createCyclopsData: Create a Cyclops data object
In Cyclops: Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis

createCyclopsData

R Documentation

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"
)

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)

Cyclops documentation built on June 25, 2025, 1:07 a.m.