tidyvpc (version 1.5.2)

censoring: Censoring observed data for Visual Predictive Check (VPC)

Description

Specify censoring variable or censoring value for VPC.

Usage

censoring(o, ...)
# S3 method for tidyvpcobj
censoring(o, blq, lloq, alq, uloq, data = o$data, ...)

Value

Updates obs

data.frame in tidypcobj with censored values for observed data which includes lloq and uloq specified values for lower/upper limit of quantification. Logicals for blq and alq are returned that indicate whether the DV value lies below/above limit of quantification.

Arguments

o

A tidyvpcobj.

...

Other arguments to include.

blq

blq variable if present in observed data.

lloq

Numeric value or numeric variable in data indicating the upper limit of quantification.

alq

Logical variable indicating above limit of quantification.

uloq

Numeric value or numeric variable in data indicating the upper limit of quantification.

data

Observed data supplied in observed() function.

See Also

observed simulated stratify predcorrect binning binless vpcstats

Examples

# \donttest{
require(magrittr)

vpc <- observed(obs_data, x=TIME, y=DV) %>%
    simulated(sim_data, y=DV) %>%
    censoring(blq=(DV < 50), lloq=50) %>%
    binning(bin = "pam", nbins = 5) %>%
    vpcstats()

#Using LLOQ variable in data with different values of LLOQ by Study:

obs_data$LLOQ <- obs_data[, ifelse(STUDY == "Study A", 50, 25)]

vpc <- observed(obs_data, x=TIME, y=DV) %>%
    simulated(sim_data, y=DV) %>%
    censoring(blq=(DV < LLOQ), lloq=LLOQ) %>%
    stratify(~ STUDY) %>%
    binning(bin = "kmeans", nbins = 4) %>%
    vpcstats()
# }