#' Convert a linear model to a text representation of the prediction equation
#'
#' @param model The linear model object
#'
#' @keywords internal
#'
get_lm_formula <- function(model) {
if (is.na(model$coefficients["(Intercept)"]) |
is.null(model$coefficients["(Intercept)"])) {
model$coefficients["(Intercept)"] <-
"(No Intercept)"
}
intercept <- model$coefficients["(Intercept)"]
coeffs <- model$coefficients[names(model$coefficients) != "(Intercept)"]
coeffs <-
paste(sapply(coeffs, function(x) switch(sign(x) + 2, "-", "+", "+")),
format(abs(coeffs), digits = 3, nsmall = 3, scientific = 5),
paste("(", names(coeffs), ")", sep = "")
)
paste(
format(intercept, digits = 3, nsmall = 3, scientific = 5),
paste(coeffs, collapse = " ")
)
}
#' Convert an object of class TwoRegression to a textual representation of the
#' algorithm
#'
#' @param object the TwoRegression object
#'
#' @keywords internal
#'
get_2rm_formula <- function(object) {
step_1 <- paste(
"If ", object$sed_variable, " <= ",
object$sed_cutpoint, ": METS = ",
object$sed_METs,
sep = ""
)
step_2 <- paste(
"If ", object$sed_variable, " > ",
object$sed_cutpoint, " AND ",
object$walkrun_variable, " <= ",
object$walkrun_cutpoint,
": METs = ",
get_lm_formula(object$walkrun_model),
sep = ""
)
step_3 <- paste(
"If ", object$sed_variable, " > ",
object$sed_cutpoint, " AND ",
object$walkrun_variable, " > ",
object$walkrun_cutpoint,
": METs = ",
get_lm_formula(object$intermittent_model),
sep = ""
)
c(step_1, step_2, step_3)
}
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