View source: R/NewDataConversion.R
| convertToCyclopsData | R Documentation | 
convertToCyclopsData loads data from two data frames or ffdf objects, and inserts it into a Cyclops data object.
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
)
| 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) | 
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 | 
An object of type cyclopsData
convertToCyclopsData(data.frame): Convert data from two data.frame
convertToCyclopsData(tbl_dbi): Convert data from two Andromeda tables
#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"))
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