pISVMsequences: pISVMsequences

In ypriverol/pIR: pIR: Estimating Isoelectric Point (pI) of Peptide and Proteins from Amino Acid Sequence.

Description

This function predict the pI from multiple sequences contained into dataframe.

Usage

pISVMsequences(df = dataframe, model = "default", newModel = FALSE)

Arguments

df	The dataset with sequences. It must contains the variables: "calibrated", "expasy" and "aaindex".
model	The SVM-based model to be used in the prediction (use "default", "heller" or "branca" options)
newModel	A flag enabling the posibility to choose a new model to be used.

Details

By default, this method use a svm-model from the setting of the "model" parameter and keeping

the parameter "newModel" = FALSE. However, it is possible to build a new svm model from the current dataset

setting "newModel"=TRUE. To do it, The input dataframe must contains the variables requeried to train

a svm model: "calibrated", "expasy" and "aaindex".

ypriverol/pIR documentation built on May 4, 2019, 5:33 p.m.