bm_glm: Fit Generalized Linear Models
In tsensembler: Dynamic Ensembles for Time Series Forecasting
Description
Usage
Arguments
Details
See Also
Description
Learning a Generalized Linear Model
from training data. Parameter setting
can vary in alpha.
See glmnet for a comprehensive description.
Usage
1
Arguments
form
formula
data
training data for building the predictive
model
lpars
a list containing the learning parameters
Details
Imports learning procedure from glmnet package.
See Also
other learning models: bm_mars;
bm_ppr; bm_gbm;
bm_gaussianprocess; bm_cubist;
bm_randomforest; bm_pls_pcr;
bm_ffnn; bm_svr
Other base learning models:
bm_cubist(),
bm_ffnn(),
bm_gaussianprocess(),
bm_gbm(),
bm_mars(),
bm_pls_pcr(),
bm_ppr(),
bm_randomforest(),
bm_svr()
tsensembler documentation built on Oct. 27, 2020, 5:07 p.m.
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Description Usage Arguments Details See Also
Description
Learning a Generalized Linear Model
from training data. Parameter setting
can vary in alpha.
See glmnet for a comprehensive description.
Usage
1 |
Arguments
form |
formula |
data |
training data for building the predictive model |
lpars |
a list containing the learning parameters |
Details
Imports learning procedure from glmnet package.
See Also
other learning models: bm_mars;
bm_ppr; bm_gbm;
bm_gaussianprocess; bm_cubist;
bm_randomforest; bm_pls_pcr;
bm_ffnn; bm_svr
Other base learning models:
bm_cubist(),
bm_ffnn(),
bm_gaussianprocess(),
bm_gbm(),
bm_mars(),
bm_pls_pcr(),
bm_ppr(),
bm_randomforest(),
bm_svr()
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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