Specify prediction variable for pcVPC.
predcorrect(o, ...)
# S3 method for class 'tidyvpcobj'
predcorrect(o, pred, data = o$data, ..., log = FALSE, varcorr = FALSE)A `tidyvpcobj`.
Other arguments to include.
Prediction variable in observed data.
Observed data supplied in `observed()` function.
Logical indicating whether DV was modeled in logarithmic scale.
Logical indicating whether variability correction should be applied for prediction corrected dependent variable
Updates `tidyvpcobj` with required information to perform prediction correction, which includes the `predcor` logical indicating whether prediction corrected VPC is to be performed, the `predcor.log` logical indicating whether the DV is on a log-scale, the `varcorr` logical indicating whether variability correction for prediction corrected dependent variable is applied and the `pred` prediction column from the original data. Both `obs` and `sim` data tables in the returned `tidyvpcobj` object have additional `ypc` column with the results of prediction correction and `ypcvc` column if variability correction is requested.
# \donttest{
require(magrittr)
obs_data <- obs_data[MDV == 0]
sim_data <- sim_data[MDV == 0]
# Add PRED variable to observed data from first replicate of
# simulated data
obs_data$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()
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning:
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning:
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning:
#> Warning:
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning:
# }