est_kcde_params_stepwise_crossval: Use a stepwise procedure to estimate parameters and select...
In reichlab/kcde: Kernel conditional density estimation with flexible kernel specifications
Description
Usage
Arguments
Value
Description
Use a stepwise procedure to estimate parameters and select lags by optimizing
a cross-validation estimate of predictive performance
Usage
1
2
est_kcde_params_stepwise_crossval(data, kcde_control, init_theta_vector,
init_phi_vector)
Arguments
data
the data frame to use in performing cross validation
kcde_control
a list of parameters specifying how the fitting is done
Value
a list with two components: vars_and_offsets is the estimated "optimal" lags
to use for each variable, and theta_hat is the estimated "optimal"
kernel parameters to use for each combination of variable and lag
reichlab/kcde documentation built on May 27, 2019, 4:53 a.m.
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Description Usage Arguments Value
Description
Use a stepwise procedure to estimate parameters and select lags by optimizing a cross-validation estimate of predictive performance
Usage
1 2 | est_kcde_params_stepwise_crossval(data, kcde_control, init_theta_vector,
init_phi_vector)
|
Arguments
data |
the data frame to use in performing cross validation |
kcde_control |
a list of parameters specifying how the fitting is done |
Value
a list with two components: vars_and_offsets is the estimated "optimal" lags to use for each variable, and theta_hat is the estimated "optimal" kernel parameters to use for each combination of variable and lag
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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