mseIPCW

Compute Mean Square Error (mse) values for a survival object

 MSE 

 IPCW 

msePICW

Contains tools to build deep neural network with flexible users define loss function and probability models. Several applications included in this package are, 1) The (deepAFT) model, a deep neural network model for accelerated failure time (AFT) model for survival data. 2) The (deepGLM) model, a deep neural network model for generalized linear model (glm) for continuous, categorical and Poisson data.

Bingshu Chen

Deep Neural Network Tools for Probability and Statistic Models

Patrick Norman

Wenyu Jiang

Wanlu Li

msePICW function

<dl> <dt>object</dt>
<dd>the results of a model fit using a deepAFT or a survreg function.</dd> <dt>newdata</dt>
<dd>optional new data at which to do predictions. If absent, predictions are for the dataframe used in the original fit.</dd> <dt>newy</dt>
<dd>optional new outcome variable y.</dd></dl>

Arguments

Author

Mean Square Error (mse) for a survival Object — msePICW

<dl>

 <dt>object</dt>
<dd>the results of a model fit using a deepAFT or a survreg function.</dd>

 <dt>newdata</dt>
<dd>optional new data at which to do predictions. If absent, predictions are for the dataframe used in the original fit.</dd>

 <dt>newy</dt>
<dd>optional new outcome variable y.</dd>

</dl>

msePICW: Mean Square Error (mse) for a survival Object

Description

Usage

Value

Arguments

Author

Details

See Also