Cyclops (version 1.2.0)

createCyclopsData: 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, weights, offset, time = NULL, pid = NULL, y = NULL, type = NULL, dx = NULL, sx = NULL, ix = NULL, model = FALSE, normalize = NULL, 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

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)

method

Currently undocumented

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

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

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)