Description Usage Arguments Examples
Compute variable importance for different groups of variables by comparing the R-squared for the optimally combined outcome.
1 2 3 4 |
optWeightObject |
An |
r2_optWeightObject |
An |
Y |
The |
X |
The |
verbose |
A |
grpX |
A |
comparison |
What type of comparison should be made. Possible choices include
|
parallel |
A |
n.cores |
A |
seed |
The seed to set before each internal call to |
alpha |
The function returns a |
... |
Other arguments (not currently used) |
1 2 3 4 5 6 7 8 | X <- data.frame(x1=runif(n=100,0,5), x2=runif(n=100,0,5))
Y1 <- rnorm(100, X$x1 + X$x2, 1)
Y2 <- rnorm(100, X$x1 + X$x2, 3)
Y <- data.frame(Y1 = Y1, Y2 = Y2)
fit <- optWeight(Y = Y, X = X, SL.library = c("SL.glm","SL.mean"),
family = "gaussian",outerV = 10, return.CV.SuperLearner = FALSE)
perf.fit <- r2_optWeight(object = fit, Y = Y, X = X, evalV = 5)
varImp <- r2_varImp(fit, perf.fit, Y = Y, X = X)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.