Description Usage Arguments Details Value Examples
In general, the function passed to sl_control$optim_risk
should expect a named list
of outcomes (Y) and predictions (pred) in validation folds and should return a criteria by
which super learner weights may be optimized. The weights are input to the function via
sl_weight
and are optimized in the sl_control$weight_fn
. See
Examples section below for an example of the format of the input
list used
for sl_control$optim_risk
functions.
1 2 | optim_risk_sl_nloglik(sl_weight, input, sl_control, l = 0, u = 1,
trim = 0.001)
|
sl_weight |
A numeric vector of super learner weights corresponding to each
|
input |
A list where each entry corresponds to a validation fold. Each entry is a list
with entries: Y (univariate outcome for this validation fold), pred (matrix of predictions
from |
sl_control |
Super learner control options. |
l |
Lower bound on outcomes |
u |
Upper bound on outcomes |
trim |
Where to trim |
In this case, the function computes cross-validated (quasi-) log-likelihood for the outcomes
scaled to be between 0 and 1. The option trim
must be a value greater than
zero in order that the loss is bounded. The bounds on the outcome are set via
l
and u
.
Numeric value of cross-validated negative log-likelihood
1 2 3 4 5 6 7 8 9 10 11 12 13 | #Simulate data with proper format:
#Y is one component of the multivariate outcome, and
#pred is the predictions made by learners.
input <- list(Y = rnorm(50), pred = cbind(rnorm(50), rnorm(50)))
#Made up weights:
sl_weight <- c(0.5, 0.5)
#Linear ensemble:
sl_control <- list(ensemble_fn = "ensemble_linear")
#Get risk by setting l and u to min and max:
risk <- optim_risk_sl_nloglik(sl_weight, input, sl_control, l = min(input$Y), u = max(input$Y))
|
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