Kernel Semi-Parametric Models
Case names of fitted models
Extract Model Coefficients
Confidence interavls for linear part of model parameters
Cook's distance for a Kernel Semi Parametric Model Fit
Computing kernel function derivatives
Model deviance
Extract AIC from a Kernel Semi Parametric Model
Extract Model Fitted values
Summarizing Kernel Semi parametric Model Fits with flexible parameters...
compute Kernel Semi Parametric model parameters
Extract Model Hyper-parameter
Giving information about Kernel Semi parametric Model Fits
Kernel Functions
List of kernel parts included in the kernel semi parametric model
Kernel matrix
some internal methods in computation of kernel semi parametric model
Create a Kernel Object
Fitting Kernel Semi Parametric model
Control various aspects of the optimisation problem
Log Likelihood of a kspm Object
Computation of the leave one out error (LOOE) in kernel semi parametri...
Extract the number of observations from a Kernel Semi parametric Model...
Plot derivatives of a kspm object
Plot Diagnostics for a kspm Object
Predicting Kernel Semi parametric Model Fits
Print results from a Kernel Semi parametric Model Fit
Extract residuals from a Kernel Semi Parametric Model
Standardized residuals for Kernel Semi parametric Model Fits
Optimisation to cumpute hyperparameter in Kernel Semi Parametric model
Extract residuals standard deviation
Choose a model by AIC or BIC in a Stepwise Algorithm
Summarizing Kernel Semi parametric Model Fits
Score Tests for kernel part in kernel semi parametric model
Variable names of fitted models
To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.