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