coef_ordered.cv.multiview: Extract an ordered list of standardized coefficients from a...

In multiview: Cooperative Learning for Multi-View Analysis

Extract an ordered list of standardized coefficients from a cv.multiview object

Description

This function extracts a ranked list of coefficients after the coefficients are standardized by the standard deviation of the corresponding features. The ranking is based on the magnitude of the standardized coefficients. It also outputs the data view to which each coefficient belongs.

Usage

## S3 method for class 'cv.multiview'
coef_ordered(object, s = c("lambda.1se", "lambda.min"), ...)

Arguments

object	Fitted "cv.multiview" object.
s	Value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object. Alternatively s="lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used. (For historical reasons we use the symbol 's' rather than 'lambda' to reference this parameter.)
...	This is the mechanism for passing arguments like ⁠x=⁠ when exact=TRUE; see exact argument.

Details

The output table shows from left to right the data view each coefficient comes from, the column index of the feature in the corresponding data view, the coefficient after being standardized by the standard deviation of the corresponding feature, and the original fitted coefficient.

Value

data frame of consisting of view name, view column, coefficient and standardized coefficient ordered by rank of standardized coefficient.

Examples

set.seed(1)
x = matrix(rnorm(100*20), 100, 20)
z = matrix(rnorm(100*20), 100, 20)
U = matrix(rnorm(100*5), 100, 5)
for (m in seq(5)){
    u = rnorm(100)
    x[, m] = x[, m] + u
    z[, m] = z[, m] + u
    U[, m] = U[, m] + u}
x = scale(x, center = TRUE, scale = FALSE)
z = scale(z, center = TRUE, scale = FALSE)
beta_U = c(rep(0.1, 5))
y = U %*% beta_U + 0.1 * rnorm(100)
fit1 = cv.multiview(list(x=x,z=z), y, rho = 0.3)
coef_ordered(fit1, s="lambda.min")

# Binomial

by = 1 * (y > median(y)) 
fit2 = cv.multiview(list(x=x,z=z), by, family = binomial(), rho = 0.9)
coef_ordered(fit2, s="lambda.min")

# Poisson
py = matrix(rpois(100, exp(y))) 
fit3 = cv.multiview(list(x=x,z=z), py, family = poisson(), rho = 0.6)
coef_ordered(fit3, s="lambda.min")

multiview documentation built on April 3, 2023, 5:20 p.m.