funbarRF (version 1.0.2)

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

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.

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

# NOT RUN {
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)


##################################
# }
# NOT RUN {
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)
# }