xMLglmnet | R Documentation

## Function to integrate predictor matrix in a supervised manner via machine learning algorithm glmnet.

### Description

`xMLglmnet` is supposed to integrate predictor matrix in a supervised manner via machine learning algorithm glmnet. It requires three inputs: 1) Gold Standard Positive (GSP) targets; 2) Gold Standard Negative (GSN) targets; 3) a predictor matrix containing genes in rows and predictors in columns, with their predictive scores inside it. It returns an object of class 'pTarget'.

### Usage

    xMLglmnet(df_predictor, GSP, GSN, family = c("binomial", "gaussian"),
    type.measure = c("auc", "mse"), nfold = 3, alphas = seq(0, 1, 0.1),
    standardize = TRUE, lower.limits = -Inf, verbose = TRUE,
    RData.location = "http://galahad.well.ox.ac.uk/bigdata", ...)

### Arguments

`df_predictor` | a data frame containing genes (in rows) and predictors (in columns), with their predictive scores inside it. This data frame must has gene symbols as row names
`GSP` | a vector containing Gold Standard Positive (GSP)
`GSN` | a vector containing Gold Standard Negative (GSN)
`family` | response family type. It can be one of "binomial" for two-class logistic model or "gaussian" for gaussian model
`type.measure` | loss to use for cross-validation. It can be one of "auc" for two-class logistic model, "mse" for the deviation from the fitted mean to the response using gaussian model
`nfold` | an integer specifying the number of folds for cross validataion
`alphas` | a vector specifying a range of alphas. Alpha is an elasticnet mixing parameter, with 0<=alpha<=1. By default, seq(0,1,by-0.1)
`standardize` | logical specifying whether to standardise the predictor. If yes (by default), the predictor standardised prior to fitting the model. The coefficients are always returned on the original scale
`lower.limits` | vector of lower limits for each coefficient (by default, '-Inf'; all should be non-positive). A single value provided will apply to every coefficient
`verbose` | logical to indicate whether the messages will be displayed in the screen. By default, it sets to TRUE for display
`RData.location` | the characters to tell the location of built-in RData files. See `xRDataLoader` for details
`...` | additional parameters. Please refer to 'glmnet::cv.glmnet' for the complete list.

### Value

an object of class "pTarget", a list with following components:

- `model`: an object of class "cv.glmnet" as a best model

- `priority`: a data frame of nGene X 5 containing gene priority information, where nGene is the number of genes in the input data frame, and the 5 columns are "GS" (either 'GSP', or 'GSN', or 'NEW'), "name" (gene names), "rank" (ranks of the priority scores), "priority" (priority score; rescaled into the 5-star ratings), and "description" (gene description)

- `predictor`: a data frame, which is the same as the input data frame but inserting an additional column 'GS' in the first column

- `cvm2alpha`: a data frame of nAlpha X 2 containing mean cross-validated error, where nAlpha is the number of alpha and the two columns are "min" (lambda.min) and "1se" (lambda.1se)

- `nonzero2alpha`: a data frame of nAlpha X 2 containing the number of non-zero coefficients, where nAlpha is the number of alpha and the two columns are "min" (lambda.min) and "1se" (lambda.1se)

- `importance`: a data frame of nPredictor X 1 containing the predictor importance/coefficient info

- `performance`: a data frame of 1+nPredictor X 2 containing the supervised/predictor performance info predictor importance info, where nPredictor is the number of predictors, two columns are "ROC" (AUC values) and "Fmax" (F-max values)

- `gp`: a ggplot object for the ROC curve

- `call`: the call that produced this result

### Note

none

### Examples

    ## Not run:
    # Load the library
    library(Pi)

    ## End(Not run)
    RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev"
    ## Not run:
    pTarget <- xMLglmnet(df_prediction, GSP, GSN)

    ## End(Not run)