Function reference • lgpr

Public API Functions that are exported from the package namespace.
`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
`uniform()` `normal()` `student_t()` `gam()` `igam()` `log_normal()` `bet()`	Prior definitions
`create_model()`	Create a model
`draw_pred()`	Draw pseudo-observations from posterior or prior predictive distribution
`fit_summary()`	Print a fit summary.
`get_draws()`	Extract parameter draws from lgpfit or stanfit
`get_pred()`	Extract model predictions and function posteriors
`lgp()`	Main function of the 'lgpr' package
`model_summary()` `param_summary()`	Print a model summary.
`new_x()`	Create test input points for prediction
`sample_model()` `optimize_model()`	Fitting a model
`plot_draws()` `plot_beta()` `plot_warp()` `plot_effect_times()`	Visualize the distribution of parameter draws
`plot_components()`	Visualize all model components
`plot_data()`	Vizualizing longitudinal data
`plot_pred()` `plot_f()`	Visualizing model predictions or inferred covariate effects
`plot_sim()`	Visualize an lgpsim object (simulated data)
`ppc()`	Graphical posterior predictive checks
`pred()`	Posterior predictions and function posteriors
`prior_pred()` `sample_param_prior()`	Prior (predictive) sampling
`read_proteomics_data()`	Function for reading the built-in proteomics data
`relevances()`	Assess component relevances
`select()` `select_freq()` `select.integrate()` `select_freq.integrate()`	Select relevant components
`simulate_data()`	Generate an artificial longitudinal data set
`split_by_factor()` `split_within_factor()` `split_within_factor_random()` `split_random()` `split_data()`	Split data into training and test sets
Data
`testdata_001`	A very small artificial test data, used mostly for unit tests
`testdata_002`	Medium-size artificial test data, used mostly for tutorials
Full documentation
`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(<lgpexpr>)` `as.character(<lgpterm>)` `as.character(<lgpformula>)`	Character representations of different formula objects
`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_model()`	Create a model
`create_plot_df()`	Helper function for plots
`create_scaling()`	Create a standardizing transform
`dinvgamma_stanlike()` `qinvgamma_stanlike()`	Density and quantile functions of the inverse gamma distribution
`draw_pred()`	Draw pseudo-observations from posterior or prior predictive distribution
`example_fit()`	Quick way to create an example lgpfit, useful for debugging
`fit_summary()`	Print a fit summary.
`show(<GaussianPrediction>)` `component_names(<GaussianPrediction>)` `num_components(<GaussianPrediction>)` `num_paramsets(<GaussianPrediction>)` `num_evalpoints(<GaussianPrediction>)`	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_pred()`	Extract model predictions and function posteriors
`kernel_eq()` `kernel_ns()` `kernel_zerosum()` `kernel_bin()` `kernel_cat()` `kernel_varmask()` `kernel_beta()`	Compute a kernel matrix (covariance matrix)
`show(<KernelComputer>)` `num_components(<KernelComputer>)` `num_evalpoints(<KernelComputer>)` `num_paramsets(<KernelComputer>)` `component_names(<KernelComputer>)`	An S4 class to represent input for kernel matrix computations
`lgp()`	Main function of the 'lgpr' package
`lgpexpr-class` `lgpexpr`	An S4 class to represent an lgp expression
`show(<lgpfit>)` `component_names(<lgpfit>)` `num_components(<lgpfit>)` `postproc(<lgpfit>)` `contains_postproc(<lgpfit>)` `clear_postproc(<lgpfit>)` `get_model(<lgpfit>)` `get_stanfit(<lgpfit>)` `is_f_sampled(<lgpfit>)` `plot(<lgpfit>,<missing>)`	An S4 class to represent the output of the `lgp` function
`lgpformula-class` `lgpformula`	An S4 class to represent an lgp formula
`show(<lgpmodel>)` `parameter_info(<lgpmodel>)` `component_info(<lgpmodel>)` `num_components(<lgpmodel>)` `covariate_info(<lgpmodel>)` `component_names(<lgpmodel>)` `is_f_sampled(<lgpmodel>)`	An S4 class to represent an additive GP model
`lgpr-package` `lgpr`	The 'lgpr' package.
`lgprhs-class` `lgprhs`	An S4 class to represent the right-hand side of an lgp formula
`lgpscaling-class` `lgpscaling`	An S4 class to represent variable scaling
`show(<lgpsim>)` `plot(<lgpsim>,<missing>)`	An S4 class to represent a data set simulated using the additive GP formalism
`lgpterm-class` `lgpterm`	An S4 class to represent one formula term
`model_summary()` `param_summary()`	Print a model summary.
`new_x()`	Create test input points for prediction
`+`(<lgprhs>,<lgprhs>) `+`(<lgpterm>,<lgpterm>) `+`(<lgprhs>,<lgpterm>) `*`(<lgpterm>,<lgpterm>)	Operations on formula terms and expressions
`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_components()`	Visualize all model components
`plot_data()`	Vizualizing longitudinal data
`plot_draws()` `plot_beta()` `plot_warp()` `plot_effect_times()`	Visualize the distribution of parameter draws
`plot_inputwarp()`	Visualize input warping function with several steepness parameter values
`plot_invgamma()`	Plot the inverse gamma-distribution pdf
`plot_pred()` `plot_f()`	Visualizing model predictions or inferred covariate effects
`plot_sim()`	Visualize an lgpsim object (simulated data)
`ppc()`	Graphical posterior predictive checks
`pred()`	Posterior predictions and function posteriors
`show(<Prediction>)` `component_names(<Prediction>)` `num_components(<Prediction>)` `num_paramsets(<Prediction>)` `num_evalpoints(<Prediction>)`	An S4 class to represent prior or posterior draws from an additive function distribution.
`uniform()` `normal()` `student_t()` `gam()` `igam()` `log_normal()` `bet()`	Prior definitions
`prior_pred()` `sample_param_prior()`	Prior (predictive) sampling
`prior_to_num()`	Convert given prior to numeric format
`read_proteomics_data()`	Function for reading the built-in proteomics data
`relevances()`	Assess component relevances
`parameter_info()` `component_info()` `covariate_info()` `component_names()` `get_model()` `is_f_sampled()` `get_stanfit()` `postproc()` `contains_postproc()` `clear_postproc()` `num_paramsets()` `num_evalpoints()` `num_components()`	S4 generics for lgpfit, lgpmodel, and other objects
`sample_model()` `optimize_model()`	Fitting a model
`select()` `select_freq()` `select.integrate()` `select_freq.integrate()`	Select relevant components
`show(<lgpformula>)` `show(<lgprhs>)` `show(<lgpterm>)`	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_by_factor()` `split_within_factor()` `split_within_factor_random()` `split_random()` `split_data()`	Split data into training and test sets
`testdata_001`	A very small artificial test data, used mostly for unit tests
`testdata_002`	Medium-size artificial test data, used mostly for tutorials
`validate_lgpexpr()` `validate_lgpformula()` `validate_lgpscaling()` `validate_lgpfit()` `validate_GaussianPrediction()` `validate_Prediction()`	Validate S4 class objects
`var_mask()`	Variance masking function
`warp_input()`	Input warping function

Reference

Public API

Data

Full documentation