predict_with_quantiles_plot

Description

Plots predictions and 90% CI

Usage

predict_with_quantiles_plot(
  data,
  fit,
  conc_col,
  dv_col,
  treatment_predictors,
  control_predictors = NULL,
  reference_threshold = c(
  10
),
  conf_int = 0.9,
  nbins = 10,
  error_bars = "CI",
  xlabel = "Concentration (
  ng/mL
)",
  ylabel = bquote(
  Delta ~ "QTc (
  ms
)"
),
  title = ""
)

Arguments

Name	Description
data	A dataframe of QTc dataset
fit	a lme model to make predictions with
conc_col	an unquoted column name of concentration measurements
dv_col	an unquoted column name of dQTC measurements
treatment_predictors	list of a values for contrast. CONC will update
control_predictors	list of b values for contrast
reference_threshold	optional vector of numbers to add as horizontal dashed lines
conf_int	confidence interval fraction, default = 0.9
nbins	number of bins for quantiles, or vector of cut points for computing average
error_bars	a string to denote which errorbars to show, CI, SE, SD or none.
xlabel	a string for xlabel, default “Concentration (ng/mL)“
ylabel	a string for ylabel, default bquote(Delta~“QTc (ms)“)
title	a string for the plot title

Returns

a plot

Examples

data <- preprocess(data)
 fit <- fit_prespecified_model(
   data,
   deltaQTCF,
   ID,
   CONC,
   deltaQTCFBL,
   TRTG,
   TAFD,
   "REML",
   TRUE
 )
 predict_with_quantiles_plot(
   data,
   fit,
   CONC,
   deltaQTCF,
   treatment_predictors = list(
     CONC = 0,
     TRTG = "Verapamil HCL",
     TAFD = "2 HR",
     deltaQTCFBL = 0
   )
 )