Man pages for jtimonen/lgpr
Longitudinal Gaussian Process Regression modeling and covariate selection

affectedSelect the affected individuals
assess_convergenceAssess convergence of the chains
check_dataValidate the 'data' input to 'lgp' and get covariate types
check_formulaValidate the model-related inputs of 'lgp'
check_hyperparameter_namesAn error message for wrong hyperparameter naming
check_unusedCheck for unused columns in the data
compute_K_betaCompute the multiplier matrix K_beta (to eneable...
compute_kernel_matricesEvaluate kernel matrices for each component
compute_predicted_componentsCompute component-wise predictions at test points
compute_predictionsCompute component-wise predictions at test points
compute_relevancesCovariate and component relevance calculations
create_data_plot_dfCreate a plotting data frame for ggplot
create_FSimulate latent function components for longitudinal data...
create_predictions_plot_df1Create a plotting data frame for ggplot
create_predictions_plot_df2Create a plotting data frame for ggplot
create_simdata_plot_dfCreate a plotting data frame for ggplot
create_stan_inputCreate input for Stan
create_test_pointsCreate a matrix of test points
create_XSimulate an input data frame X
create_X_starCreate X_star
create_yGenerate noisy observations
disease_peaksDraw disease component from a parameteric form
drawCategoricalIndepedently draw categorical variables for each individual
drawContinuousIndepedently draw continuous variables for each individual
drawLatentComponentsDraw realizations of multivariate normals
drawMeasurementTimesDraw the age covariate
get_case_row_mappingsCreate case ID to rows and back mappings
get_diseased_infoGet some variables related to diseased individuals
get_function_component_samplesGet values of sampled function components at data points
get_model_dimsSet a lot of generic variables that the Stan model needs as...
get_onset_infoGet disease onset info
get_predictedA helper function
get_prior_paramsGet prior parameters
get_prior_typeA dictionary from distribution names to integer encoding
get_responseGet the (scaled) response variable
get_runtimeGet average runtime of a chain
get_transform_typeA dictionary from transform names to integer encoding
hyperparam_estimateGet a posterior estimate of model (hyper)parameters
hyperparam_samplesGet a set of model (hyper)parameter samples
kernel_binCompute a binary kernel matrix
kernel_catCompute a categorical kernel matrix
kernel_nsCompute a nonstationary kernel matrix using input warping
kernel_seCompute a squared exponential kernel matrix
kernel_smoothingEstimate conditional mean time profile using gaussian kernel...
lgpThe main function of the 'lgpr' package
lgp_component_namesGet names of model components
lgp_covariate_namesGet names of model covariates
lgp_fitFit an lgp model
lgpfit-classAn S4 class to represent the output of the 'lgp_fit' function
lgp_modelCreate an lgp model
lgpmodel-classAn S4 class to represent an lgp model
lgp_predictCompute predictions for a fitted model
lgpr-packageThe 'lgpr' package.
likelihood_as_strConvert the Stan likelihood encoding to a string
matrix_to_dfMatrix to data frame without editing column names
nameComponentsCreate names for all components based on covariate names and...
onsetsToDiseaseAgeCompute the disease-related ages
parse_prior_distributionTurn a list describing a prior distribution into vectors to...
parse_prior_onsetTurn a list describing an onset prior distribution into...
plot_componentsVisualize the (average) inferred components evaluated at data...
plot_dataA spaghetti plot of longitudinal data.
plot_inputwarpVisualize the input warping function for different parameter...
plot_lgpfitVisualize a fitted 'lgpfit' object
plot_onsetVisualize posterior uncertainty in the disease onset
plot_predictionsPlot computed predictions componentwise
plot_relevancesBarplot of covariate relevances
plot_samplesVisualize the distribution of the model parameter samples
plot_simdataPlot simulated data for each individual separately
plot_simdata_by_componentPlot each component of a simulated longitudinal data set...
plot_simdata_by_individualPlot a simulated longitudinal data set for each individual...
postprocFinalize the lgpfit object after sampling
print_priorHuman-readable description of a specified prior
prior_defaultCreate the default prior
prior_LonGPCreate a similar default prior as in 'LonGP' (Cheng et. al,...
prior_stan_to_readableHuman-readable information about the priors in the Stan data...
prior_statementHuman-readable prior statement
prior_to_stanGet priors as a format that can be input to Stan
reorder_covariatesReorder design matrix
repvecRepeat a vector as a rows of an array
rtgeomSample from the 'truncated geometric' distribution
scaleRelevancesScale the effect sizes
separate_effectsSeparate the covariate effects from an interaction components...
show_lgpfitShow a summary of results of the 'lgp' function
show_lgpmodelShow a summary of an 'lgpmodel'
sim_check_covariatesInput check for the covariates-related arguments of...
simdata_colnames_prettySimulated data column names in a prettier form
sim_data_to_observedReal generated disease ages to observed ones
sim_generate_namesGenerate names for covariates
simulate_dataGenerate an artificial longitudinal data set
simulate_kernelsCompute all kernel matrices when simulating data
standardize_inputsStandardize continuous input variables in X
validate_priorValidate prior by sampling the signal and noise from it
varselCovariate selection
warp_inputWarp inputs
jtimonen/lgpr documentation built on April 17, 2019, 6:49 p.m.