PLSDA_from_file_and_predict_second_dataset: Partial Least Squares and predict second dataset

In graeberlab/small.cell.project: small.cell.project

Description

Builds PLS model from training dataset and Writes out loadings. Predicts classifications of test dataset and returns dataframe of predictions

Usage

PLSDA_from_file_and_predict_second_dataset(file, file2, sample.names,
  sample.type, y.response, comps = 3, scale = F, ind.names = F,
  output_folder = "./", train_string = "", test_string = "",
  comp.x = "comp.1", comp.y = "comp.2", TCGA = F, plot_both = T,
  colpalette = NULL, shape.palette = NULL, labels = F,
  legendname = "default", ellipses = F, saveplot = T,
  savetype = ".png", w = 8, h = 6, do.legend = T,
  title = "PLSDA")

Arguments

file	file for X matirx
file2	file for test data matrix
sample.names	Vector of sample names in X matrix
comps	number of components to compute
scale	default=T
ind.names	Labels the samples, default =F
output_folder	the output_folder to write files to
train_string	string of training data to insert in file name of predicted scores
test_string	string of data being tested to insert in file name of predicted scores
TCGA	default is false, true if test samples are TCGA, removes normal samples
plot_both	if true plots both training and test set in color
colpalette	allows you to put in a color palette of form c("#F87660", "#39B600",....etc) to manually assign colors
shape.palette	allows you to put in a shape palette of form c(1, 3,....etc) to manually assign shapes
response.values	Vector of response values in same order matching sample.names
sample.names2	Vector of sample names in 2nd dataset, if needed
response.values	Vector of response values in same order matching sample.names2, if available

graeberlab/small.cell.project documentation built on May 12, 2019, 5:16 p.m.