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

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

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

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

# S3 method for tbl_dbi
convertToCyclopsData(
  outcomes,
  covariates,
  modelType = "lr",
  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"
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)

Value

An object of type cyclopsData

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

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)
#> Sorting outcomes by stratumId and rowId
#> Sorting covariates by covariateId, stratumId, and rowId

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