predict.TwoRegression | R Documentation |
Predict metabolic equivalents from a TwoRegression object
## S3 method for class 'TwoRegression' predict( object, newdata, min_mets = object$sed_METs, max_mets = 20, warn_high_low = TRUE, verbose = FALSE, ... )
object |
the TwoRegression object |
newdata |
the data on which to predict metabolic equivalents (METs) |
min_mets |
the minimum allowable value for MET predictions. Defaults to
the value stored in |
max_mets |
the maximum allowable value for MET predictions. There is no
value embedded in |
warn_high_low |
logical. Issue warnings about values less than
|
verbose |
logical. Print processing updates? |
... |
further arguments passed to or from other methods |
A two-column data frame giving the activity classification (sedentary, walk/run, or intermittent activity) and the corresponding metabolic equivalent prediction
data(all_data, package = "TwoRegression") all_data$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)
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