Longitudinal Gaussian Process Regression

Documentation for package ‘lgpr’ version 1.1.5

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

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 prediction points
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_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