tidyvpc (version 1.5.2)

predcorrect: Prediction corrected Visual Predictive Check (pcVPC)

Description

Specify prediction variable for pcVPC.

Usage

predcorrect(o, ...)
# S3 method for tidyvpcobj
predcorrect(o, pred, data = o$data, ..., log = FALSE, varcorr = FALSE)

Value

Updates `tidyvpcobj` with required information to perform prediction correction, which includes the `predcor` logical indicating whether prediction corrected VPC is to be performed, the `predcor.log` logical indicating whether the DV is on a log-scale, the `varcorr` logical indicating whether variability correction for prediction corrected dependent variable is applied and the `pred` prediction column from the original data. Both `obs` and `sim` data tables in the returned `tidyvpcobj` object have additional `ypc` column with the results of prediction correction and `ypcvc` column if variability correction is requested.

Arguments

o

A `tidyvpcobj`.

...

Other arguments to include.

pred

Prediction variable in observed data.

data

Observed data supplied in `observed()` function.

log

Logical indicating whether DV was modeled in logarithmic scale.

varcorr

Logical indicating whether variability correction should be applied for prediction corrected dependent variable

See Also

observed simulated censoring stratify binning binless vpcstats

Examples

# \donttest{
require(magrittr)

obs_data <- obs_data[MDV == 0]
sim_data <- sim_data[MDV == 0]

 # Add PRED variable to observed data from first replicate of
 # simulated data

obs_data$PRED <- sim_data[REP == 1, PRED]

  vpc <- observed(obs_data, x=TIME, yobs=DV) %>%
       simulated(sim_data, ysim=DV) %>%
       binning(bin = NTIME) %>%
       predcorrect(pred=PRED, varcorr = TRUE) %>%
       vpcstats()

 # For binless loess prediction corrected, use predcorrect() before
 # binless() and set loess.ypc = TRUE

  vpc <- observed(obs_data, x=TIME, yobs=DV) %>%
       simulated(sim_data, ysim=DV) %>%
       predcorrect(pred=PRED) %>%
       binless() %>%
       vpcstats()
       # }