence of the query dataset.

Author(s)

Prabina Kumar Meher, Division of Statistical Genetics,Indian Agricultural Statistics Research Institute, New Delhi-110012, INDIA

References

1. Liaw A., and Wiener M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22.

2. Meher P.K., Sahu T.K., and Rao A.R. (2016). Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier. Gene, 592(2), 316-324.

See Also

randomForest, predict_train_funbarRF, predict

Examples

data (data_barcode)
train1 <- seq_funbarRF_manual (manual_seq=data_barcode$Fish$train[1:30])
test1 <- seq_funbarRF_manual (manual_seq=data_barcode$Fish$test[1:3])
res1 <- predict_test_funbarRF (object1=train1, object2=test1, m_try = 10, n_tree = 5)
# kindly use large number of n_tree
print(res1)


##################################

data (data_barcode)
train2 <- seq_funbarRF_manual (manual_seq=data_barcode$Inga$train[1:30])
test2 <- seq_funbarRF_manual (manual_seq=data_barcode$Inga$test[1:3])
res2 <- predict_test_funbarRF (object1=train2, object2=test2, m_try = 10, n_tree = 20) 
# kindly use large number of n_tree
print(res2)

funbarRF documentation built on May 27, 2019, 5:03 p.m.