What's New in tidyvpc? • tidyvpc

tidyvpc 1.5.0

Plot Percentage of BLQ and/or ALQ

For VPC with censoring, users can supply additional arguments to plot.tidyvpcobj e.g., censoring.type (options are 'none', 'blq', 'alq', or 'both', defaults to 'none') and censoring.output (options are 'grid' or 'list', defaults to 'grid').

If censoring.output = 'grid', the plots will be arranged into single grid plot. Users may pass additional arguments via ellipsis to egg::ggarrange e.g., nrow = 1, ncol = 2 in order to customize plots in grid arrangement.

If censoring.output = 'list', the resulting plots will be returned individually as elements in list.

Example usage is below:

library(tidyvpc)
#> tidyvpc is part of Certara.R!
#> Follow the link below to learn more about PMx R package development at Certara.
#> https://certara.github.io/R-Certara/
obs_data <- obs_data[MDV == 0]
sim_data <- sim_data[MDV == 0]
obs_data$LLOQ <- obs_data[, ifelse(STUDY == "Study A", 50, 25)]
obs_data$ULOQ <- obs_data[, ifelse(STUDY == "Study A", 125, 100)]

vpc <- observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  censoring(blq = DV < LLOQ, lloq = LLOQ,  alq = DV > ULOQ, uloq = ULOQ) |>
  stratify(~ STUDY) |>
  binning(bin = NTIME) |>
  vpcstats(qpred = c(0.1, 0.5, 0.9))

If blq data, users may supply censoring.type = "blq":

plot(vpc, censoring.type = "blq", censoring.output = "grid", facet.scales = "fixed")

If alq data, users may supply censoring.type = "alq":

plot(vpc, censoring.type = "alq", censoring.output = "grid", ncol = 2, nrow = 1)

If both blq and alq data, users may supply censoring.type = "both"

vpc_plots <- plot(vpc, censoring.type = "both", censoring.output = "list")

By default, when censoring.tidyvpcobj is used, no percentage blq/alq plots will be returned e.g., default for censoring.type = 'none'. If users specify censoring.type='both' and only blq censoring was performed, for example, they will receive an error stating e.g., pctalq data.frame was not found in tidyvpcobj. Use censoring() to create censored data for plotting alq.

tidyvpc 1.4.0

Additional Binning Methods

The following additional binning methods from classInt have been made available in tidyvpc.

see ?classIntervals ‘style’ descriptions for applicable arguments for each selected binning method.

library(tidyvpc)
obs_data <- obs_data[MDV == 0]
sim_data <- sim_data[MDV == 0]

`headtails`

observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "headtails") |>
  plot()

Including additional thr argument.

observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "headtails", thr = 0.55) |>
  plot()

`maximum`

observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "maximum") |>
  plot()

Including additional nbins argument.

observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "maximum", nbins = 7) |>
  plot()

`box`

observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "box") |>
  plot()

Including additional iqr_mult and type argument.

observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "box", iqr_mult = 4) |>
  plot()


# additional (quantile) type arg
observed(obs_data, x = TIME, y = DV) |>
  simulated(sim_data, y = DV) |>
  binning(bin = "box", type = 3) |>
  plot()

Additional flexibility for binless() + predcorrect()

Users may now execute predcorrect() either before, or after calling binless(loess.ypc=TRUE). Previously, you were required to execute predcorrect() before binless(loess.ypc=TRUE), otherwise you’d receive an error.

The following code below produces equivalent output:

observed(obs_data, x = TIME, y = DV ) |>
  simulated(sim_data, y = DV) |>
  stratify(~ GENDER) |>
  predcorrect(pred=PRED) |> #before binless()
  binless(loess.ypc=TRUE) |>
  vpcstats() |>
  plot()

observed(obs_data, x = TIME, y = DV ) |>
  simulated(sim_data, y = DV) |>
  stratify(~ GENDER) |>
  binless(loess.ypc=TRUE) |> 
  predcorrect(pred=PRED) |> #after binless()
  vpcstats() |>
  plot()

tidyvpc 1.3.0

An overview of updates to plot() function in tidyvpc v1.3.0

Set plot output dimensions:

knitr::opts_chunk$set(fig.width=12, fig.height=8, dpi = 300)

library(tidyvpc)
library(magrittr)
obs_data <- obs_data[MDV == 0]
sim_data <- sim_data[MDV == 0]

vpc <- observed(obs_data, x=TIME, y=DV) %>%
  simulated(sim_data, y=DV) %>%
  stratify(~ GENDER) %>%
  binless() %>%
  vpcstats()

plot(vpc)

vpc <- observed(obs_data, x=TIME, y=DV) %>%
  simulated(sim_data, y=DV) %>%
  stratify(~ GENDER + STUDY) %>%
  binning(bin = "jenks", nbins = 8) %>%
  vpcstats()

plot(vpc)

vpc <- observed(obs_data, x=TIME, y=DV) %>%
  simulated(sim_data, y=DV) %>%
  stratify(GENDER ~ STUDY) %>%
  binning(bin = "kmeans", nbins = 6) %>%
  vpcstats()

plot(vpc)

We can use facet = TRUE argument to facet continuous VPC by quantile or facet categorical VPC by predicted probability.

vpc <- observed(obs_data, x=TIME, y=DV) %>%
  simulated(sim_data, y=DV) %>%
  binless() %>%
  vpcstats()

plot(vpc, facet = TRUE, point.alpha = 0.1, point.size = 1, ribbon.alpha = 0.2)

vpc <- observed(obs_cat_data, x = agemonths, yobs = zlencat) %>%
  simulated(sim_cat_data, ysim = DV) %>%
  binless() %>% 
  vpcstats(vpc.type = "categorical")

plot(vpc, facet = TRUE, legend.position = "bottom")

Changing point size, point alpha, point shape, point stroke, and ribbon alpha

Setup categorical VPC.

vpc <- observed(obs_cat_data, x = agemonths, yobs = zlencat) %>%
  simulated(sim_cat_data, ysim = DV) %>%
  binning(bin = round(agemonths, 0)) %>%
  vpcstats(vpc.type = "categorical")

Adjust point size.

plot(vpc, point.size = 4)

Setup continuous VPC.

vpc <- observed(obs_data, x=TIME, y=DV) %>%
  simulated(sim_data, y=DV) %>%
  binless() %>%
  vpcstats()

plot(vpc, point.size = 1.5, point.stroke = 2.5, point.alpha = 0.1, ribbon.alpha = 0.05)

plot(vpc, point.size = 5, point.stroke = 0.3, point.shape = "triangle")

plot(vpc, point.size = 7, point.shape = "square-fill", point.alpha = 0.1, ribbon.alpha = 0.5)

What’s New in tidyvpc?