fit_kernel_smoothed_stacked_model_fixed_bw: Estimate weights for a linear combination of predictive...
In reichlab/2017-2018-cdc-flu-contest: code for cdc flu contest 2017-2018
Description
Usage
Arguments
Value
View source: R/stacking-utils.R
Description
This function uses the "degenerate EM" algorithm outlined at
http://www.cs.cmu.edu/~roni/11761-f16/Presentations/degenerateEM.pdf,
but modified in a way that is ad hoc but I think could be justified
to use kernel-weighted observations.
Usage
1
2
fit_kernel_smoothed_stacked_model_fixed_bw(component_model_log_scores,
covariate, prediction_covariate, bw, tol = .Machine$double.eps)
Arguments
component_model_log_scores
a data frame or matrix of log scores.
Each column gives log scores for a particular predictive model.
Each row corresponds to one observation
covariate
a single covariate over which weights should be smoothed
(more than one covariate may be supported later)
bw
a bandwidth for the smoothing
tol
numeric, if method was "em", stopping tolerance
Value
model weights
reichlab/2017-2018-cdc-flu-contest documentation built on May 24, 2019, 6:17 a.m.
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Description Usage Arguments Value
View source: R/stacking-utils.R
Description
This function uses the "degenerate EM" algorithm outlined at http://www.cs.cmu.edu/~roni/11761-f16/Presentations/degenerateEM.pdf, but modified in a way that is ad hoc but I think could be justified to use kernel-weighted observations.
Usage
1 2 | fit_kernel_smoothed_stacked_model_fixed_bw(component_model_log_scores,
covariate, prediction_covariate, bw, tol = .Machine$double.eps)
|
Arguments
component_model_log_scores |
a data frame or matrix of log scores. Each column gives log scores for a particular predictive model. Each row corresponds to one observation |
covariate |
a single covariate over which weights should be smoothed (more than one covariate may be supported later) |
bw |
a bandwidth for the smoothing |
tol |
numeric, if method was "em", stopping tolerance |
Value
model weights
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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