plot.TwoRegression: Create summary plots for TwoRegression objects

In paulhibbing/TwoRegression: Develop and Apply Two-Regression Algorithms

Create summary plots for TwoRegression objects

Description

Four plots are generated: a threshold plot for both cut-points, and a model plot for both regression models

Usage

## S3 method for class 'TwoRegression'
plot(
  x = NULL,
  object = NULL,
  sed_cp_activities,
  sed_activities,
  sed_cpVar = NULL,
  activity_var,
  met_var,
  walkrun_activities,
  walkrun_cpVar,
  x_sed = NULL,
  y_sed = NULL,
  x_walkrun = NULL,
  y_walkrun = NULL,
  print = TRUE,
  ...
)

Arguments

x	passed from generic function but not used in the method
object	the TwoRegression object
sed_cp_activities	Character vector. Activities to be included in the process of forming the sedentary classifier
sed_activities	Character vector. Actual sedentary activities
sed_cpVar	character scalar. The name of the variable on which the cut-point is based
activity_var	Character scalar. Name of the variable defining which activity is being performed
met_var	character scalar. The name of the variable containing energy expenditure values, in metabolic equivalents
walkrun_activities	Character vector. Actual ambulatory activities
walkrun_cpVar	character scalar giving the name of the variable on which the walk/run cut-point is based
x_sed	numeric scalar giving x coordinate for label placement in sedentary cut-point plot
y_sed	numeric scalar giving y coordinate for label placement in sedentary cut-point plot
x_walkrun	numeric scalar giving x coordinate for label placement in walk/run cut-point plot
y_walkrun	numeric scalar giving y coordinate for label placement in walk/run cut-point plot
print	logical. Should the plot be arranged in a grid? If false, the panels will be returned in a list of gg/ggplot objects.
...	further arguments passed to plotting calls

Value

A two-by-two grid of summary plots

Examples

data(all_data, package = "TwoRegression")
all_data$PID <-
  rep(
    c("Test1", "Test2"),
    each = ceiling(nrow(all_data) / 2))[seq(nrow(all_data))]

fake_sed <- c("Lying", "Sitting")
fake_lpa <- c("Sweeping", "Dusting")
fake_cwr <- c("Walking", "Running")
fake_ila <- c("Tennis", "Basketball")

fake_activities <- c(fake_sed, fake_lpa, fake_cwr, fake_ila)

all_data$Activity <-
  sample(fake_activities, nrow(all_data), TRUE)

all_data$fake_METs <-
  ifelse(all_data$Activity %in% c(fake_sed, fake_lpa),
    runif(nrow(all_data), 1, 2),
    runif(nrow(all_data), 2.5, 8)
  )

ex_2rm <- fit_2rm(
  data = all_data,
  activity_var = "Activity",
  sed_cp_activities = c(fake_sed, fake_lpa),
  sed_activities = fake_sed,
  sed_cp_var = "ENMO",
  sed_METs = 1.25,
  walkrun_activities = fake_cwr,
  walkrun_cp_var = "ENMO_CV10s",
  met_var = "fake_METs",
  walkrun_formula = "fake_METs ~ ENMO",
  intermittent_formula = "fake_METs ~ ENMO + I(ENMO^2) + I(ENMO^3)"
)

model_plot_list <- plot(
  object = ex_2rm,
  sed_cp_activities = c(fake_sed, fake_lpa),
  sed_activities = fake_sed,
  sed_cpVar = "ENMO",
  activity_var = "Activity",
  met_var = "fake_METs",
  walkrun_activities = fake_cwr,
  walkrun_cpVar = "ENMO_CV10s",
  print = FALSE
)


  print(model_plot_list$sed_cut_point)
  print(model_plot_list$walkrun_cut_point)
  print(model_plot_list$walkrun_regression)
  print(model_plot_list$intermittent_regression)

  plot(
    object = ex_2rm,
    sed_cp_activities = c(fake_sed, fake_lpa),
    sed_activities = fake_sed,
    sed_cpVar = "ENMO",
    activity_var = "Activity",
    met_var = "fake_METs",
    walkrun_activities = fake_cwr,
    walkrun_cpVar = "ENMO_CV10s",
    print = TRUE
  )

paulhibbing/TwoRegression documentation built on July 9, 2023, 2:57 a.m.