Merge pull request #46 from mayer79/importfrom

remove importFrom

.gitignore

@@ -7,4 +7,4 @@ Meta
 /doc/
 /Meta/
 inst/doc
-revdep
+

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 2.2.0
-Date: 2023-03-24 19:48:56 UTC
-SHA: 7213048cfb0727fc7f705715bf603ab86550dc61
+Version: 2.2.1
+Date: 2023-04-28 12:35:48 UTC
+SHA: d9029a1a099cc255088439b2e623f7fa3b4aecf0

NAMESPACE

@@ -4,13 +4,3 @@ export(generateNA)
 export(imputeUnivariate)
 export(missRanger)
 export(pmm)
-importFrom(FNN,knnx.index)
-importFrom(ranger,ranger)
-importFrom(stats,predict)
-importFrom(stats,reformulate)
-importFrom(stats,rmultinom)
-importFrom(stats,setNames)
-importFrom(stats,terms.formula)
-importFrom(stats,var)
-importFrom(utils,setTxtProgressBar)
-importFrom(utils,txtProgressBar)

NEWS.md

@@ -1,6 +1,7 @@
 # missRanger 2.2.1
 
-- Maintenance: Remove "documentation" workflow
+- Switch from `importFrom` to `::` code style
+- Documentation improved
 
 # missRanger 2.2.0
 

R/missRanger.R

@@ -1,6 +1,6 @@
 #' Fast Imputation of Missing Values by Chained Random Forests
 #' 
-#' Uses the "ranger" package (Wright & Ziegler) to do fast missing value imputation by 
+#' Uses the {ranger} package (Wright & Ziegler) to do fast missing value imputation by 
 #' chained random forests, see Stekhoven & Buehlmann and Van Buuren & Groothuis-Oudshoorn.
 #' Between the iterative model fitting, it offers the option of predictive mean matching. 
 #' This firstly avoids imputation with values not present in the original data 
@@ -21,14 +21,11 @@
 #' single-argument function of the number of available covariables, 
 #' e.g. \code{mtry = function(m) max(1, m %/% 3)}.
 #' 
-#' @importFrom stats var reformulate terms.formula predict setNames
-#' @importFrom ranger ranger
-#' @importFrom utils setTxtProgressBar txtProgressBar
 #' @param data A \code{data.frame} or \code{tibble} with missing values to impute.
 #' @param formula A two-sided formula specifying variables to be imputed 
 #' (left hand side) and variables used to impute (right hand side). 
-#' Defaults to \code{. ~ .}, i.e. use all variables to impute all variables. 
-#' If e.g. all variables (with missings) should be imputed by all variables 
+#' Defaults to \code{. ~ .}, i.e., use all variables to impute all variables. 
+#' For instance, if all variables (with missings) should be imputed by all variables 
 #' except variable "ID", use \code{. ~ . - ID}. Note that a "." is evaluated 
 #' separately for each side of the formula. Further note that variables with missings 
 #' must appear in the left hand side if they should be used on the right hand side.
@@ -47,10 +44,10 @@
 #' is added to the output as attribute "oob". This does not work in the special case 
 #' when the variables are imputed univariately.
 #' @param case.weights Vector with non-negative case weights.
-#' @param ... Arguments passed to \code{ranger()}. If the data set is large, 
+#' @param ... Arguments passed to \code{ranger::ranger()}. If the data set is large, 
 #' better use less trees (e.g. \code{num.trees = 20}) and/or a low value of 
 #' \code{sample.fraction}. 
-#' The following arguments are e.g. incompatible: 
+#' The following arguments are incompatible, amongst others: 
 #' \code{write.forest}, \code{probability}, \code{split.select.weights}, 
 #' \code{dependent.variable.name}, and \code{classification}. 
 #'
@@ -77,8 +74,13 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
  }
 
  # 1) INITIAL CHECKS
- bad_args <- c("write.forest", "probability", "split.select.weights", 
- "dependent.variable.name", "classification")
+ bad_args <- c(
+ "write.forest", 
+ "probability", 
+ "split.select.weights", 
+ "dependent.variable.name", 
+ "classification"
+ )
  stopifnot(
  "'data' should be a data.frame!" = is.data.frame(data), 
  "'data' should have at least one row and column!" = dim(data) >= 1L, 
@@ -106,11 +108,11 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
  # 2) SELECT AND CONVERT VARIABLES TO IMPUTE
 
  # Extract lhs and rhs from formula
- relevantVars <- lapply(formula[2:3], function(z) attr(terms.formula(
- reformulate(z), data = data[1, ]), "term.labels"))
+ relevantVars <- lapply(formula[2:3], function(z) attr(stats::terms.formula(
+ stats::reformulate(z), data = data[1L, ]), "term.labels"))
 
  # Pick variables from lhs with some but not all missings
- toImpute <- relevantVars[[1]][vapply(data[, relevantVars[[1]], drop = FALSE], 
+ toImpute <- relevantVars[[1L]][vapply(data[, relevantVars[[1L]], drop = FALSE], 
  FUN.VALUE = TRUE, function(z) anyNA(z) && !all(is.na(z)))]
 
  # Try to convert special variables to numeric/factor
@@ -141,8 +143,8 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
 
  # Variables on the rhs should either appear in "visitSeq" 
  # or do not contain any missings
- imputeBy <- relevantVars[[2]][relevantVars[[2]] %in% visitSeq | 
- !vapply(data[, relevantVars[[2]], drop = FALSE], anyNA, TRUE)]
+ imputeBy <- relevantVars[[2L]][relevantVars[[2L]] %in% visitSeq | 
+ !vapply(data[, relevantVars[[2L]], drop = FALSE], anyNA, TRUE)]
  completed <- setdiff(imputeBy, visitSeq)
 
  if (verbose) {
@@ -157,7 +159,7 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
  j <- 1L
  crit <- TRUE
  verboseDigits <- 4L
- predError <- setNames(rep(1, length(visitSeq)), visitSeq)
+ predError <- stats::setNames(rep(1, length(visitSeq)), visitSeq)
 
  if (verbose >= 2) {
  cat("\n", abbreviate(visitSeq, minlength = verboseDigits + 2L), sep = "\t")
@@ -167,11 +169,11 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
  while (crit && j <= maxiter) {
  if (verbose) {
  if (verbose == 1) {
- i <- 1
+ i <- 1L
  cat("\n")
  cat(paste("iter", j))
  cat("\n")
- pb <- txtProgressBar(0, length(visitSeq), style = 3)
+ pb <- utils::txtProgressBar(0, length(visitSeq), style = 3)
  } else if (verbose >= 2) {
  cat("\niter ", j, ":\t", sep = "")
  }
@@ -186,16 +188,18 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
  if (length(completed) == 0L) {
  data[[v]] <- imputeUnivariate(data[[v]])
  } else {
- fit <- ranger(formula = reformulate(completed, response = v), 
- data = data[!v.na, union(v, completed), drop = FALSE],
- case.weights = case.weights[!v.na], ...)
- pred <- predict(fit, data[v.na, completed, drop = FALSE])$predictions
- data[v.na, v] <- if (pmm.k) pmm(xtrain = fit$predictions, 
- xtest = pred, 
- ytrain = data[[v]][!v.na], 
- k = pmm.k) else pred
+ fit <- ranger::ranger(
+ formula = stats::reformulate(completed, response = v),
+ data = data[!v.na, union(v, completed), drop = FALSE],
+ case.weights = case.weights[!v.na], 
+ ...
+ )
+ pred <- stats::predict(fit, data[v.na, completed, drop = FALSE])$predictions
+ data[v.na, v] <- if (pmm.k) pmm(
+ xtrain = fit$predictions, xtest = pred, ytrain = data[[v]][!v.na], k = pmm.k
+ ) else pred
  predError[[v]] <- fit$prediction.error / (
- if (fit$treetype == "Regression") var(data[[v]][!v.na]) else 1
+ if (fit$treetype == "Regression") stats::var(data[[v]][!v.na]) else 1
  )
 
  if (is.nan(predError[[v]])) {
@@ -209,8 +213,8 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
 
  if (verbose) {
  if (verbose == 1) {
- setTxtProgressBar(pb, i)
- i <- i + 1
+ utils::setTxtProgressBar(pb, i)
+ i <- i + 1L
  } else if (verbose >= 2) {
  cat(format(round(predError[[v]], verboseDigits), 
  nsmall = verboseDigits), "\t") 
@@ -239,15 +243,14 @@ missRanger <- function(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
  revert(converted, X = dataLast)
 }
 
-#' A version of \code{typeof} internally used by \code{missRanger}.
+#' A version of \code{typeof()} internally used by \code{missRanger()}.
 #'
-#' @description Returns either "numeric" (double or integer), "factor", "character", "logical", "special" (mode numeric, but neither double nor integer) or "" (otherwise).
-#' \code{missRanger} requires this information to deal with response types not natively supported by \code{ranger}.
-#' 
-#' @author Michael Mayer
+#' Returns either "numeric" (double or integer), "factor", "character", "logical", 
+#' "special" (mode numeric, but neither double nor integer) or "" (otherwise).
+#' \code{missRanger} requires this information to deal with response types not natively 
+#' supported by \code{ranger::ranger()}.
 #' 
 #' @param object Any object.
-#'
 #' @return A string.
 typeof2 <- function(object) {
  if (is.numeric(object)) "numeric" else
@@ -259,14 +262,16 @@ typeof2 <- function(object) {
 
 #' Conversion of non-factor/non-numeric variables.
 #'
-#' @description Converts non-factor/non-numeric variables in a data frame to factor/numeric. Stores information to revert back.
-#' 
-#' @author Michael Mayer
+#' Converts non-factor/non-numeric variables in a data frame to factor/numeric. 
+#' Stores information to revert back.
 #' 
 #' @param X A data frame.
-#' @param check If \code{TRUE}, the function checks if the converted columns can be reverted without changes.
-#'
-#' @return A list with the following elements: \code{X} is the converted data frame, \code{vars}, \code{types}, \code{classes} are the names, types and classes of the converted variables. Finally, \code{bad} names variables in \code{X} that should have been converted but could not. 
+#' @param check If \code{TRUE}, the function checks if the converted columns can be 
+#' reverted without changes.
+#' @return A list with the following elements: \code{X} is the converted data frame, 
+#' \code{vars}, \code{types}, \code{classes} are the names, types and classes of the 
+#' converted variables. Finally, \code{bad} names variables in \code{X} that should 
+#' have been converted but could not. 
 convert <- function(X, check = FALSE) {
  stopifnot(is.data.frame(X))
 
@@ -289,14 +294,11 @@ convert <- function(X, check = FALSE) {
 }
 
 #' Revert conversion.
-#'
-#' @description Reverts conversions done by \code{convert}.
-#' 
-#' @author Michael Mayer
 #' 
-#' @param con A list returned by \code{convert}.
-#' @param X A data frame with some columns to be converted back according to the information stored in \code{converted}.
-#'
+#' Reverts conversions done by \code{convert()}.
+#' @param con A list returned by \code{convert()}.
+#' @param X A data frame with some columns to be converted back according to the 
+#' information stored in \code{converted}.
 #' @return A data frame.
 revert <- function(con, X = con$X) {
  stopifnot(c("vars", "types", "classes") %in% names(con), is.data.frame(X))
@@ -306,8 +308,13 @@ revert <- function(con, X = con$X) {
  }
 
  f <- function(v, ty, cl) {
- switch(ty, logical = as.logical(v), character = as.character(v),
- special = {class(v) <- cl; v}, v)
+ switch(
+ ty, 
+ logical = as.logical(v), 
+ character = as.character(v),
+ special = {class(v) <- cl; v}, 
+ v
+ )
  }
  X[, con$vars] <- Map(f, X[, con$vars, drop = FALSE], con$types, con$classes)
  X

R/pmm.R

@@ -4,9 +4,6 @@
 #' values in the prediction vector \code{xtrain} is randomly chosen and its observed 
 #' value in \code{ytrain} is returned. 
 #' 
-#' @importFrom stats rmultinom
-#' @importFrom FNN knnx.index
-#' 
 #' @param xtrain Vector with predicted values in the training data. 
 #' Can be of type logical, numeric, character, or factor.
 #' @param xtest Vector as \code{xtrain} with predicted values in the test data. 
@@ -70,7 +67,7 @@ pmm <- function(xtrain, xtest, ytrain, k = 1L, seed = NULL) {
 
  # STEP 2: PMM based on k-nearest neightbour.
  k <- min(k, length(xtrain))
- nn <- knnx.index(xtrain, xtest, k)
- take <- t(rmultinom(nt, 1L, rep(1L, k)))
+ nn <- FNN::knnx.index(xtrain, xtest, k)
+ take <- t(stats::rmultinom(nt, 1L, rep(1L, k)))
  ytrain[rowSums(nn * take)]
 }

README.md

@@ -1,4 +1,4 @@
-# missRanger <a href='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/mayer79/missRanger'><img src='man/figures/logo.png' align="right" height="139" /></a>
+# {missRanger} <a href='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/mayer79/missRanger'><img src='man/figures/logo.png' align="right" height="139" /></a>
 
 <!-- badges: start -->
 

cran-comments.md

@@ -1,25 +1,27 @@
-# missRanger 2.2.0
+# missRanger 2.2.1
 
-- removed suggested dependencies dplyr, mice, survival
-- improved documentation
+Dear CRAN team
 
-## R CMD check
-
-checking for unstated dependencies in examples ... OK
+This is a small update, mainly aiming at replacing "importFrom" by "::" logic, plus
+some documentation improvement.
 
-WARNING
- 'qpdf' is needed for checks on size reduction of PDFs
+## R CMD check
 
-checking for future file timestamps ... NOTE
- unable to verify current time
+- WARNING: 'qpdf' is needed for checks on size reduction of PDFs
+- NOTE: unable to verify current time
+- NOTE: no command 'tidy' found
 
 ## RHub 
 
 Note: lastMiKTeXException
+
+## Winbuilder
+
+Status: OK
+
+## REVDEP
+
+- OK: 7 
+- BROKEN: 0
 
 ## Reverse dependency check of 7 packages
-
-- hdImpute 0.1.1 -- E: 0 | W: 0 | N: 0 - marginaleffects 0.11.0 -- E: 0 | W: 0 | N: 0 - mlim 0.3.0 -- E: 0 | W: 0 | N: 0 - NADIA 0.4.2 -- E: 0 | W: 0 | N: 1 - outForest 0.1.2 -- E: 0 | W: 0 | N: 0 - wiseR 1.0.1 -- E: 0 | W: 0 | N: 3 
-- worcs 0.1.10 -- E: 0 | W: 0 | N: 0 
-OK: 7 
-BROKEN: 0