MRM {mvs} | R Documentation |
Minority Report Measure
Description
Calculate the Minority Report Measure (MRM) for each view in a (hierarchical) multi-view stacking model.
Usage
MRM(fit, constant, level = 2, a = 0, b = 1, cvlambda = "lambda.min")
mrm(fit, constant, level = 2, a = 0, b = 1, cvlambda = "lambda.min")
Arguments
fit |
an object of class |
constant |
the value at which to keep the predictions of the other views constant. The recommended value is the mean of the outcome variable. |
level |
the level at which to calculate the MRM. In a 3-level MVS model, |
a |
the start of the interval over which to calculate the MRM. Defaults to 0. |
b |
the end of the interval over which to calculate the MRM. Defaults to 1. |
cvlambda |
denotes which values of the penalty parameters to use for calculating predictions. This corresponds to the defaults used during model fitting. |
Details
The Minority Report Measure (MRM) considers the view-specific sub-models at a given level of the hierarchy as members of a committee making predictions of the outcome variable. For each view, the MRM quantifies how much the final prediction of the stacked model changes if the prediction of the corresponding sub-model changes from a
to b
, while keeping the predictions corresponding to the other views constant at constant
. For more information about the MRM see <doi:10.3389/fnins.2022.830630>.
Value
A numeric vector of a length equal to the number of views at the specified level, containing the values of the MRM for each view.
Author(s)
Wouter van Loon <w.s.van.loon@fsw.leidenuniv.nl>
Examples
set.seed(012)
n <- 1000
X <- matrix(rnorm(8500), nrow=n, ncol=85)
beta <- c(rep(10, 55), rep(0, 30)) * ((rbinom(85, 1, 0.5)*2)-1)
eta <- X %*% beta
p <- 1 /(1 + exp(-eta))
y <- rbinom(n, 1, p)
## 3-level MVS
bottom_level <- c(rep(1:3, each=15), rep(4:5, each=10), rep(6:9, each=5))
top_level <- c(rep(1,45), rep(2,20), rep(3,20))
views <- cbind(bottom_level, top_level)
fit <- MVS(x=X, y=y, views=views, levels=3, alphas=c(0,1,1), nnc=c(0,1,1))
MRM(fit, constant=mean(y))