& Manuscript, A. (2014). UpSet: Visualization of Intersecting Sets Europe PMC Funders Group. IEEE Trans Vis Comput Graph, 20(12), 1983–1992. https://doi.org/10.1109/TVCG.2014.2346248 Combination Matrix Set Menu Set View
na.rm = TRUE) [1] 1 sum(c(1, NA), na.rm = FALSE) [1] NA # Instead, write two functions: # one for the TRUE case; and # one for the FALSE case sum_without_na <- function(...) sum(..., na.rm = TRUE) sum_with_na <- function(...) sum(..., na.rm = FALSE)
na.action=na.fail) Question: How can randomForest fit a model on the data, if the data arg is NULL? ## Is this better? randomForest(formula, data, data.env = parent.frame(), ..., subset, na. Answer (from the function documentation): `data` an optional data frame containing the variables in the model. By default the variables are taken from the environment which randomForest is called from NULL Case: Model `data` in parent.env(environment()) Non-NULL Case: Model `data` in environment()
thing is fitting a random forest model on data. It’s easy to make the case that randomForest is doing three things: 1. It searches for data in different environments; 2. It treats NA values in data (in accordance with na.action); and 3. It fits a random forest model on data. If randomForest was truly doing one thing, the absence of clean data would have prompted an ERROR.
does ONE THING, it fits a random forest on data; ## onlyRandomForest doesn’t handle errors from external sources. onlyRandomForest <- function(formula, data, ...){ stopifnot(isFALSE(missing(formula)), isFALSE(missing(data))) stopifnot(class(data) %in% "data.frame") stopifnot(isFALSE(any(is.na(data)))) randomForest::randomForest(formula, data, ...) }
of defining how a function should handle missing values internally, tidyr::drop_na can be used to handle missing values externally. The original function randomForest(formula, data=NULL, ..., subset, na.action=na.fail) Becomes data = tidyr::drop_na(data) randomForest(formula, data, ..., subset)
## 01 Three Arguments Max (0 is best) randomForest(formula, data=NULL, ..., na.action=na.fail) ## 02 No Boolean Arguments Ever (Nor NULL as a pseudo-Boolean) randomForest(formula, data, ..., na.action=na.fail) ## 03 Do One Thing (Either handle errors, or do something else) randomForest(formula, data, ...)