get_training_data

<p>#' @keywords internal
where &lt;- function(name, env = parent.frame()) 
  if (identical(env, emptyenv())) 
    stop("Can't find ", name, ".", call. = FALSE)
   else if (exists(name, envir = env, inherits = FALSE)) 
    env
   else 
    where(name, parent.env(env))</p>

internal

A general framework for constructing partial dependence (i.e.,
marginal effect) plots from various types machine learning models
in R.

Brandon Greenwell

Partial Dependence Plots

get_training_data function

#' @keywords internal
where &lt;- function(name, env = parent.frame()) 
  if (identical(env, emptyenv())) 
    stop("Can't find ", name, ".", call. = FALSE)
   else if (exists(name, envir = env, inherits = FALSE)) 
    env
   else 
    where(name, parent.env(env)) — get_training_data

#' @keywords internal
where &lt;- function(name, env = parent.frame()) 
  if (identical(env, emptyenv())) 
    stop("Can't find ", name, ".", call. = FALSE)
   else if (exists(name, envir = env, inherits = FALSE)) 
    env
   else 
    where(name, parent.env(env))

get_training_data: #' @keywords internal where <- function(name, env = parent.frame()) if (identical(env, emptyenv())) stop("Can't find ", name, ".", call. = FALSE) else if (exists(name, envir = env, inherits = FALSE)) env else where(name, parent.env(env))

Description

Usage

Arguments