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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