R: Longitudinal Gaussian Process Regression

# Longitudinal Gaussian Process Regression

## Documentation for package ‘lgpr’ version 1.2.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

A B C D E F G I K L M N O P Q R S T U V W misc

lgpr-package | The 'lgpr' package.

## -- A --

add_dis_age | Easily add the disease-related age variable to a data frame
add_factor | Easily add a categorical covariate to a data frame
add_factor_crossing | Add a crossing of two factors to a data frame
adjusted_c_hat | Set the GP mean vector, taking TMM or other normalization into account
apply_scaling | Apply variable scaling
as.character-method | Character representations of different formula objects
as_character | Character representations of different formula objects

## -- B --

bet | Prior definitions

## -- C --

clear_postproc | S4 generics for lgpfit, lgpmodel, and other objects
clear_postproc-method | An S4 class to represent the output of the 'lgp' function
component_info | S4 generics for lgpfit, lgpmodel, and other objects
component_info-method | An S4 class to represent an additive GP model
component_names | S4 generics for lgpfit, lgpmodel, and other objects
component_names-method | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
component_names-method | An S4 class to represent input for kernel matrix computations
component_names-method | An S4 class to represent prior or posterior draws from an additive function distribution.
component_names-method | An S4 class to represent the output of the 'lgp' function
component_names-method | An S4 class to represent an additive GP model
contains_postproc | S4 generics for lgpfit, lgpmodel, and other objects
contains_postproc-method | An S4 class to represent the output of the 'lgp' function
covariate_info | S4 generics for lgpfit, lgpmodel, and other objects
covariate_info-method | An S4 class to represent an additive GP model
create_model | Create a model
create_model.covs_and_comps | Parse the covariates and model components from given data and formula
create_model.formula | Create a model formula
create_model.likelihood | Parse the response variable and its likelihood model
create_model.options | Parse the given modeling options
create_model.prior | Parse given prior
create_plot_df | Helper function for plots
create_scaling | Create a standardizing transform

## -- D --

dinvgamma_stanlike | Density and quantile functions of the inverse gamma distribution
draw_pred | Draw pseudo-observations from posterior or prior predictive distribution

## -- E --

example_fit | Quick way to create an example lgpfit, useful for debugging

## -- F --

fit_summary | Print a fit summary.

## -- G --

gam | Prior definitions
gam, | Prior definitions
GaussianPrediction | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
GaussianPrediction-class | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
get_draws | Extract parameter draws from lgpfit or stanfit
get_model | S4 generics for lgpfit, lgpmodel, and other objects
get_model-method | An S4 class to represent the output of the 'lgp' function
get_pred | Extract model predictions and function posteriors
get_stanfit | S4 generics for lgpfit, lgpmodel, and other objects
get_stanfit-method | An S4 class to represent the output of the 'lgp' function

## -- I --

igam | Prior definitions
igam, | Prior definitions
is_f_sampled | S4 generics for lgpfit, lgpmodel, and other objects
is_f_sampled-method | An S4 class to represent the output of the 'lgp' function
is_f_sampled-method | An S4 class to represent an additive GP model

## -- K --

kernel | Compute a kernel matrix (covariance matrix)
KernelComputer | An S4 class to represent input for kernel matrix computations
KernelComputer-class | An S4 class to represent input for kernel matrix computations
kernel_beta | Compute a kernel matrix (covariance matrix)
kernel_bin | Compute a kernel matrix (covariance matrix)
kernel_cat | Compute a kernel matrix (covariance matrix)
kernel_eq | Compute a kernel matrix (covariance matrix)
kernel_ns | Compute a kernel matrix (covariance matrix)
kernel_varmask | Compute a kernel matrix (covariance matrix)
kernel_zerosum | Compute a kernel matrix (covariance matrix)

## -- L --

lgp | Main function of the 'lgpr' package
lgpexpr | An S4 class to represent an lgp expression
lgpexpr-class | An S4 class to represent an lgp expression
lgpfit | An S4 class to represent the output of the 'lgp' function
lgpfit-class | An S4 class to represent the output of the 'lgp' function
lgpformula | An S4 class to represent an lgp formula
lgpformula-class | An S4 class to represent an lgp formula
lgpmodel | An S4 class to represent an additive GP model
lgpmodel-class | An S4 class to represent an additive GP model
lgpr | The 'lgpr' package.
lgprhs | An S4 class to represent the right-hand side of an lgp formula
lgprhs-class | An S4 class to represent the right-hand side of an lgp formula
lgpscaling | An S4 class to represent variable scaling
lgpscaling-class | An S4 class to represent variable scaling
lgpsim | An S4 class to represent a data set simulated using the additive GP formalism
lgpsim-class | An S4 class to represent a data set simulated using the additive GP formalism
lgpterm | An S4 class to represent one formula term
lgpterm-class | An S4 class to represent one formula term
log_normal | Prior definitions
log_normal, | Prior definitions

## -- M --

model_summary | Print a model summary.

## -- N --

new_x | Create test input points for prediction
normal | Prior definitions
normal, | Prior definitions
num_components | S4 generics for lgpfit, lgpmodel, and other objects
num_components-method | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
num_components-method | An S4 class to represent input for kernel matrix computations
num_components-method | An S4 class to represent prior or posterior draws from an additive function distribution.
num_components-method | An S4 class to represent the output of the 'lgp' function
num_components-method | An S4 class to represent an additive GP model
num_evalpoints | S4 generics for lgpfit, lgpmodel, and other objects
num_evalpoints-method | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
num_evalpoints-method | An S4 class to represent input for kernel matrix computations
num_evalpoints-method | An S4 class to represent prior or posterior draws from an additive function distribution.
num_paramsets | S4 generics for lgpfit, lgpmodel, and other objects
num_paramsets-method | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
num_paramsets-method | An S4 class to represent input for kernel matrix computations
num_paramsets-method | An S4 class to represent prior or posterior draws from an additive function distribution.

## -- O --

operations | Operations on formula terms and expressions
optimize_model | Fitting a model

## -- P --

parameter_info | S4 generics for lgpfit, lgpmodel, and other objects
parameter_info-method | An S4 class to represent an additive GP model
param_summary | Print a model summary.
plot-method | An S4 class to represent the output of the 'lgp' function
plot-method | An S4 class to represent a data set simulated using the additive GP formalism
plot_api_c | Plot a generated/fit model component
plot_api_g | Plot longitudinal data and/or model fit so that each subject/group has their own panel
plot_beta | Visualize the distribution of parameter draws
plot_components | Visualize all model components
plot_data | Vizualizing longitudinal data
plot_draws | Visualize the distribution of parameter draws
plot_effect_times | Visualize the distribution of parameter draws
plot_f | Visualizing model predictions or inferred covariate effects
plot_inputwarp | Visualize input warping function with several steepness parameter values
plot_invgamma | Plot the inverse gamma-distribution pdf
plot_pred | Visualizing model predictions or inferred covariate effects
plot_sim | Visualize an lgpsim object (simulated data)
plot_warp | Visualize the distribution of parameter draws
postproc | S4 generics for lgpfit, lgpmodel, and other objects
postproc-method | An S4 class to represent the output of the 'lgp' function
ppc | Graphical posterior predictive checks
pred | Posterior predictions and function posteriors
Prediction | An S4 class to represent prior or posterior draws from an additive function distribution.
Prediction-class | An S4 class to represent prior or posterior draws from an additive function distribution.
priors | Prior definitions
prior_pred | Prior (predictive) sampling
prior_to_num | Convert given prior to numeric format

## -- Q --

qinvgamma_stanlike | Density and quantile functions of the inverse gamma distribution

## -- R --

read_proteomics_data | Function for reading the built-in proteomics data
relevances | Assess component relevances

## -- S --

s4_generics | S4 generics for lgpfit, lgpmodel, and other objects
sample_model | Fitting a model
sample_param_prior | Prior (predictive) sampling
select | Select relevant components
select.integrate | Select relevant components
select_freq | Select relevant components
select_freq.integrate | Select relevant components
show | Printing formula object info using the show generic
show-method | An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
show-method | An S4 class to represent input for kernel matrix computations
show-method | An S4 class to represent prior or posterior draws from an additive function distribution.
show-method | An S4 class to represent the output of the 'lgp' function
show-method | An S4 class to represent an additive GP model
show-method | An S4 class to represent a data set simulated using the additive GP formalism
show-method | Printing formula object info using the show generic
sim.create_f | Simulate latent function components for longitudinal data analysis
sim.create_x | Create an input data frame X for simulated data
sim.create_y | Simulate noisy observations
sim.kernels | Compute all kernel matrices when simulating data
simulate_data | Generate an artificial longitudinal data set
split | Split data into training and test sets
split_by_factor | Split data into training and test sets
split_data | Split data into training and test sets
split_random | Split data into training and test sets
split_within_factor | Split data into training and test sets
split_within_factor_random | Split data into training and test sets
student_t | Prior definitions
student_t, | Prior definitions

## -- T --

testdata_001 | A very small artificial test data, used mostly for unit tests
testdata_002 | Medium-size artificial test data, used mostly for tutorials

## -- U --

uniform | Prior definitions
uniform, | Prior definitions

## -- V --

validate | Validate S4 class objects
validate_GaussianPrediction | Validate S4 class objects
validate_lgpexpr | Validate S4 class objects
validate_lgpfit | Validate S4 class objects
validate_lgpformula | Validate S4 class objects
validate_lgpscaling | Validate S4 class objects
validate_Prediction | Validate S4 class objects
var_mask | Variance masking function

## -- W --

warp_input | Input warping function

## -- misc --

*-method | Operations on formula terms and expressions
+-method | Operations on formula terms and expressions