TwoRegression (version 1.1.1)

predict.TwoRegression: Predict metabolic equivalents from a TwoRegression object

Description

Predict metabolic equivalents from a TwoRegression object

Usage

# S3 method for TwoRegression
predict(
  object,
  newdata,
  min_mets = object$sed_METs,
  max_mets = 20,
  warn_high_low = TRUE,
  verbose = FALSE,
  ...
)

Value

A two-column data frame giving the activity classification (sedentary, walk/run, or intermittent activity) and the corresponding metabolic equivalent prediction

Arguments

object

the TwoRegression object

newdata

the data on which to predict metabolic equivalents (METs)

min_mets

the minimum allowable value for MET predictions. Defaults to the value stored in object$sed_METs

max_mets

the maximum allowable value for MET predictions. There is no value embedded in object. The default is 20

warn_high_low

logical. Issue warnings about values less than min_mets or greater than max_mets?

verbose

logical. Print processing updates?

...

further arguments passed to or from other methods

Examples

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

train_data <- all_data[all_data$PID != "Test2", ]
test_data <- all_data[all_data$PID == "Test2", ]

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)

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

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

ex_2rm <- fit_2rm(
  data = train_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)"
)

predict(ex_2rm, test_data)