posterior_probs: Calculate Probabilities of Binary Outcome
In PaoloLuciano/BPWPM: Bayesian Piecewise Polinomial Model
Description
Usage
Arguments
Value
Description
Given a model, we can calculate the corresponding fitted probabilites of the
random response variable Y. Whereas this is new data or the one used to train
the model. Since the model is a probit GLM at this point, we only need to
calculate the projection and then plug them on the inverse of the normal
gaussian acomulation function
Usage
1
posterior_probs(new_X, bpwpm_params)
Arguments
new_X
A new set of data for which to calculate the f(x) projection
function
bpwpm_params
A list of bpwpm parameters created by the function
posterior_params
Value
A vector of fitted probabilities for the response variable Y
PaoloLuciano/BPWPM documentation built on May 21, 2019, 1:20 p.m.
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Description Usage Arguments Value
Description
Given a model, we can calculate the corresponding fitted probabilites of the random response variable Y. Whereas this is new data or the one used to train the model. Since the model is a probit GLM at this point, we only need to calculate the projection and then plug them on the inverse of the normal gaussian acomulation function
Usage
1 | posterior_probs(new_X, bpwpm_params)
|
Arguments
new_X |
A new set of data for which to calculate the f(x) projection function |
bpwpm_params |
A list of bpwpm parameters created by the function
|
Value
A vector of fitted probabilities for the response variable Y
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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