Package: catalytic 0.1.0

Dongming Huang

catalytic: Tools for Applying Catalytic Priors in Statistical Modeling

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Authors:Yitong Wu [aut], Dongming Huang [aut, cre], Weihao Li [aut], Ministry of Education, Singapore [fnd]

catalytic_0.1.0.tar.gz
catalytic_0.1.0.tar.gz(r-4.5-noble)catalytic_0.1.0.tar.gz(r-4.4-noble)
catalytic_0.1.0.tgz(r-4.4-emscripten)catalytic_0.1.0.tgz(r-4.3-emscripten)
catalytic.pdf |catalytic.html✨
catalytic/json (API)
NEWS

# Install 'catalytic' in R:

install.packages('catalytic', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

swim - Simulated SWIM Dataset with Binary Response

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.18 score 209 downloads 15 exports 65 dependencies

Last updated 4 months agofrom:00e3494d40. Checks:3 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 18 2025
R-4.5-linux	OK	Mar 18 2025
R-4.4-linux	OK	Mar 18 2025

Exports:cat_cox cat_cox_bayes cat_cox_bayes_joint cat_cox_initialization cat_cox_tune cat_glm cat_glm_bayes cat_glm_bayes_joint cat_glm_bayes_joint_gibbs cat_glm_initialization cat_glm_tune cat_lmm cat_lmm_initialization cat_lmm_tune traceplot

Dependencies:abind backports BH boot callr checkmate cli coda colorspace desc distributional fansi farver generics ggplot2 glue gridExtra gtable inline invgamma isoband labeling lattice lifecycle lme4 loo magrittr MASS mathjaxr Matrix matrixStats mgcv minqa munsell nlme nloptr numDeriv pillar pkgbuild pkgconfig posterior processx ps quadform QuickJSR R6 rbibutils RColorBrewer Rcpp RcppEigen RcppParallel Rdpack reformulas rlang rstan scales StanHeaders survival tensorA tibble truncnorm utf8 vctrs viridisLite withr

catalytic_cox

Yitong Wu ywu039@e.ntu.edu.sg

Rendered fromcatalytic_cox.Rmdusingknitr::rmarkdownon Mar 18 2025.

Last update: 2024-12-18
Started: 2024-12-18

catalytic_glm_binomial

Yitong Wu ywu039@e.ntu.edu.sg

Rendered fromcatalytic_glm_binomial.Rmdusingknitr::rmarkdownon Mar 18 2025.

Last update: 2024-12-18
Started: 2024-12-18

catalytic_glm_gaussian

Yitong Wu ywu039@e.ntu.edu.sg

Rendered fromcatalytic_glm_gaussian.Rmdusingknitr::rmarkdownon Mar 18 2025.

Last update: 2024-12-18
Started: 2024-12-18

Citation

Wu Y, Huang D, Li W, Ministry of Education, Singapore (2024). catalytic: Tools for Applying Catalytic Priors in Statistical Modeling. https://CRAN.R-project.org/package=catalytic.

Corresponding BibTeX entry:

  @Manual{,
    title = {catalytic: Tools for Applying Catalytic Priors in
      Statistical Modeling},
    author = {Yitong Wu and Dongming Huang and Weihao Li and {Ministry
      of Education, Singapore}},
    year = {2024},
    url = {https://CRAN.R-project.org/package=catalytic},
  }

Readme and manuals

catalytic

The catalytic package enhances the stability and accuracy of Generalized Linear Models (GLMs) and Cox proportional hazards models (COX) in R, especially in high-dimensional or sparse data settings where traditional methods struggle. By employing catalytic prior distributions, this package integrates observed data with synthetic data generated from simpler models, effectively reducing issues like overfitting. This approach provides more robust parameter estimation, making catalytic a valuable tool for complex and limited-data scenarios.

Installation

You can install the released version of catalytic from CRAN with:

install.packages("catalytic")

## Installing package into '/private/var/folders/j5/bktz8wt94hv7m_wtcc0k6ymh0000gn/T/RtmpNJNjW2/temp_libpath107b87e07f100'
## (as 'lib' is unspecified)

## Warning: package 'catalytic' is not available for this version of R
## 
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages

library(catalytic)

Example

set.seed(1)

data <- data.frame(
  X1 = stats::rnorm(10),
  X2 = stats::rnorm(10),
  Y = stats::rnorm(10)
)

# Step 1: Initialization
cat_init <- cat_glm_initialization(
  formula = Y ~ 1, # Formula for a simple model to generate synthetic response
  family = gaussian,
  data = data,
  syn_size = 100 # Synthetic data size
)
print(cat_init)

## cat_glm_initialization
##  formula:               Y ~ 1
##  model:                 Unknown Variance
##  custom variance:       NULL
##  family:                gaussian [identity]
##  covariates dimention:  10 (Observation) + 100 (Synthetic) = 110 rows with 2 column(s)
## ------
## Observation Data Information:
##  covariates dimention:  10 rows with  2 column(s)
##  head(data) :              
##  [only show the head of first 10 columns] 
## 
##           X1          X2           Y
## 1 -0.6264538  1.51178117  0.91897737
## 2  0.1836433  0.38984324  0.78213630
## 3 -0.8356286 -0.62124058  0.07456498
## 4  1.5952808 -2.21469989 -1.98935170
## 5  0.3295078  1.12493092  0.61982575
## 6 -0.8204684 -0.04493361 -0.05612874
## 
## ------
## Synthetic Data Information:
##  covariates dimention:  100 rows with  2  column(s)
##  head(data):              
##  [only show the head of first 10 columns] 
## 
##           X1         X2          Y
## 1 -0.8356286  0.5953590 -0.1336732
## 2 -0.3053884  0.6855034 -0.1336732
## 3 -0.8204684  0.3898432 -0.1336732
## 4  0.7383247  0.5939013 -0.1336732
## 5  0.1836433  0.7523157 -0.1336732
## 6  0.1836433 -0.7461351 -0.1336732
## 
##  data generation process:
##  [only show the first 10 columns] 
## 
##                Covariate                   Type                Process
## 1 X1                     Continuous             Coordinate            
## 2 X2                     Continuous             Coordinate->Deskewing 
## 
## ------
## * For help interpreting the printed output see ?print.cat_initialization

# Step 2: Choose Method(s)
## Method 1 - Estimation with fixed tau
cat_glm_model <- cat_glm(
  formula = Y ~ ., # Formula for model fitting
  cat_init = cat_init, # Output object from `cat_glm_initialization`
  tau = 10 # Down-weight factor for synthetic data
)
print(cat_glm_model)

## cat_glm
##  formula:                Y ~ .
##  covariates dimention:   10 (Observation) + 100 (Synthetic) = 110 rows with 2 column(s)
##  tau:                    10
##  family:                 gaussian [identity]
## ------
## coefficients' information:
## (Intercept)          X1          X2 
##      -0.199      -0.402       0.250 
## 
## ------
## * For help interpreting the printed output see ?print.cat

print(predict(cat_glm_model))

##           1           2           3           4           5           6 
##  0.43141012 -0.17506574 -0.01774890 -1.39427971 -0.04996112  0.12024636 
##           7           8           9          10 
## -0.39882013 -0.25973457 -0.22499036  0.07272498

## Method 2 - Estimation with selective tau
cat_glm_tune_model <- cat_glm_tune(
  formula = Y ~ .,
  cat_init = cat_init,
  tau_seq = seq(1, 10) # Vector of values for downweighting synthetic data
)
print(cat_glm_tune_model)

## cat_glm_tune
##  formula:                 Y ~ .
##  covariates dimention:    10 (Observation) + 100 (Synthetic) = 110 rows with 2 column(s)
##  tau sequnce:             1, 2, 3, 4, 5, 6, 7, 8, 9, 10
##  family:                  gaussian
##  risk estimate method:    parametric_bootstrap
##  discrepancy method:      mean_square_error
## 
##  optimal tau:             10
##  minimun risk estimate:   1.539
## ------
## coefficients' information:
## 
## (Intercept)          X1          X2 
##      -0.199      -0.402       0.250 
## 
## ------
## * For help interpreting the printed output see ?print.cat_tune

print(predict(cat_glm_tune_model))

##           1           2           3           4           5           6 
##  0.43141012 -0.17506574 -0.01774890 -1.39427971 -0.04996112  0.12024636 
##           7           8           9          10 
## -0.39882013 -0.25973457 -0.22499036  0.07272498

plot(cat_glm_tune_model, text_pos = 2)

## Method 3 - Bayesian posterior sampling with fixed tau
cat_glm_bayes_model <- cat_glm_bayes(
  formula = Y ~ .,
  cat_init = cat_init,
  tau = 10
)

## 
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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print(cat_glm_bayes_model)

## cat_glm_bayes
##  formula:                Y ~ .
##  covariates dimention:   10 (Observation) + 100 (Synthetic) = 110 rows with 2 column(s)
##  tau:                    10
##  family:                 gaussian [identity]
##  stan algorithm:         NUTS
##  stan chain:             4
##  stan iter:              2000
##  stan warmup:            1000
## ------
## coefficients' information:
## 
##                mean se_mean    sd    2.5%     25%     50%     75%   97.5%
## (Intercept)  -0.196   0.003 0.156  -0.504  -0.298  -0.197  -0.093   0.118
## X1           -0.405   0.003 0.198  -0.813  -0.536  -0.405  -0.277  -0.017
## X2            0.247   0.003 0.153  -0.053   0.146   0.247   0.351   0.554
## sigma         0.648   0.002 0.103   0.483   0.575   0.636   0.703   0.888
## lp__        -18.429   0.039 1.550 -22.281 -19.198 -18.074 -17.319 -16.535
##                n_eff  Rhat
## (Intercept) 3547.056 1.003
## X1          3928.271 0.999
## X2          3583.399 1.001
## sigma       2780.931 1.000
## lp__        1547.193 1.000
## 
## ------
## * For help interpreting the printed output see ?print.cat_bayes

print(predict(cat_glm_bayes_model))

##  [1]  0.43217093 -0.17392884 -0.01089480 -1.39082785 -0.05115094  0.12558130
##  [7] -0.39757973 -0.26171856 -0.22616132  0.07484400

traceplot(cat_glm_bayes_model)

## Method 4 - Bayesian posterior sampling with adaptive tau
cat_glm_bayes_joint_model <- cat_glm_bayes_joint(
  formula = Y ~ .,
  cat_init = cat_init
)

## 
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print(cat_glm_bayes_joint_model)

## cat_glm_bayes_joint
##  formula:                Y ~ .
##  covariates dimention:   10 (Observation) + 100 (Synthetic) = 110 rows with 2 column(s)
##  family:                 gaussian [identity]
##  stan algorithm:         NUTS
##  stan chain:             4
##  stan iter:              2000
##  stan warmup:            1000
## ------
## coefficients' information:
## 
##                mean se_mean    sd    2.5%     25%     50%     75%   97.5%
## (Intercept)  -0.141   0.004 0.259  -0.658  -0.308  -0.141   0.028   0.385
## X1           -0.647   0.006 0.348  -1.331  -0.876  -0.647  -0.422   0.053
## X2            0.334   0.005 0.257  -0.179   0.171   0.335   0.497   0.864
## tau           0.963   0.011 0.684   0.122   0.470   0.798   1.310   2.623
## sigma         0.777   0.003 0.167   0.527   0.658   0.752   0.867   1.167
## lp__        -14.090   0.045 1.801 -18.409 -15.022 -13.716 -12.755 -11.682
##                n_eff  Rhat
## (Intercept) 3607.756 1.000
## X1          3361.235 1.000
## X2          3098.601 1.001
## tau         4149.713 1.001
## sigma       2964.667 1.001
## lp__        1588.695 1.002
## 
## ------
## * For help interpreting the printed output see ?print.cat_bayes

print(predict(cat_glm_bayes_joint_model))

##  [1]  0.7690212 -0.1300890  0.1918019 -1.9138229  0.0210847  0.3745221
##  [7] -0.4623462 -0.3040014 -0.2397665  0.2545836

traceplot(cat_glm_bayes_joint_model)

Help Manual

Help page	Topics
Catalytic Cox Proportional Hazards Model (COX) Fitting Function with Fixed Tau	cat_cox
Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with Fixed tau	cat_cox_bayes
Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with adaptive tau	cat_cox_bayes_joint
Initialization for Catalytic Cox proportional hazards model (COX)	cat_cox_initialization
Catalytic Cox Proportional-Hazards Model (COX) Fitting Function by Tuning tau from a Sequence of tau Values	cat_cox_tune
Catalytic Generalized Linear Models (GLMs) Fitting Function with Fixed Tau	cat_glm
Bayesian Estimation for Catalytic Generalized Linear Models (GLMs) with Fixed tau	cat_glm_bayes
Bayesian Estimation for Catalytic Generalized Linear Models (GLMs) with adaptive tau	cat_glm_bayes_joint
Bayesian Estimation with Gibbs Sampling for Catalytic Generalized Linear Models (GLMs) Binomial Family for Coefficients and tau	cat_glm_bayes_joint_gibbs
Initialization for Catalytic Generalized Linear Models (GLMs)	cat_glm_initialization
Catalytic Generalized Linear Models (GLMs) Fitting Function by Tuning tau from a Sequence of tau Values	cat_glm_tune
Catalytic Linear Mixed Model (LMM) Fitting Function with fixed tau	cat_lmm
Initialization for Catalytic Linear Mixed Model (LMM)	cat_lmm_initialization
Catalytic Linear Mixed Model (LMM) Fitting Function by Tuning tau from a Sequence of tau Values	cat_lmm_tune
Perform Cross-Validation for Model Estimation	cross_validation
Perform Cross-Validation for Catalytic Cox Proportional-Hazards Model (COX) to Select Optimal tau	cross_validation_cox
Perform Cross-Validation for Catalytic Linear Mixed Model (LMM) to Select Optimal tau	cross_validation_lmm
Extract and Format Model Coefficients	extract_coefs
Extract Dimension Information from Model Initialization	extract_dim
Extract and Format Summary of Stan Model Results	extract_stan_summary
Extract and Format Sequence of Tau Values	extract_tau_seq
Adjusted Cat Initialization	get_adjusted_cat_init
Compute the Gradient for Cox Proportional Hazards Model	get_cox_gradient
Compute the Hessian Matrix for Cox Proportional Hazards Model	get_cox_hessian
Estimate the kappa value for the synthetic Cox proportional hazards model	get_cox_kappa
Compute the Partial Likelihood for the Cox Proportional Hazards Model	get_cox_partial_likelihood
Solve Linear System using QR Decomposition	get_cox_qr_solve
Calculate Risk and Failure Sets for Cox Proportional Hazards Model	get_cox_risk_and_failure_sets
Identify the risk set indices for Cox proportional hazards model	get_cox_risk_set_idx
Compute the gradient of the synthetic Cox proportional hazards model	get_cox_syn_gradient
Compute the Synthetic Hessian Matrix for Cox Proportional Hazards Model	get_cox_syn_hessian
Compute Discrepancy Measures	get_discrepancy
Extract Left-Hand Side of Formula as String	get_formula_lhs
Extract the Right-Hand Side of a Formula	get_formula_rhs
Convert Formula to String	get_formula_string
Get Custom Variance for Generalized Linear Model (GLM)	get_glm_custom_var
Compute Diagonal Approximate Covariance Matrix	get_glm_diag_approx_cov
Retrieve GLM Family Name or Name with Link Function	get_glm_family_string
Compute Lambda Based on Discrepancy Method	get_glm_lambda
Compute Log Density Based on GLM Family	get_glm_log_density
Compute Gradient of Log Density for GLM Families	get_glm_log_density_grad
Compute Mean Based on GLM Family	get_glm_mean
Generate Sample Data for GLM	get_glm_sample_data
Run Hamiltonian Monte Carlo to Get MCMC Sample Result	get_hmc_mcmc_result
Compute Linear Predictor	get_linear_predictor
Resampling Methods for Data Processing	get_resampled_df
Generate Stan Model Based on Specified Parameters	get_stan_model
Standardize Data	get_standardized_data
Hamiltonian Monte Carlo (HMC) Implementation	hmc_neal_2010
Check if a Variable is Continuous	is.continuous
Perform Mallowian Estimate for Model Risk (Only Applicable for Gaussian Family)	mallowian_estimate
Perform Parametric Bootstrap for Model Risk Estimation	parametric_bootstrap
Plot Likelihood or Risk Estimate vs. Tau for Tuning Model	plot.cat_tune
Predict Linear Predictor for New Data Using a Fitted Cox Model	predict.cat_cox
Predict Outcome for New Data Using a Fitted GLM Model	predict.cat_glm
Predict Linear Predictor for New Data Using a Fitted Linear Mixed Model	predict.cat_lmm
Print Data Frame with Head and Tail Rows	print_df_head_tail
Generate Suggestions for Bayesian Joint Binomial GLM Parameter Estimation	print_glm_bayes_joint_binomial_suggestion
Print Method for 'cat' Object	print.cat
Print Summary of 'cat_bayes' Model	print.cat_bayes
Print Summary of 'cat_gibbs' Model	print.cat_gibbs
Print Summary for Catalytic Initialization Model	print.cat_initialization
Print Method for 'cat_tune' Object	print.cat_tune
Perform Steinian Estimate for Model Risk (Only Applicable for Binomial Family)	steinian_estimate
Simulated SWIM Dataset with Binary Response	swim
Traceplot for Bayesian Model Sampling	traceplot
Traceplot for Bayesian Sampling Model	traceplot.cat_bayes
Traceplot for Gibbs Sampling Model	traceplot.cat_gibbs
Calculates the log-likelihood for linear mixed models (LMMs) by combining observed and synthetic log-likelihoods based on the variance parameters.	update_lmm_variance
Validate Inputs for Catalytic Cox proportional hazards model (COX) Initialization	validate_cox_initialization_input
Validate Inputs for Catalytic Cox Model	validate_cox_input
Validate Inputs for Catalytic Generalized Linear Models (GLMs) Initialization	validate_glm_initialization_input
Validate Inputs for Catalytic Generalized Linear Models (GLMs)	validate_glm_input
Validate Inputs for Catalytic Linear Mixed Model (LMM) Initialization	validate_lmm_initialization_input
Validate Inputs for Catalytic Linear Mixed Model (LMM)	validate_lmm_input
Validate Positive or Non-negative Parameter	validate_positive

Package: catalytic 0.1.0

catalytic: Tools for Applying Catalytic Priors in Statistical Modeling

catalytic_cox

catalytic_glm_binomial

catalytic_glm_gaussian

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Readme and manuals

catalytic

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Example

Help Manual

Usage by other packages (reverse dependencies)