Plot QT against RR

eda_qt_rr_plot(
  data,
  rr_col,
  qt_col,
  id_col = NULL,
  trt_col = NULL,
  conf_int = 0.9,
  model_type = c("lm", "lme"),
  show_model_results = TRUE,
  method = "REML",
  remove_rr_iiv = FALSE,
  style = list()
)

Arguments

data: A data frame containing C-QT analysis dataset
rr_col: An unquoted column name for RR measurements
qt_col: An unquoted column name for QT measurements
id_col: An unquoted column name for subject ID
trt_col: An unquoted column name for treatment group
conf_int: Numeric confidence interval level (default: 0.9)
model_type: Lm or lme, which model to fit for showing on plot
show_model_results: A bool for showing regression slope on plot.
method: Method for nlme::lme fitting (ML or REML)
remove_rr_iiv: A boolean for removing IIV on RR slope
style: A named list of arguments passed to style_plot()

Value

a plot of the input QT against RR

Examples

data_proc <- preprocess(cqtkit_data_verapamil)

eda_qt_rr_plot(data_proc, RR, QT, ID, model_type = "lme")
#> variance-covariance issues detected, try running again with method = 'REML'
#> If issues persist, try removing IIV.
#> Error in intervals.lme(fit, conf_int): cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance
#>  Consider 'which = "fixed"'