Cyclops v2.0.2
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Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis
This model fitting tool incorporates cyclic coordinate descent and
majorization-minimization approaches to fit a variety of regression models
found in large-scale observational healthcare data. Implementations focus
on computational optimization and fine-scale parallelization to yield
efficient inference in massive datasets. Please see:
Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.
Readme
Cyclops
Cyclops is part of the OHDSI Methods Library.
Introduction
Cyclops (Cyclic coordinate descent for logistic, Poisson and survival analysis) is an R package for performing large scale regularized regressions.
Features
- Regression of very large problems: up to millions of observations, millions of variables
- Supports (conditional) logistic regression, (conditional) Poisson regression, as well as (conditional) Cox regression
- Uses a sparse representation of the independent variables when appropriate
- Supports using no prior, a normal prior or a Laplace prior
- Supports automatic selection of hyperparameter through cross-validation
- Efficient estimation of confidence intervals for a single variable using a profile-likelihood for that variable
Examples
library(Cyclops)
cyclopsData <- createCyclopsDataFrame(formula)
cyclopsFit <- fitCyclopsModel(cyclopsData)
Technology
Cyclops in an R package, with most functionality implemented in C++. Cyclops uses cyclic coordinate descent to optimize the likelihood function, which makes use of the sparse nature of the data.
System Requirements
Requires R (version 3.1.0 or higher). Compilation on Windows requires RTools >= 3.4.
Installation
In R, to install the latest stable version, install from CRAN:
install.packages("Cyclops")
To install the latest development version, install from GitHub. Note that this will require RTools to be installed.
install.packages("devtools")
devtools::install_github("ohdsi/Cyclops")
User Documentation
- Package manual: Cyclops manual
Support
- Developer questions/comments/feedback: OHDSI Forum
- We use the GitHub issue tracker for all bugs/issues/enhancements
License
Cyclops is licensed under Apache License 2.0. Cyclops contains the TinyThread libray.
The TinyThread library is licensed under the zlib/libpng license as described here.
Development
Cyclops is being developed in R Studio.
Development status
Beta
Acknowledgements
- This project is supported in part through the National Science Foundation grants IIS 1251151 and DMS 1264153.
Functions in Cyclops
| Name | Description | |
| fitCyclopsSimulation | Fit simulated data | |
| finalizeSqlCyclopsData | finalizeSqlCyclopsData | |
| fitCyclopsModel | Fit a Cyclops model | |
| getFloatingPointSize | Get floating point size | |
| isSorted | Check if data are sorted by one or more columns | |
| survfit.cyclopsFit | Calculate baseline hazard function | |
| isInitialized | Check if a Cyclops data object is initialized | |
| vcov.cyclopsFit | Calculate variance-covariance matrix for a fitted Cyclops model object | |
| getCyclopsPredictiveLogLikelihood | Compute predictive log-likelihood from a Cyclops model fit | |
| convertToGlmnetLambda | Convert to glmnet regularization parameter | |
| getUnivariableCorrelation | Get univariable correlation | |
| getUnivariableSeparability | Get univariable linear separability | |
| getCovariateTypes | Get covariate types | |
| createParameterizedPrior | Create a Cyclops parameterized prior object | |
| createPrior | Create a Cyclops prior object | |
| cyclops | Cyclops: Cyclic coordinate descent for logistic, Poisson and survival analysis | |
| createNonSeparablePrior | Create a Cyclops prior object that returns the MLE of non-separable coefficients | |
| getCrossValidationInfo | Get cross-validation information from a Cyclops model fit | |
| printCyclopsRowIds | Print row identifiers | |
| createControl | Create a Cyclops control object | |
| getHyperParameter | Get hyperparameter | |
| getNumberOfCovariates | Get total number of covariates | |
| mse | Mean squared error | |
| createCyclopsData | Create a Cyclops data object | |
| getNumberOfRows | Get total number of rows | |
| logLik.cyclopsFit | Extract log-likelihood | |
| oxford | Oxford self-controlled case series data | |
| meanLinearPredictor | Calculates xbar*beta | |
| getNumberOfStrata | Get number of strata | |
| simulateCyclopsData | Simulation Cyclops dataset | |
| printMatrixMarket | Print Cyclops data matrix to file | |
| plotCyclopsSimulationFit | Plot Cyclops simulation model fit | |
| summary.cyclopsData | Cyclops data object summary | |
| getCovariateIds | Get covariate identifiers | |
| predict.cyclopsFit | Model predictions | |
| readCyclopsData | Read Cyclops data from file | |
| reduce | Apply simple data reductions | |
| getNumberOfTypes | Get total number of outcome types | |
| getSEs | Extract standard errors | |
| print.cyclopsData | Print a Cyclops data object | |
| print.cyclopsFit | Print a Cyclops model fit object | |
| convertToCyclopsVariance | Convert to Cyclops Prior Variance | |
| coverage | Coverage | |
| createAutoGridCrossValidationControl | Create a Cyclops control object that supports multiple hyperparameters | |
| Multitype | Create a multitype outcome object | |
| aconfint | Asymptotic confidence intervals for a fitted Cyclops model object | |
| coef.cyclopsFit | Extract model coefficients | |
| appendSqlCyclopsData | appendSqlCyclopsData | |
| confint.cyclopsFit | Confidence intervals for Cyclops model parameters | |
| convertToCyclopsData | Convert data from two data frames or ffdf objects into a CyclopsData object | |
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Details
| Type | Package |
| License | Apache License 2.0 |
| LazyData | Yes |
| URL | https://github.com/ohdsi/cyclops |
| BugReports | https://github.com/ohdsi/cyclops/issues |
| LinkingTo | Rcpp, BH (>= 1.51.0), RcppEigen (>= 0.3.2) |
| RoxygenNote | 6.1.1 |
| NeedsCompilation | yes |
| Packaged | 2019-03-17 14:13:13 UTC; msuchard |
| Repository | CRAN |
| Date/Publication | 2019-03-17 14:53:23 UTC |
| linkingto | BH (>= 1.51.0) , RcppEigen (>= 0.3.2) |
| imports | bit , ff , ffbase , MASS , Matrix , methods , Rcpp (>= 0.12.12) , survival |
| suggests | ggplot2 , gnm , microbenchmark , testthat |
| depends | R (>= 3.1.0) |
| Contributors | R Development Core Team, Marcus Geelnard, Martijn J. Schuemie, Trevor R. Shaddox, Yuxi Tian, Sushil Mittal, Observational Health Data Sciences and Informatics , Rutgers University , Jianxiao Yang |
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