Package 'Cyclops' reference manual

Title:	Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis
Description:	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>.
Authors:	Marc A. Suchard [aut, cre], Martijn J. Schuemie [aut], Trevor R. Shaddox [aut], Yuxi Tian [aut], Jianxiao Yang [aut], Eric Kawaguchi [aut], Sushil Mittal [ctb], Observational Health Data Sciences and Informatics [cph], Marcus Geelnard [cph, ctb] (provided the TinyThread library), Rutgers University [cph, ctb] (provided the HParSearch routine), R Development Core Team [cph, ctb] (provided the ZeroIn routine)
Maintainer:	Marc A. Suchard <[email protected]>
License:	Apache License 2.0
Version:	3.4.1
Built:	2024-10-02 05:59:44 UTC
Source:	https://github.com/ohdsi/cyclops

Extract model coefficients

Description

coef.cyclopsFit extracts model coefficients from an Cyclops model fit object

Usage

## S3 method for class 'cyclopsFit'
coef(object, rescale = FALSE, ignoreConvergence = FALSE, ...)
## S3 method for class 'cyclopsFit'
coef(object, rescale = FALSE, ignoreConvergence = FALSE, ...)

Arguments

`object`	Cyclops model fit object
`rescale`	Boolean: rescale coefficients for unnormalized covariate values
`ignoreConvergence`	Boolean: return coefficients even if fit object did not converge
`...`	Other arguments

Value

Named numeric vector of model coefficients.

Confidence intervals for Cyclops model parameters

Description

confinit.cyclopsFit profiles the data likelihood to construct confidence intervals of arbitrary level. Usually it only makes sense to do this for variables that have not been regularized.

Usage

## S3 method for class 'cyclopsFit'
confint(
  object,
  parm,
  level = 0.95,
  overrideNoRegularization = FALSE,
  includePenalty = TRUE,
  rescale = FALSE,
  ...
)
## S3 method for class 'cyclopsFit'
confint(
  object,
  parm,
  level = 0.95,
  overrideNoRegularization = FALSE,
  includePenalty = TRUE,
  rescale = FALSE,
  ...
)

Arguments

`object`	A fitted Cyclops model object
`parm`	A specification of which parameters require confidence intervals, either a vector of numbers of covariateId names
`level`	Numeric: confidence level required
`overrideNoRegularization`	Logical: Enable confidence interval estimation for regularized parameters
`includePenalty`	Logical: Include regularized covariate penalty in profile
`rescale`	Boolean: rescale coefficients for unnormalized covariate values
`...`	Additional argument(s) for methods

Value

A matrix with columns reporting lower and upper confidence limits for each parameter. These columns are labelled as (1-level) / 2 and 1 - (1 - level) / 2 in percent (by default 2.5 percent and 97.5 percent)

Examples

#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))

Convert data from two data frames or ffdf objects into a CyclopsData object

Description

convertToCyclopsData loads data from two data frames or ffdf objects, and inserts it into a Cyclops data object.

Usage

convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  timeEffectMap = NULL,
  addIntercept = TRUE,
  checkSorting = NULL,
  checkRowIds = TRUE,
  normalize = NULL,
  quiet = FALSE,
  floatingPoint = 64
)

## S3 method for class 'data.frame'
convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  timeEffectMap = NULL,
  addIntercept = TRUE,
  checkSorting = NULL,
  checkRowIds = TRUE,
  normalize = NULL,
  quiet = FALSE,
  floatingPoint = 64
)

## S3 method for class 'tbl_dbi'
convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  timeEffectMap = NULL,
  addIntercept = TRUE,
  checkSorting = NULL,
  checkRowIds = TRUE,
  normalize = NULL,
  quiet = FALSE,
  floatingPoint = 64
)
convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  timeEffectMap = NULL,
  addIntercept = TRUE,
  checkSorting = NULL,
  checkRowIds = TRUE,
  normalize = NULL,
  quiet = FALSE,
  floatingPoint = 64
)

## S3 method for class 'data.frame'
convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  timeEffectMap = NULL,
  addIntercept = TRUE,
  checkSorting = NULL,
  checkRowIds = TRUE,
  normalize = NULL,
  quiet = FALSE,
  floatingPoint = 64
)

## S3 method for class 'tbl_dbi'
convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  timeEffectMap = NULL,
  addIntercept = TRUE,
  checkSorting = NULL,
  checkRowIds = TRUE,
  normalize = NULL,
  quiet = FALSE,
  floatingPoint = 64
)

Arguments

`outcomes`	A data frame or ffdf object containing the outcomes with predefined columns (see below).
`covariates`	A data frame or ffdf object containing the covariates with predefined columns (see below).
`modelType`	Cyclops model type. Current supported types are "pr", "cpr", lr", "clr", or "cox"
`timeEffectMap`	A data frame or ffdf object containing the convariates that have time-varying effects on the outcome
`addIntercept`	Add an intercept to the model?
`checkSorting`	(DEPRECATED) Check if the data are sorted appropriately, and if not, sort.
`checkRowIds`	Check if all rowIds in the covariates appear in the outcomes.
`normalize`	String: Name of normalization for all non-indicator covariates (possible values: stdev, max, median)
`quiet`	If true, (warning) messages are suppressed.
`floatingPoint`	Specified floating-point representation size (32 or 64)

Details

These columns are expected in the outcome object:

`⁠stratumId⁠`	(integer)	(optional) Stratum ID for conditional regression models
`⁠rowId⁠`	(integer)	Row ID is used to link multiple covariates (x) to a single outcome (y)
`⁠y⁠`	(real)	The outcome variable
`⁠time⁠`	(real)	For models that use time (e.g. Poisson or Cox regression) this contains time
		(e.g. number of days)
`⁠weights⁠`	(real)	(optional) Non-negative weights to apply to outcome
`⁠censorWeights⁠`	(real)	(optional) Non-negative censoring weights for competing risk model; will be computed if not provided.

These columns are expected in the covariates object:

`⁠stratumId⁠`	(integer)	(optional) Stratum ID for conditional regression models
`⁠rowId⁠`	(integer)	Row ID is used to link multiple covariates (x) to a single outcome (y)
`⁠covariateId⁠`	(integer)	A numeric identifier of a covariate
`⁠covariateValue⁠`	(real)	The value of the specified covariate

These columns are expected in the timeEffectMap object:

`⁠covariateId⁠`	(integer)	A numeric identifier of the covariates that have time-varying effects on the outcome

Value

An object of type cyclopsData

Methods (by class)

convertToCyclopsData(data.frame): Convert data from two data.frame
convertToCyclopsData(tbl_dbi): Convert data from two Andromeda tables

Examples

#Convert infert dataset to Cyclops format:
covariates <- data.frame(stratumId = rep(infert$stratum, 2),
                         rowId = rep(1:nrow(infert), 2),
                         covariateId = rep(1:2, each = nrow(infert)),
                         covariateValue = c(infert$spontaneous, infert$induced))
outcomes <- data.frame(stratumId = infert$stratum,
                       rowId = 1:nrow(infert),
                       y = infert$case)
#Make sparse:
covariates <- covariates[covariates$covariateValue != 0, ]

#Create Cyclops data object:
cyclopsData <- convertToCyclopsData(outcomes, covariates, modelType = "clr",
                                    addIntercept = FALSE)

#Fit model:
fit <- fitCyclopsModel(cyclopsData, prior = createPrior("none"))

#Convert infert dataset to Cyclops format:
covariates <- data.frame(stratumId = rep(infert$stratum, 2),
                         rowId = rep(1:nrow(infert), 2),
                         covariateId = rep(1:2, each = nrow(infert)),
                         covariateValue = c(infert$spontaneous, infert$induced))
outcomes <- data.frame(stratumId = infert$stratum,
                       rowId = 1:nrow(infert),
                       y = infert$case)
#Make sparse:
covariates <- covariates[covariates$covariateValue != 0, ]

#Create Cyclops data object:
cyclopsData <- convertToCyclopsData(outcomes, covariates, modelType = "clr",
                                    addIntercept = FALSE)

#Fit model:
fit <- fitCyclopsModel(cyclopsData, prior = createPrior("none"))

Convert short sparse covariate table to long sparse covariate table for time-varying coefficients.

Description

convertToTimeVaryingCoef convert short sparse covariate table to long sparse covariate table for time-varying coefficients.

Usage

convertToTimeVaryingCoef(shortCov, longOut, timeVaryCoefId)
convertToTimeVaryingCoef(shortCov, longOut, timeVaryCoefId)

Arguments

`shortCov`	A data frame containing the covariate with predefined columns (see below).
`longOut`	A data frame containing the outcomes with predefined columns (see below), output of `splitTime`.
`timeVaryCoefId`	Integer: A numeric identifier of a time-varying coefficient

Details

These columns are expected in the shortCov object:

`⁠rowId⁠`	(integer)	Row ID is used to link multiple covariates (x) to a single outcome (y)
`⁠covariateId⁠`	(integer)	A numeric identifier of a covariate
`⁠covariateValue⁠`	(real)	The value of the specified covariate

These columns are expected in the longOut object:

`⁠stratumId⁠`	(integer)	Stratum ID for time-varying models
`⁠subjectId⁠`	(integer)	Subject ID is used to link multiple covariates (x) at different time intervals to a single subject
`⁠rowId⁠`	(integer)	Row ID is used to link multiple covariates (x) to a single outcome (y)
`⁠y⁠`	(real)	The outcome variable
`⁠time⁠`	(real)	For models that use time (e.g. Poisson or Cox regression) this contains time
		(e.g. number of days)

Value

A long sparse covariate table for time-varying coefficients.

Coverage

Description

coverage computes the coverage on confidence intervals

Usage

coverage(goldStandard, lowerBounds, upperBounds)
coverage(goldStandard, lowerBounds, upperBounds)

Arguments

`goldStandard`	Numeric vector
`lowerBounds`	Numeric vector. Lower bound of the confidence intervals
`upperBounds`	Numeric vector. Upper bound of the confidence intervals

Value

The proportion of times goldStandard falls between lowerBound and upperBound

Create a Cyclops control object that supports multiple hyperparameters

Description

createCrossValidationControl creates a Cyclops control object for use with fitCyclopsModel that supports multiple hyperparameters through an auto-search in one dimension and a grid-search over the remaining dimensions

Usage

createAutoGridCrossValidationControl(
  outerGrid,
  autoPosition = 1,
  refitAtMaximum = TRUE,
  cvType = "auto",
  initialValue = 1,
  ...
)
createAutoGridCrossValidationControl(
  outerGrid,
  autoPosition = 1,
  refitAtMaximum = TRUE,
  cvType = "auto",
  initialValue = 1,
  ...
)

Arguments

`outerGrid`	List or data.frame of grid parameters to explore
`autoPosition`	Vector position for auto-search parameter (concatenated into outerGrid)
`refitAtMaximum`	Logical: re-fit Cyclops object at maximal cross-validation parameters
`cvType`	Must equal "auto"
`initialValue`	Initial value for auto-search parameter
`...`	Additional parameters passed through to `createControl`

Value

A Cyclops prior object of class inheriting from "cyclopsPrior" and "cyclopsFunctionalPrior" for use with fitCyclopsModel.

Create a Cyclops control object

Description

createControl creates a Cyclops control object for use with fitCyclopsModel.

Usage

createControl(
  maxIterations = 1000,
  tolerance = 1e-06,
  convergenceType = "gradient",
  cvType = "auto",
  fold = 10,
  lowerLimit = 0.01,
  upperLimit = 20,
  gridSteps = 10,
  cvRepetitions = 1,
  minCVData = 100,
  noiseLevel = "silent",
  threads = 1,
  seed = NULL,
  resetCoefficients = FALSE,
  startingVariance = -1,
  useKKTSwindle = FALSE,
  tuneSwindle = 10,
  selectorType = "auto",
  initialBound = 2,
  maxBoundCount = 5,
  algorithm = "ccd",
  doItAll = TRUE,
  syncCV = FALSE
)
createControl(
  maxIterations = 1000,
  tolerance = 1e-06,
  convergenceType = "gradient",
  cvType = "auto",
  fold = 10,
  lowerLimit = 0.01,
  upperLimit = 20,
  gridSteps = 10,
  cvRepetitions = 1,
  minCVData = 100,
  noiseLevel = "silent",
  threads = 1,
  seed = NULL,
  resetCoefficients = FALSE,
  startingVariance = -1,
  useKKTSwindle = FALSE,
  tuneSwindle = 10,
  selectorType = "auto",
  initialBound = 2,
  maxBoundCount = 5,
  algorithm = "ccd",
  doItAll = TRUE,
  syncCV = FALSE
)

Arguments

`maxIterations`	Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error
`tolerance`	Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence
`convergenceType`	String: name of convergence criterion to employ (described in more detail below)
`cvType`	String: name of cross validation search. Option `"auto"` selects an auto-search following BBR. Option `"grid"` selects a grid-search cross validation
`fold`	Numeric: Number of random folds to employ in cross validation
`lowerLimit`	Numeric: Lower prior variance limit for grid-search
`upperLimit`	Numeric: Upper prior variance limit for grid-search
`gridSteps`	Numeric: Number of steps in grid-search
`cvRepetitions`	Numeric: Number of repetitions of X-fold cross validation
`minCVData`	Numeric: Minimum number of data for cross validation
`noiseLevel`	String: level of Cyclops screen output (`"silent"`, `"quiet"`, `"noisy"`)
`threads`	Numeric: Specify number of CPU threads to employ in cross-validation; default = 1 (auto = -1)
`seed`	Numeric: Specify random number generator seed. A null value sets seed via `Sys.time`.
`resetCoefficients`	Logical: Reset all coefficients to 0 between model fits under cross-validation
`startingVariance`	Numeric: Starting variance for auto-search cross-validation; default = -1 (use estimate based on data)
`useKKTSwindle`	Logical: Use the Karush-Kuhn-Tucker conditions to limit search
`tuneSwindle`	Numeric: Size multiplier for active set
`selectorType`	String: name of exchangeable sampling unit. Option `"byPid"` selects entire strata. Option `"byRow"` selects single rows. If set to `"auto"`, `"byRow"` will be used for all models except conditional models where the average number of rows per stratum is smaller than the number of strata.
`initialBound`	Numeric: Starting trust-region size
`maxBoundCount`	Numeric: Maximum number of tries to decrease initial trust-region size
`algorithm`	String: name of fitting algorithm to employ; default is `ccd`
`doItAll`	Currently unused
`syncCV`	Currently unused Todo: Describe convegence types

Value

A Cyclops control object of class inheriting from "cyclopsControl" for use with fitCyclopsModel.

Examples

#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))

Create a Cyclops data object

Description

createCyclopsData creates a Cyclops data object from an R formula or data matrices.

Usage

createCyclopsData(
  formula,
  sparseFormula,
  indicatorFormula,
  modelType,
  data,
  subset = NULL,
  weights = NULL,
  censorWeights = NULL,
  offset = NULL,
  time = NULL,
  pid = NULL,
  y = NULL,
  type = NULL,
  dx = NULL,
  sx = NULL,
  ix = NULL,
  model = FALSE,
  normalize = NULL,
  floatingPoint = 64,
  method = "cyclops.fit"
)
createCyclopsData(
  formula,
  sparseFormula,
  indicatorFormula,
  modelType,
  data,
  subset = NULL,
  weights = NULL,
  censorWeights = NULL,
  offset = NULL,
  time = NULL,
  pid = NULL,
  y = NULL,
  type = NULL,
  dx = NULL,
  sx = NULL,
  ix = NULL,
  model = FALSE,
  normalize = NULL,
  floatingPoint = 64,
  method = "cyclops.fit"
)

Arguments

`formula`	An object of class `"formula"` that provides a symbolic description of the numerically dense model response and terms.
`sparseFormula`	An object of class `"formula"` that provides a symbolic description of numerically sparse model terms.
`indicatorFormula`	An object of class `"formula"` that provides a symbolic description of {0,1} model terms.
`modelType`	character string: Valid types are listed below.
`data`	An optional data frame, list or environment containing the variables in the model.
`subset`	Currently unused
`weights`	Currently unused
`censorWeights`	Vector of subject-specific censoring weights (between 0 and 1). Currently only supported in `modelType = "fgr"`.
`offset`	Currently unused
`time`	Currently undocumented
`pid`	Optional vector of integer stratum identifiers. If supplied, all rows must be sorted by increasing identifiers
`y`	Currently undocumented
`type`	Currently undocumented
`dx`	Optional dense `"Matrix"` of covariates
`sx`	Optional sparse `"Matrix"` of covariates
`ix`	Optional {0,1} `"Matrix"` of covariates
`model`	Currently undocumented
`normalize`	String: Name of normalization for all non-indicator covariates (possible values: stdev, max, median)
`floatingPoint`	Integer: Floating-point representation size (32 or 64)
`method`	Currently undocumented

Details

This function creates a Cyclops model data object from R "formula" or directly from numeric vectors and matrices to define the model response and covariates. If specifying a model using a "formula", then the left-hand side define the model response and the right-hand side defines dense covariate terms. Objects provided with "sparseFormula" and "indicatorFormula" must be include left-hand side responses and terms are coersed into sparse and indicator representations for computational efficiency.

Items to discuss:

Only use formula or (y,dx,...)
stratum() in formula
offset() in formula
when "stratum" (renamed from pid) are necessary
when "time" are necessary

Value

A list that contains a Cyclops model data object pointer and an operation duration

Models

Currently supported model types are:

`⁠ "ls"⁠`	Least squares
`⁠ "pr"⁠`	Poisson regression
`⁠ "lr"⁠`	Logistic regression
`⁠ "clr"⁠`	Conditional logistic regression
`⁠ "cpr"⁠`	Conditional Poisson regression
`⁠ "sccs"⁠`	Self-controlled case series
`⁠ "cox"⁠`	Cox proportional hazards regression
`⁠ "fgr"⁠`	Fine-Gray proportional subdistribution hazards regression

Examples

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
cyclopsData <- createCyclopsData(
     counts ~ outcome + treatment,
     modelType = "pr")
cyclopsFit <- fitCyclopsModel(cyclopsData)

cyclopsData2 <- createCyclopsData(
     counts ~ outcome,
     indicatorFormula = ~ treatment,
     modelType = "pr")
summary(cyclopsData2)
cyclopsFit2 <- fitCyclopsModel(cyclopsData2)

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
cyclopsData <- createCyclopsData(
     counts ~ outcome + treatment,
     modelType = "pr")
cyclopsFit <- fitCyclopsModel(cyclopsData)

cyclopsData2 <- createCyclopsData(
     counts ~ outcome,
     indicatorFormula = ~ treatment,
     modelType = "pr")
summary(cyclopsData2)
cyclopsFit2 <- fitCyclopsModel(cyclopsData2)

Create a Cyclops prior object that returns the MLE of non-separable coefficients

Description

createNonSeparablePrior creates a Cyclops prior object for use with fitCyclopsModel.

Usage

createNonSeparablePrior(maxIterations = 10, ...)
createNonSeparablePrior(maxIterations = 10, ...)

Arguments

`maxIterations`	Numeric: maximum iterations to achieve convergence
`...`	Additional argument(s) for `fitCyclopsModel`

Value

A Cyclops prior object of class inheriting from "cyclopsPrior" for use with fitCyclopsModel.

Examples

prior <- createNonSeparablePrior()

prior <- createNonSeparablePrior()

Create a Cyclops parameterized prior object

Description

createParameterizedPrior creates a Cyclops prior object for use with fitCyclopsModel in which arbitrary R functions parameterize the prior location and variance.

Usage

createParameterizedPrior(
  priorType,
  parameterize,
  values,
  useCrossValidation = FALSE,
  forceIntercept = FALSE
)
createParameterizedPrior(
  priorType,
  parameterize,
  values,
  useCrossValidation = FALSE,
  forceIntercept = FALSE
)

Arguments

`priorType`	Character vector: specifies prior distribution. See below for options
`parameterize`	Function list: parameterizes location and variance
`values`	Numeric vector: initial parameter values
`useCrossValidation`	Logical: Perform cross-validation to determine `parameters`.
`forceIntercept`	Logical: Force intercept coefficient into prior

Value

A Cyclops prior object of class inheriting from "cyclopsPrior" and "cyclopsFunctionalPrior" for use with fitCyclopsModel.

Create a Cyclops prior object

Description

createPrior creates a Cyclops prior object for use with fitCyclopsModel.

Usage

createPrior(
  priorType,
  variance = 1,
  exclude = c(),
  graph = NULL,
  neighborhood = NULL,
  useCrossValidation = FALSE,
  forceIntercept = FALSE
)
createPrior(
  priorType,
  variance = 1,
  exclude = c(),
  graph = NULL,
  neighborhood = NULL,
  useCrossValidation = FALSE,
  forceIntercept = FALSE
)

Arguments

`priorType`	Character: specifies prior distribution. See below for options
`variance`	Numeric: prior distribution variance
`exclude`	A vector of numbers or covariateId names to exclude from prior
`graph`	Child-to-parent mapping for a hierarchical prior
`neighborhood`	A list of first-order neighborhoods for a partially fused prior
`useCrossValidation`	Logical: Perform cross-validation to determine prior `variance`.
`forceIntercept`	Logical: Force intercept coefficient into prior

Value

A Cyclops prior object of class inheriting from "cyclopsPrior" for use with fitCyclopsModel.

Prior types

We specify all priors in terms of their variance parameters. Similar fitting tools for regularized regression often parameterize the Laplace distribution in terms of a rate "lambda" per observation. See "glmnet", for example.

variance = 2 * / (nobs * lambda)^2 or lambda = sqrt(2 / variance) / nobs

Examples

#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))

Create a Cyclops control object that supports in- / out-of-sample hyperparameter search using weights

Description

createWeightBasedSearchControl creates a Cyclops control object for use with fitCyclopsModel that supports hyperparameter optimization through an auto-search where weight = 1 identifies in-sample observations and weight = 0 identifies out-of-sample observations.

Usage

createWeightBasedSearchControl(cvType = "auto", initialValue = 1, ...)
createWeightBasedSearchControl(cvType = "auto", initialValue = 1, ...)

Arguments

`cvType`	Must equal "auto"
`initialValue`	Initial value for auto-search parameter
`...`	Additional parameters passed through to `createControl`

Value

A Cyclops prior object of class inheriting from "cyclopsControl" for use with fitCyclopsModel.

Cyclops: Cyclic coordinate descent for logistic, Poisson and survival analysis

Description

The Cyclops package 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.

Fit a Cyclops model

Description

fitCyclopsModel fits a Cyclops model data object

Usage

fitCyclopsModel(
  cyclopsData,
  prior = createPrior("none"),
  control = createControl(),
  weights = NULL,
  forceNewObject = FALSE,
  returnEstimates = TRUE,
  startingCoefficients = NULL,
  fixedCoefficients = NULL,
  warnings = TRUE,
  computeDevice = "native"
)
fitCyclopsModel(
  cyclopsData,
  prior = createPrior("none"),
  control = createControl(),
  weights = NULL,
  forceNewObject = FALSE,
  returnEstimates = TRUE,
  startingCoefficients = NULL,
  fixedCoefficients = NULL,
  warnings = TRUE,
  computeDevice = "native"
)

Arguments

`cyclopsData`	A Cyclops data object
`prior`	A prior object. More details are given below.
`control`	A `"cyclopsControl"` object constructed by `createControl`
`weights`	Vector of 0/1 weights for each data row
`forceNewObject`	Logical, forces the construction of a new Cyclops model fit object
`returnEstimates`	Logical, return regression coefficient estimates in Cyclops model fit object
`startingCoefficients`	Vector of starting values for optimization
`fixedCoefficients`	Vector of booleans indicating if coefficient should be fix
`warnings`	Logical, report regularization warnings
`computeDevice`	String: Name of compute device to employ; defaults to `"native"` C++ on CPU

Details

This function performs numerical optimization to fit a Cyclops model data object.

Value

A list that contains a Cyclops model fit object pointer and an operation duration

Prior

Currently supported prior types are:

`⁠ "none"⁠`	Useful for finding MLE
`⁠ "laplace"⁠`	L_1 regularization
`⁠ "normal"⁠`	L_2 regularization

References

Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D. Massive parallelization of serial inference algorithms for complex generalized linear models. ACM Transactions on Modeling and Computer Simulation, 23, 10, 2013.

Simpson SE, Madigan D, Zorych I, Schuemie M, Ryan PB, Suchard MA. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics, 69, 893-902, 2013.

Mittal S, Madigan D, Burd RS, Suchard MA. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics, 15, 207-221, 2014.

Examples

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
cyclopsData <- createCyclopsData(counts ~ outcome + treatment, modelType = "pr")
cyclopsFit <- fitCyclopsModel(cyclopsData, prior = createPrior("none"))
coef(cyclopsFit)
confint(cyclopsFit, c("outcome2","treatment3"))
predict(cyclopsFit)

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
cyclopsData <- createCyclopsData(counts ~ outcome + treatment, modelType = "pr")
cyclopsFit <- fitCyclopsModel(cyclopsData, prior = createPrior("none"))
coef(cyclopsFit)
confint(cyclopsFit, c("outcome2","treatment3"))
predict(cyclopsFit)

Fit simulated data

Description

fitCyclopsSimulation fits simulated Cyclops data using Cyclops or a standard routine. This function is useful for simulation studies comparing the performance of Cyclops when considering large, sparse datasets.

Usage

fitCyclopsSimulation(
  sim,
  useCyclops = TRUE,
  model = "logistic",
  coverage = TRUE,
  includePenalty = FALSE,
  computeDevice = "native"
)
fitCyclopsSimulation(
  sim,
  useCyclops = TRUE,
  model = "logistic",
  coverage = TRUE,
  includePenalty = FALSE,
  computeDevice = "native"
)

Arguments

`sim`	A simulated Cyclops dataset generated via `simulateCyclopsData`
`useCyclops`	Logical: use Cyclops or a standard routine
`model`	String: Fitted regression model type
`coverage`	Logical: report coverage statistics
`includePenalty`	Logical: include regularized regression penalty in computing profile likelihood based confidence intervals
`computeDevice`	String: Name of compute device to employ; defaults to `"native"` C++ on CPU

Get covariate identifiers

Description

getCovariateIds returns a vector of integer64 covariate identifiers in a Cyclops data object

Usage

getCovariateIds(object)
getCovariateIds(object)

Arguments

object

A Cyclops data object

Get covariate types

Description

getCovariateTypes returns a vector covariate types in a Cyclops data object

Usage

getCovariateTypes(object, covariateLabel)
getCovariateTypes(object, covariateLabel)

Arguments

`object`	A Cyclops data object
`covariateLabel`	Integer vector: covariate identifiers to return

Profile likelihood for Cyclops model parameters

Description

getCyclopsProfileLogLikelihood evaluates the profile likelihood at a grid of parameter values.

Usage

getCyclopsProfileLogLikelihood(
  object,
  parm,
  x = NULL,
  bounds = NULL,
  tolerance = 0.001,
  initialGridSize = 10,
  includePenalty = TRUE
)
getCyclopsProfileLogLikelihood(
  object,
  parm,
  x = NULL,
  bounds = NULL,
  tolerance = 0.001,
  initialGridSize = 10,
  includePenalty = TRUE
)

Arguments

`object`	Fitted Cyclops model object
`parm`	Specification of which parameter requires profiling, either a vector of numbers of covariateId names
`x`	Vector of values of the parameter
`bounds`	Pair of values to bound adaptive profiling
`tolerance`	Absolute tolerance allowed for adaptive profiling
`initialGridSize`	Initial grid size for adaptive profiling
`includePenalty`	Logical: Include regularized covariate penalty in profile

Value

A data frame containing the profile log likelihood. Returns NULL when the adaptive profiling fails to converge.

Creates a `Surv` object that forces in competing risks and the IPCW needed for Fine-Gray estimation.

Description

getFineGrayWeights creates a list Surv object and vector of weights required for estimation.

Usage

getFineGrayWeights(ftime, fstatus, cvweights = NULL, cencode = 0, failcode = 1)
getFineGrayWeights(ftime, fstatus, cvweights = NULL, cencode = 0, failcode = 1)

Arguments

`ftime`	Numeric: Observed event (failure) times
`fstatus`	Numeric: Observed event (failure) types
`cvweights`	Numeric: Vector of 0/1 (cross-validation) weights for each data row
`cencode`	Numeric: Code to denote censored observations (Default is 0)
`failcode`	Numeric: Code to denote event of interest (Default is 1)

Value

A list that returns both an object of class Surv that forces in the competing risks indicators and a vector of weights needed for parameter estimation.

Examples

ftime <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
fstatus <- c(1, 2, 0, 1, 2, 0, 1, 2, 0, 1)
getFineGrayWeights(ftime, fstatus, cencode = 0, failcode = 1)
ftime <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
fstatus <- c(1, 2, 0, 1, 2, 0, 1, 2, 0, 1)
getFineGrayWeights(ftime, fstatus, cencode = 0, failcode = 1)

Get floating point size

Description

getFloatingPointSize returns the floating-point representation size in a Cyclops data object

Usage

getFloatingPointSize(object)
getFloatingPointSize(object)

Arguments

object

A Cyclops data object

Get hyperparameter

Description

getHyperParameter returns the current hyper parameter in a Cyclops model fit object

Usage

getHyperParameter(object)
getHyperParameter(object)

Arguments

object

A Cyclops model fit object

Examples

#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))

Get total number of covariates

Description

getNumberOfCovariates returns the total number of covariates in a Cyclops data object

Usage

getNumberOfCovariates(object)
getNumberOfCovariates(object)

Arguments

object

A Cyclops data object

Get total number of rows

Description

getNumberOfRows returns the total number of outcome rows in a Cyclops data object

Usage

getNumberOfRows(object)
getNumberOfRows(object)

Arguments

object

A Cyclops data object

Get number of strata

Description

getNumberOfStrata return the number of unique strata in a Cyclops data object

Usage

getNumberOfStrata(object)
getNumberOfStrata(object)

Arguments

object

A Cyclops data object

Get univariable correlation

Description

getUnivariableCorrelation reports covariates that have high correlation with the outcome

Usage

getUnivariableCorrelation(cyclopsData, covariates = NULL, threshold = 0)
getUnivariableCorrelation(cyclopsData, covariates = NULL, threshold = 0)

Arguments

`cyclopsData`	A Cyclops data object
`covariates`	Integer or string vector: list of covariates to report; default (NULL) implies all covariates
`threshold`	Correlation threshold for reporting

Value

A list of covariates whose absolute correlation with the outcome is greater than or equal to the threshold

Get univariable linear separability

Description

getUnivariableSeparability reports covariates that are univariably separable with the outcome

Usage

getUnivariableSeparability(cyclopsData, covariates = NULL)
getUnivariableSeparability(cyclopsData, covariates = NULL)

Arguments

`cyclopsData`	A Cyclops data object
`covariates`	Integer or string vector: list of covariates to report; default (NULL) implies all covariates

Value

A list of covariates that are univariably separable with the outcome

Check if a Cyclops data object is initialized

Description

isInitialized determines if an Cyclops data object is properly initialized and remains in memory. Cyclops data objects do not serialized/deserialize their back-end memory across R sessions.

Usage

isInitialized(object)
isInitialized(object)

Arguments

object

Cyclops data object to test

List available GPU devices

Description

listGPUDevices list available GPU devices

Usage

listGPUDevices()
listGPUDevices()

Extract log-likelihood

Description

logLik returns the current log-likelihood of the fit in a Cyclops model fit object

Usage

## S3 method for class 'cyclopsFit'
logLik(object, ...)
## S3 method for class 'cyclopsFit'
logLik(object, ...)

Arguments

`object`	A Cyclops model fit object
`...`	Additional arguments

Examples

#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))

Calculates xbar*beta

Description

meanLinearPredictor computes xbar*beta for model fit

Usage

meanLinearPredictor(cyclopsFit)
meanLinearPredictor(cyclopsFit)

Arguments

cyclopsFit

A Cyclops model fit object

Mean squared error

Description

mse computes the mean squared error between two numeric vectors

Usage

mse(goldStandard, estimates)
mse(goldStandard, estimates)

Arguments

`goldStandard`	Numeric vector
`estimates`	Numeric vector

Value

MSE(goldStandard, estimates)

Create a multitype outcome object

Description

Multitype creates a multitype outcome object, usually used as a response variable in a hierarchical Cyclops model fit.

Usage

Multitype(y, type)
Multitype(y, type)

Arguments

`y`	Numeric: Response count(s)
`type`	Numeric or factor: Response type

Value

An object of class Multitype with length equal to the length of y and type.

Examples

Multitype(c(0,1,0), as.factor(c("A","A","B")))

Multitype(c(0,1,0), as.factor(c("A","A","B")))

Oxford self-controlled case series data

Description

A dataset containing the MMR vaccination / meningitis in Oxford example from Farrington and Whitaker. There are 10 patients comprising 38 unique exposure intervals.

Usage

data(oxford)
data(oxford)

Format

A data frame with 38 rows and 6 variables:

indiv: patient identifier
event: number of events in interval
interval: interval length in days
agegr: age group
exgr: exposure group
loginterval: log interval length

...

Model predictions

Description

predict.cyclopsFit computes model response-scale predictive values for all data rows

Usage

## S3 method for class 'cyclopsFit'
predict(object, newOutcomes, newCovariates, ...)
## S3 method for class 'cyclopsFit'
predict(object, newOutcomes, newCovariates, ...)

Arguments

`object`	A Cyclops model fit object
`newOutcomes`	An optional data frame or Andromeda table object, similar to the object used in `convertToCyclopsData`.
`newCovariates`	An optional data frame or Andromeda table object, similar to the object used in `convertToCyclopsData`.
`...`	Additional arguments

Print a Cyclops data object

Description

print.cyclopsData displays information about a Cyclops data model object.

Usage

## S3 method for class 'cyclopsData'
print(x, show.call = TRUE, ...)
## S3 method for class 'cyclopsData'
print(x, show.call = TRUE, ...)

Arguments

`x`	A Cyclops data model object
`show.call`	Logical: display last call to construct the Cyclops data model object
`...`	Additional arguments

Print a Cyclops model fit object

Description

print.cyclopsFit displays information about a Cyclops model fit object

Usage

## S3 method for class 'cyclopsFit'
print(x, show.call = TRUE, ...)
## S3 method for class 'cyclopsFit'
print(x, show.call = TRUE, ...)

Arguments

`x`	A Cyclops model fit object
`show.call`	Logical: display last call to update the Cyclops model fit object
`...`	Additional arguments

Read Cyclops data from file

Description

readCyclopsData reads a Cyclops-formatted text file.

Usage

readCyclopsData(fileName, modelType)
readCyclopsData(fileName, modelType)

Arguments

`fileName`	Name of text file to be read. If fileName does not contain an absolute path,
`modelType`	character string: Valid types are listed below.

Details

This function reads a Cyclops-formatted text file and returns a Cyclops data object. The first line of the file may start with '‘⁠#⁠’', indicating that it contains header options. Valid header options are:

`⁠ row_label⁠`	(assume file contains a numeric column of unique row identifiers)
`⁠ stratum_label⁠`	(assume file contains a numeric column of stratum identifiers)
`⁠ weight⁠`	(assume file contains a column of row-specific model weights, currently unused)
`⁠ offset⁠`	(assume file contains a dense column of linear predictor offsets)
`⁠ bbr_outcome⁠`	(assume logistic outcomes are encoded -1/+1 following BBR)
`⁠ log_offset⁠`	(assume file contains a dense column of values x_i for which log(x_i) is the offset)
`⁠ add_intercept⁠`	(automatically include an intercept column of all 1s for each entry)
`⁠ indicator_only⁠`	(assume all covariates 0/1-valued and only covariate name is given)
`⁠ sparse⁠`	(force all BBR formatted covariates to be represented as sparse, instead of
	sparse-indicator, columns .. really only for debugging)
`⁠ dense⁠`	(force all BBR formatted covariates to be represented as dense columns.. really
	only for debugging)

Successive lines of the file are white-space delimited and follow the format:

	[Row ID] {Stratum ID} [Weight] <Outcome> {Censored} {Offset} <BBR covariates>

⁠[optional]⁠
⁠<required>⁠
⁠{required or optional depending on model}⁠

Bayesian binary regression (BBR) covariates are white-space delimited and generally in a sparse ‘⁠<name>:<value>⁠’ format, where ‘⁠name⁠’ must (currently) be numeric and ‘⁠value⁠’ is non-zero. If option ‘⁠indicator_only⁠’ is specified, then format is simply ‘⁠<name>⁠’. ‘⁠Row ID⁠’ and ‘⁠Stratum ID⁠’ must be numeric, and rows must be sorted such that equal ‘⁠Stratum ID⁠’ are consecutive. ‘⁠Stratum ID⁠’ is required for ‘⁠clr⁠’ and ‘⁠sccs⁠’ models. ‘⁠Censored⁠’ is required for a ‘⁠cox⁠’ model. ‘⁠Offset⁠’ is (currently) required for a ‘⁠sccs⁠’ model.

Value

A list that contains a Cyclops model data object pointer and an operation duration

Models

Currently supported model types are:

`⁠ "ls"⁠`	Least squares
`⁠ "pr"⁠`	Poisson regression
`⁠ "lr"⁠`	Logistic regression
`⁠ "clr"⁠`	Conditional logistic regression
`⁠ "cpr"⁠`	Conditional Poisson regression
`⁠ "sccs"⁠`	Self-controlled case series
`⁠ "cox"⁠`	Cox proportional hazards regression
`⁠ "fgr"⁠`	Fine-Gray proportional subdistribution hazards regression

Examples

## Not run: 
dataPtr = readCyclopsData(system.file("extdata/infert_ccd.txt", package="Cyclops"), "clr")

## End(Not run)
## Not run: 
dataPtr = readCyclopsData(system.file("extdata/infert_ccd.txt", package="Cyclops"), "clr")

## End(Not run)

Run Bootstrap for Cyclops model parameter

Description

Run Bootstrap for Cyclops model parameter

Usage

runBootstrap(object, outFileName, treatmentId, replicates)
runBootstrap(object, outFileName, treatmentId, replicates)

Arguments

`object`	A fitted Cyclops model object
`outFileName`	Character: Output file name
`treatmentId`	Character: variable to output
`replicates`	Numeric: number of bootstrap samples

Set GPU device

Description

setOpenCLDevice set GPU device

Usage

setOpenCLDevice(name)
setOpenCLDevice(name)

Arguments

name

String: Name of GPU device

Simulation Cyclops dataset

Description

simulateCyclopsData generates a simulated large, sparse data set for use by fitCyclopsSimulation.

Usage

simulateCyclopsData(
  nstrata = 200,
  nrows = 10000,
  ncovars = 20,
  effectSizeSd = 1,
  zeroEffectSizeProp = 0.9,
  eCovarsPerRow = ncovars/100,
  model = "survival"
)
simulateCyclopsData(
  nstrata = 200,
  nrows = 10000,
  ncovars = 20,
  effectSizeSd = 1,
  zeroEffectSizeProp = 0.9,
  eCovarsPerRow = ncovars/100,
  model = "survival"
)

Arguments

`nstrata`	Numeric: Number of strata
`nrows`	Numeric: Number of observation rows
`ncovars`	Numeric: Number of covariates
`effectSizeSd`	Numeric: Standard derivation of the non-zero simulated regression coefficients
`zeroEffectSizeProp`	Numeric: Expected proportion of zero effect size
`eCovarsPerRow`	Number: Effective number of non-zero covariates per data row
`model`	String: Simulation model. Choices are: `logistic`, `poisson` or `survival`

Value

A simulated data set

Examples

#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, 
                           model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", 
                                    addIntercept = TRUE)

#Define the prior and control objects to use cross-validation for finding the 
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")

#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)  

#Find out what the optimal hyperparameter was:
getHyperParameter(fit)

#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)

#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))

Split the analysis time into several intervals for time-varying coefficients.

Description

splitTime split the analysis time into several intervals for time-varying coefficients

Usage

splitTime(shortOut, cut)
splitTime(shortOut, cut)

Arguments

shortOut

A data frame containing the outcomes with predefined columns (see below).

cut

Numeric: Time points to cut at

These columns are expected in the shortOut object:

`⁠rowId⁠`	(integer)	Row ID is used to link multiple covariates (x) to a single outcome (y)
`⁠y⁠`	(real)	Observed event status
`⁠time⁠`	(real)	Observed event time

Value

A long outcome table for time-varying coefficients.

Cyclops data object summary

Description

summary.cyclopsData summarizes the data held in an Cyclops data object.

Usage

## S3 method for class 'cyclopsData'
summary(object, ...)
## S3 method for class 'cyclopsData'
summary(object, ...)

Arguments

`object`	A Cyclops data object
`...`	Additional arguments

Value

Returns a data.frame that reports simply summarize statistics for each covariate in a Cyclops data object.

Calculate baseline hazard function

Description

survfit.cyclopsFit computes baseline hazard function

Usage

## S3 method for class 'cyclopsFit'
survfit(formula, type = "aalen", ...)
## S3 method for class 'cyclopsFit'
survfit(formula, type = "aalen", ...)

Arguments

`formula`	A Cyclops survival model fit object
`type`	type of baseline survival, choices are: "aalen" (Breslow)
`...`	for future methods

Value

Baseline survival function for mean covariates

Calculate variance-covariance matrix for a fitted Cyclops model object

Description

vcov.cyclopsFit returns the variance-covariance matrix for all covariates of a Cyclops model object

Usage

## S3 method for class 'cyclopsFit'
vcov(object, control, overrideNoRegularization = FALSE, ...)
## S3 method for class 'cyclopsFit'
vcov(object, control, overrideNoRegularization = FALSE, ...)

Arguments

`object`	A fitted Cyclops model object
`control`	A `"cyclopsControl"` object constructed by `createControl`
`overrideNoRegularization`	Logical: Enable variance-covariance estimation for regularized parameters
`...`	Additional argument(s) for methods

Value

A matrix of the estimates covariances between all covariate estimates.

Package 'Cyclops'

Help Index

Extract model coefficients

Description

Usage

Arguments

Value

Confidence intervals for Cyclops model parameters

Description

Usage

Arguments

Value

Examples

Convert data from two data frames or ffdf objects into a CyclopsData object

Description

Usage

Arguments

Details

Value

Methods (by class)

Examples

Convert short sparse covariate table to long sparse covariate table for time-varying coefficients.

Description

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Coverage

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Create a Cyclops control object that supports multiple hyperparameters

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Create a Cyclops control object

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Create a Cyclops data object

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Create a Cyclops prior object that returns the MLE of non-separable coefficients

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Create a Cyclops parameterized prior object

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Create a Cyclops prior object

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Prior types

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Create a Cyclops control object that supports in- / out-of-sample hyperparameter search using weights

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Cyclops: Cyclic coordinate descent for logistic, Poisson and survival analysis

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Fit a Cyclops model

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Creates a `Surv` object that forces in competing risks and the IPCW needed for Fine-Gray estimation.