我爱do.call。我喜欢能够将函数参数存储在列表中，然后将它们分散到给定的函数中。
例如，我经常发现自己使用这种模式来拟合不同预测模型的列表，每种模型都有一些共享和一些独特的参数：
library(caret)
global_args <- list(
x=iris[,1:3],
y=iris[,4],
trControl=trainControl(
method='cv',
number=2,
returnResamp='final',
)
)
global_args$trControl$index <- createFolds(
global_args$y,
global_args$trControl$number
)
model_specific_args <- list(
'lm' = list(method='lm', tuneLength=1),
'nn' = list(method='nnet', tuneLength=3, trace=FALSE),
'gbm' = list(
method='gbm',
verbose=FALSE,
tuneGrid=expand.grid(
n.trees=1:100,
interaction.depth=c(2, 3),
shrinkage=c(.1, .01)
)
)
)
list_of_models <- lapply(model_specific_args, function(args){
return(do.call(train, c(global_args, args), quote=TRUE))
})
resamps <- resamples(list_of_models)
dotplot(resamps, metric='RMSE')
global_args包含对所有模型都相同的参数，model_specific_args包含特定于模型的参数列表。我遍历model_specific_args，用global_args连接每个元素，然后使用do.call将最终参数列表传递给模型拟合函数。
虽然这段代码在视觉上很优雅，但其性能非常糟糕：do.call将整个x数据集字面序列化为文本，然后将其传递给模型拟合函数。如果x是几GB的数据，则会使用疯狂的RAM并且通常会失败。
print(list_of_models[[1]]$call)
有没有办法将参数列表传递给R中的函数，而不使用do.call或call？
答案 0 :(得分：1)
基于@ r2evans注释，这里有一个可能的解决方案：quote()参数列表中的大对象。当do.call评估函数时，它们将从全局环境中被拉出：
library(caret)
x <- iris[,1:3]
y <- iris[,4]
global_args <- list(
x=quote(x),
y=quote(y),
trControl=trainControl(
method='cv',
number=2,
returnResamp='final'
)
)
global_args$trControl$index <- createFolds(
y,
global_args$trControl$number
)
model_specific_args <- list(
'lm' = list(method='lm', tuneLength=1),
'nn' = list(method='nnet', tuneLength=3, trace=FALSE),
'gbm' = list(
method='gbm',
verbose=FALSE,
tuneGrid=expand.grid(
n.trees=1:100,
interaction.depth=c(2, 3),
shrinkage=c(.1, .01)
)
)
)
list_of_models <- lapply(model_specific_args, function(args){
return(do.call(train, c(global_args, args), quote=FALSE))
})
print(list_of_models[[1]]$call)
结果要小得多：
train.default(x = x, y = y, method = "lm", trControl = list(method = "cv",
number = 2, repeats = 1, p = 0.75, initialWindow = NULL,
horizon = 1, fixedWindow = TRUE, verboseIter = FALSE, returnData = TRUE,
returnResamp = "final", savePredictions = FALSE, classProbs = FALSE,
summaryFunction = function (data, lev = NULL, model = NULL)
{
if (is.character(data$obs))
data$obs <- factor(data$obs, levels = lev)
postResample(data[, "pred"], data[, "obs"])
}, selectionFunction = "best", preProcOptions = list(thresh = 0.95,
ICAcomp = 3, k = 5), index = list(Fold1 = c(6L, 7L, 11L,
12L, 13L, 14L, 15L, 16L, 21L, 22L, 25L, 26L, 29L, 32L, 34L,
35L, 36L, 37L, 38L, 39L, 40L, 41L, 48L, 49L, 50L, 51L, 52L,
54L, 57L, 58L, 59L, 64L, 65L, 66L, 67L, 69L, 70L, 71L, 72L,
74L, 78L, 80L, 83L, 84L, 85L, 91L, 92L, 93L, 95L, 98L, 99L,
100L, 103L, 105L, 106L, 107L, 109L, 111L, 112L, 116L, 118L,
122L, 123L, 124L, 125L, 128L, 130L, 132L, 133L, 135L, 138L,
141L, 143L, 144L, 145L, 148L), Fold2 = c(1L, 2L, 3L, 4L,
5L, 8L, 9L, 10L, 17L, 18L, 19L, 20L, 23L, 24L, 27L, 28L,
30L, 31L, 33L, 42L, 43L, 44L, 45L, 46L, 47L, 53L, 55L, 56L,
60L, 61L, 62L, 63L, 68L, 73L, 75L, 76L, 77L, 79L, 81L, 82L,
86L, 87L, 88L, 89L, 90L, 94L, 96L, 97L, 101L, 102L, 104L,
108L, 110L, 113L, 114L, 115L, 117L, 119L, 120L, 121L, 126L,
127L, 129L, 131L, 134L, 136L, 137L, 139L, 140L, 142L, 146L,
147L, 149L, 150L)), indexOut = NULL, timingSamps = 0, predictionBounds = c(FALSE,
FALSE), seeds = NA, adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE), allowParallel = TRUE),
tuneLength = 1)
虽然不必序列化所有其他选项仍然很好。特别是第三个模型的调用仍然很大：print(list_of_models[[3]]$call)
答案 1 :(得分：1)
考虑fastDoCall包中的Gmisc。
library("ranger")
iris2 <- iris[c(1:10, 51:60, 101:110), ]
args <- list(dependent.variable.name = "Species"
, data = iris2
)
args2 <- list(dependent.variable.name = "Species"
, data = as.name("iris2")
)
# do.call with args2 (call prints the function but not data)
do.call(ranger, args2)
#> Ranger result
#>
#> Call:
#> (function (formula = NULL, data = NULL, num.trees = 500, mtry = NULL, importance = "none", write.forest = TRUE, probability = FALSE, min.node.size = NULL, max.depth = NULL, replace = TRUE, sample.fraction = ifelse(replace, 1, 0.632), case.weights = NULL, class.weights = NULL, splitrule = NULL, num.random.splits = 1, alpha = 0.5, minprop = 0.1, split.select.weights = NULL, always.split.variables = NULL, respect.unordered.factors = NULL, scale.permutation.importance = FALSE, keep.inbag = FALSE, inbag = NULL, holdout = FALSE, quantreg = FALSE, oob.error = TRUE, num.threads = NULL, save.memory = FALSE, verbose = TRUE, seed = NULL, dependent.variable.name = NULL, status.variable.name = NULL, classification = NULL) { if ("gwaa.data" %in% class(data)) { snp.names <- data@gtdata@snpnames snp.data <- data@gtdata@gtps@.Data data <- data@phdata if ("id" %in% names(data)) { data$id <- NULL } gwa.mode <- TRUE save.memory <- FALSE } else { snp.data <- as.matrix(0) gwa.mode <- FALSE } if (inherits(data, "Matrix")) { if (!("dgCMatrix" %in% class(data))) { stop("Error: Currently only sparse data of class 'dgCMatrix' supported.") } if (!is.null(formula)) { stop("Error: Sparse matrices only supported with alternative interface. Use dependent.variable.name instead of formula.") } } if (is.null(formula)) { if (is.null(dependent.variable.name)) { stop("Error: Please give formula or dependent variable name.") } if (is.null(status.variable.name)) { status.variable.name <- "" response <- data[, dependent.variable.name, drop = TRUE] } else { response <- survival::Surv(data[, dependent.variable.name], data[, status.variable.name]) } data.selected <- data } else { formula <- formula(formula) if (class(formula) != "formula") { stop("Error: Invalid formula.") } data.selected <- parse.formula(formula, data, env = parent.frame()) response <- data.selected[, 1] } if (any(is.na(data.selected))) { offending_columns <- colnames(data.selected)[colSums(is.na(data.selected)) > 0] stop("Missing data in columns: ", paste0(offending_columns, collapse = ", "), ".", call. = FALSE) } if (is.factor(response)) { if (nlevels(response) != nlevels(droplevels(response))) { dropped_levels <- setdiff(levels(response), levels(droplevels(response))) warning("Dropped unused factor level(s) in dependent variable: ", paste0(dropped_levels, collapse = ", "), ".", call. = FALSE) } } if (is.factor(response)) { if (probability) { treetype <- 9 } else { treetype <- 1 } } else if (is.numeric(response) && (is.null(ncol(response)) || ncol(response) == 1)) { if (!is.null(classification) && classification && !probability) { treetype <- 1 } else if (probability) { treetype <- 9 } else { treetype <- 3 } } else if (class(response) == "Surv" || is.data.frame(response) || is.matrix(response)) { treetype <- 5 } else { stop("Error: Unsupported type of dependent variable.") } if (quantreg && treetype != 3) { stop("Error: Quantile prediction implemented only for regression outcomes.") } if (!is.null(formula)) { if (treetype == 5) { dependent.variable.name <- dimnames(response)[[2]][1] status.variable.name <- dimnames(response)[[2]][2] } else { dependent.variable.name <- names(data.selected)[1] status.variable.name <- "" } independent.variable.names <- names(data.selected)[-1] } else { independent.variable.names <- colnames(data.selected)[colnames(data.selected) != dependent.variable.name & colnames(data.selected) != status.variable.name] } if (is.null(respect.unordered.factors)) { if (!is.null(splitrule) && splitrule == "extratrees") { respect.unordered.factors <- "partition" } else { respect.unordered.factors <- "ignore" } } if (respect.unordered.factors == TRUE) { respect.unordered.factors <- "order" } else if (respect.unordered.factors == FALSE) { respect.unordered.factors <- "ignore" } if (!is.matrix(data.selected) && !inherits(data.selected, "Matrix")) { character.idx <- sapply(data.selected, is.character) if (respect.unordered.factors == "order") { names.selected <- names(data.selected) ordered.idx <- sapply(data.selected, is.ordered) factor.idx <- sapply(data.selected, is.factor) independent.idx <- names.selected != dependent.variable.name & names.selected != status.variable.name & names.selected != paste0("Surv(", dependent.variable.name, ", ", status.variable.name, ")") recode.idx <- independent.idx & (character.idx | (factor.idx & !ordered.idx)) if (any(recode.idx) & (importance == "impurity_corrected" || importance == "impurity_unbiased")) { warning("Corrected impurity importance may not be unbiased for re-ordered factor levels. Consider setting respect.unordered.factors to 'ignore' or 'partition' or manually compute corrected importance.") } if (is.factor(response)) { num.response <- as.numeric(response) } else { num.response <- response } data.selected[recode.idx] <- lapply(data.selected[recode.idx], function(x) { if (!is.factor(x)) { x <- as.factor(x) } if ("Surv" %in% class(response)) { levels.ordered <- largest.quantile(response ~ x) levels.missing <- setdiff(levels(x), levels.ordered) levels.ordered <- c(levels.missing, levels.ordered) } else if (is.factor(response) & nlevels(response) > 2) { levels.ordered <- pca.order(y = response, x = x) } else { means <- sapply(levels(x), function(y) { mean(num.response[x == y]) }) levels.ordered <- as.character(levels(x)[order(means)]) } factor(x, levels = levels.ordered, ordered = TRUE, exclude = NULL) }) covariate.levels <- lapply(data.selected[independent.idx], levels) } else { data.selected[character.idx] <- lapply(data.selected[character.idx], factor) } } if (!is.null(formula) && treetype == 5) { data.final <- data.matrix(cbind(response[, 1], response[, 2], data.selected[-1])) colnames(data.final) <- c(dependent.variable.name, status.variable.name, independent.variable.names) } else if (is.matrix(data.selected) || inherits(data.selected, "Matrix")) { data.final <- data.selected } else { data.final <- data.matrix(data.selected) } variable.names <- colnames(data.final) if (gwa.mode) { variable.names <- c(variable.names, snp.names) all.independent.variable.names <- c(independent.variable.names, snp.names) } else { all.independent.variable.names <- independent.variable.names } if (length(all.independent.variable.names) < 1) { stop("Error: No covariates found.") } if (!is.numeric(num.trees) || num.trees < 1) { stop("Error: Invalid value for num.trees.") } if (is.null(mtry)) { mtry <- 0 } else if (!is.numeric(mtry) || mtry < 0) { stop("Error: Invalid value for mtry") } if (is.null(seed)) { seed <- runif(1, 0, .Machine$integer.max) } if (!is.logical(keep.inbag)) { stop("Error: Invalid value for keep.inbag") } if (is.null(num.threads)) { num.threads = 0 } else if (!is.numeric(num.threads) || num.threads < 0) { stop("Error: Invalid value for num.threads") } if (is.null(min.node.size)) { min.node.size <- 0 } else if (!is.numeric(min.node.size) || min.node.size < 0) { stop("Error: Invalid value for min.node.size") } if (is.null(max.depth)) { max.depth <- 0 } else if (!is.numeric(max.depth) || max.depth < 0) { stop("Error: Invalid value for max.depth. Please give a positive integer.") } if (!is.numeric(sample.fraction)) { stop("Error: Invalid value for sample.fraction. Please give a value in (0,1] or a vector of values in [0,1].") } if (length(sample.fraction) > 1) { if (!(treetype %in% c(1, 9))) { stop("Error: Invalid value for sample.fraction. Vector values only valid for classification forests.") } if (any(sample.fraction < 0) || any(sample.fraction > 1)) { stop("Error: Invalid value for sample.fraction. Please give a value in (0,1] or a vector of values in [0,1].") } if (sum(sample.fraction) <= 0) { stop("Error: Invalid value for sample.fraction. Sum of values must be >0.") } if (length(sample.fraction) != nlevels(response)) { stop("Error: Invalid value for sample.fraction. Expecting ", nlevels(response), " values, provided ", length(sample.fraction), ".") } if (!replace & any(sample.fraction * length(response) > table(response))) { idx <- which(sample.fraction * length(response) > table(response))[1] stop("Error: Not enough samples in class ", names(idx), "; available: ", table(response)[idx], ", requested: ", (sample.fraction * length(response))[idx], ".") } if (!is.null(case.weights)) { stop("Error: Combination of case.weights and class-wise sampling not supported.") } } else { if (sample.fraction <= 0 || sample.fraction > 1) { stop("Error: Invalid value for sample.fraction. Please give a value in (0,1] or a vector of values in [0,1].") } } if (is.null(importance) || importance == "none") { importance.mode <- 0 } else if (importance == "impurity") { importance.mode <- 1 } else if (importance == "impurity_corrected" || importance == "impurity_unbiased") { importance.mode <- 5 } else if (importance == "permutation") { if (scale.permutation.importance) { importance.mode <- 2 } else { importance.mode <- 3 } } else { stop("Error: Unknown importance mode.") } if (is.null(case.weights)) { case.weights <- c(0, 0) use.case.weights <- FALSE if (holdout) { stop("Error: Case weights required to use holdout mode.") } } else { use.case.weights <- TRUE if (holdout) { sample.fraction <- sample.fraction * mean(case.weights > 0) } if (!replace && sum(case.weights > 0) < sample.fraction * nrow(data.final)) { stop("Error: Fewer non-zero case weights than observations to sample.") } } if (is.null(inbag)) { inbag <- list(c(0, 0)) use.inbag <- FALSE } else if (is.list(inbag)) { use.inbag <- TRUE if (use.case.weights) { stop("Error: Combination of case.weights and inbag not supported.") } if (length(sample.fraction) > 1) { stop("Error: Combination of class-wise sampling and inbag not supported.") } if (length(inbag) != num.trees) { stop("Error: Size of inbag list not equal to number of trees.") } } else { stop("Error: Invalid inbag, expects list of vectors of size num.trees.") } if (is.null(class.weights)) { class.weights <- rep(1, nlevels(response)) } else { if (!(treetype %in% c(1, 9))) { stop("Error: Argument class.weights only valid for classification forests.") } if (!is.numeric(class.weights) || any(class.weights < 0)) { stop("Error: Invalid value for class.weights. Please give a vector of non-negative values.") } if (length(class.weights) != nlevels(response)) { stop("Error: Number of class weights not equal to number of classes.") } class.weights <- class.weights[unique(as.numeric(response))] } if (is.null(split.select.weights)) { split.select.weights <- list(c(0, 0)) use.split.select.weights <- FALSE } else if (is.numeric(split.select.weights)) { if (length(split.select.weights) != length(all.independent.variable.names)) { stop("Error: Number of split select weights not equal to number of independent variables.") } split.select.weights <- list(split.select.weights) use.split.select.weights <- TRUE } else if (is.list(split.select.weights)) { if (length(split.select.weights) != num.trees) { stop("Error: Size of split select weights list not equal to number of trees.") } use.split.select.weights <- TRUE } else { stop("Error: Invalid split select weights.") } if (is.null(always.split.variables)) { always.split.variables <- c("0", "0") use.always.split.variables <- FALSE } else { use.always.split.variables <- TRUE } if (use.split.select.weights && use.always.split.variables) { stop("Error: Please use only one option of split.select.weights and always.split.variables.") } if (is.null(splitrule)) { if (treetype == 5) { splitrule <- "logrank" } else if (treetype == 3) { splitrule <- "variance" } else if (treetype %in% c(1, 9)) { splitrule <- "gini" } splitrule.num <- 1 } else if (splitrule == "logrank") { if (treetype == 5) { splitrule.num <- 1 } else { stop("Error: logrank splitrule applicable to survival data only.") } } else if (splitrule == "gini") { if (treetype %in% c(1, 9)) { splitrule.num <- 1 } else { stop("Error: Gini splitrule applicable to classification data only.") } } else if (splitrule == "variance") { if (treetype == 3) { splitrule.num <- 1 } else { stop("Error: variance splitrule applicable to regression data only.") } } else if (splitrule == "auc" || splitrule == "C") { if (treetype == 5) { splitrule.num <- 2 } else { stop("Error: C index splitrule applicable to survival data only.") } } else if (splitrule == "auc_ignore_ties" || splitrule == "C_ignore_ties") { if (treetype == 5) { splitrule.num <- 3 } else { stop("Error: C index splitrule applicable to survival data only.") } } else if (splitrule == "maxstat") { if (treetype == 5 || treetype == 3) { splitrule.num <- 4 } else { stop("Error: maxstat splitrule applicable to regression or survival data only.") } } else if (splitrule == "extratrees") { splitrule.num <- 5 } else { stop("Error: Unknown splitrule.") } if (alpha < 0 || alpha > 1) { stop("Error: Invalid value for alpha, please give a value between 0 and 1.") } if (minprop < 0 || minprop > 0.5) { stop("Error: Invalid value for minprop, please give a value between 0 and 0.5.") } if (!is.numeric(num.random.splits) || num.random.splits < 1) { stop("Error: Invalid value for num.random.splits, please give a positive integer.") } if (splitrule.num == 5 && save.memory && respect.unordered.factors == "partition") { stop("Error: save.memory option not possible in extraTrees mode with unordered predictors.") } if (respect.unordered.factors == "partition") { names.selected <- names(data.selected) ordered.idx <- sapply(data.selected, is.ordered) factor.idx <- sapply(data.selected, is.factor) independent.idx <- names.selected != dependent.variable.name & names.selected != status.variable.name unordered.factor.variables <- names.selected[factor.idx & !ordered.idx & independent.idx] if (length(unordered.factor.variables) > 0) { use.unordered.factor.variables <- TRUE num.levels <- sapply(data.selected[, factor.idx & !ordered.idx & independent.idx, drop = FALSE], nlevels) max.level.count <- .Machine$double.digits if (max(num.levels) > max.level.count) { stop(paste("Too many levels in unordered categorical variable ", unordered.factor.variables[which.max(num.levels)], ". Only ", max.level.count, " levels allowed on this system. Consider using the 'order' option.", sep = "")) } } else { unordered.factor.variables <- c("0", "0") use.unordered.factor.variables <- FALSE } } else if (respect.unordered.factors == "ignore" || respect.unordered.factors == "order") { unordered.factor.variables <- c("0", "0") use.unordered.factor.variables <- FALSE } else { stop("Error: Invalid value for respect.unordered.factors, please use 'order', 'partition' or 'ignore'.") } if (use.unordered.factor.variables && !is.null(splitrule)) { if (splitrule == "maxstat") { stop("Error: Unordered factor splitting not implemented for 'maxstat' splitting rule.") } else if (splitrule %in% c("C", "auc", "C_ignore_ties", "auc_ignore_ties")) { stop("Error: Unordered factor splitting not implemented for 'C' splitting rule.") } } if (respect.unordered.factors == "order") { if (treetype == 3 && splitrule == "maxstat") { warning("Warning: The 'order' mode for unordered factor handling with the 'maxstat' splitrule is experimental.") } if (gwa.mode & ((treetype %in% c(1, 9) & nlevels(response) > 2) | treetype == 5)) { stop("Error: Ordering of SNPs currently only implemented for regression and binary outcomes.") } } prediction.mode <- FALSE predict.all <- FALSE prediction.type <- 1 loaded.forest <- list() if ("dgCMatrix" %in% class(data.final)) { sparse.data <- data.final data.final <- matrix(c(0, 0)) use.sparse.data <- TRUE } else { sparse.data <- Matrix(matrix(c(0, 0))) use.sparse.data <- FALSE } if (respect.unordered.factors == "order") { order.snps <- TRUE } else { order.snps <- FALSE } rm("data.selected") result <- rangerCpp(treetype, dependent.variable.name, data.final, variable.names, mtry, num.trees, verbose, seed, num.threads, write.forest, importance.mode, min.node.size, split.select.weights, use.split.select.weights, always.split.variables, use.always.split.variables, status.variable.name, prediction.mode, loaded.forest, snp.data, replace, probability, unordered.factor.variables, use.unordered.factor.variables, save.memory, splitrule.num, case.weights, use.case.weights, class.weights, predict.all, keep.inbag, sample.fraction, alpha, minprop, holdout, prediction.type, num.random.splits, sparse.data, use.sparse.data, order.snps, oob.error, max.depth, inbag, use.inbag) if (length(result) == 0) { stop("User interrupt or internal error.") } if (importance.mode != 0) { names(result$variable.importance) <- all.independent.variable.names } if (treetype == 1 && is.factor(response) && oob.error) { result$predictions <- integer.to.factor(result$predictions, levels(response)) true.values <- integer.to.factor(unlist(data.final[, dependent.variable.name]), levels(response)) result$confusion.matrix <- table(true.values, result$predictions, dnn = c("true", "predicted"), useNA = "ifany") } else if (treetype == 5 && oob.error) { if (is.list(result$predictions)) { result$predictions <- do.call(rbind, result$predictions) } if (is.vector(result$predictions)) { result$predictions <- matrix(result$predictions, nrow = 1) } result$chf <- result$predictions result$predictions <- NULL result$survival <- exp(-result$chf) } else if (treetype == 9 && !is.matrix(data) && oob.error) { if (is.list(result$predictions)) { result$predictions <- do.call(rbind, result$predictions) } if (is.vector(result$predictions)) { result$predictions <- matrix(result$predictions, nrow = 1) } colnames(result$predictions) <- unique(response) if (is.factor(response)) { result$predictions <- result$predictions[, levels(droplevels(response)), drop = FALSE] } } result$splitrule <- splitrule if (treetype == 1) { result$treetype <- "Classification" } else if (treetype == 3) { result$treetype <- "Regression" } else if (treetype == 5) { result$treetype <- "Survival" } else if (treetype == 9) { result$treetype <- "Probability estimation" } if (treetype == 3) { result$r.squared <- 1 - result$prediction.error/var(response) } result$call <- sys.call() result$importance.mode <- importance result$num.samples <- nrow(data.final) result$replace <- replace if (write.forest) { if (is.factor(response)) { result$forest$levels <- levels(response) } result$forest$independent.variable.names <- independent.variable.names result$forest$treetype <- result$treetype class(result$forest) <- "ranger.forest" if (respect.unordered.factors == "order" && !is.matrix(data)) { result$forest$covariate.levels <- covariate.levels } } class(result) <- "ranger" if (quantreg) { terminal.nodes <- predict(result, data, type = "terminalNodes")$predictions + 1 n <- result$num.samples result$random.node.values <- matrix(nrow = max(terminal.nodes), ncol = num.trees) for (tree in 1:num.trees) { idx <- sample(1:n, n) result$random.node.values[terminal.nodes[idx, tree], tree] <- response[idx] } if (!is.null(result$inbag.counts)) { inbag.counts <- simplify2array(result$inbag.counts) random.node.values.oob <- 0 * terminal.nodes random.node.values.oob[inbag.counts > 0] <- NA for (tree in 1:num.trees) { is.oob <- inbag.counts[, tree] == 0 num.oob <- sum(is.oob) if (num.oob != 0) { oob.obs <- which(is.oob) oob.nodes <- terminal.nodes[oob.obs, tree] for (j in 1:num.oob) { idx <- terminal.nodes[, tree] == oob.nodes[j] idx[oob.obs[j]] <- FALSE random.node.values.oob[oob.obs[j], tree] <- save.sample(response[idx], size = 1) } } } minoob <- min(rowSums(inbag.counts == 0)) if (minoob < 10) { stop("Error: Too few trees for out-of-bag quantile regression.") } result$random.node.values.oob <- t(apply(random.node.values.oob, 1, function(x) { sample(x[!is.na(x)], minoob) })) } } return(result) })(dependent.variable.name = "Species", data = iris2)
#>
#> Type: Classification
#> Number of trees: 500
#> Sample size: 30
#> Number of independent variables: 4
#> Mtry: 2
#> Target node size: 1
#> Variable importance mode: none
#> Splitrule: gini
#> OOB prediction error: 3.33 %
# Gmisc fastDoCall (cleaner and faster)
Gmisc::fastDoCall(ranger, args)
#> Registered S3 methods overwritten by 'ggplot2':
#> method from
#> [.quosures rlang
#> c.quosures rlang
#> print.quosures rlang
#> Ranger result
#>
#> Call:
#> ranger(dependent.variable.name = dependent.variable.name, data = data)
#>
#> Type: Classification
#> Number of trees: 500
#> Sample size: 30
#> Number of independent variables: 4
#> Mtry: 2
#> Target node size: 1
#> Variable importance mode: none
#> Splitrule: gini
#> OOB prediction error: 3.33 %
答案 2 :(得分：0)
rlang::invoke为我工作。
不推荐使用exec，但仍然可以使用。