partition.cv.strat(sperrorest)
partition.cv.strat()所属R语言包：sperrorest
Partition the data for a stratified (non-spatial) cross-validation
一个分层的（非空间）交叉验证对分区的数据。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
partition.cv.strat creates a set of sample indices corresponding to cross-validation test and training sets.
partition.cv.strat创建一组对应的交叉验证测试和训练集的样本指数。
用法----------Usage----------
partition.cv.strat(data, coords = c("x", "y"),
nfold = 10, return.factor = FALSE, repetition = 1,
seed1 = NULL, strat)
参数----------Arguments----------
参数：coords
vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations
向量，长度为2data包含的x和y坐标的样本的位置定义的变量在
参数：strat
character: column in data containing a factor variable over which the partitioning should be stratified; or factor vector of length nrow(data): variable over which to stratify
字符列在data包含的一个因素变量的分区应该是分层或系数向量的长度nrow(data)：可变的分层
参数：data
data.frame containing at least the columns specified by coords
data.frame的至少包含列指定的coords
参数：nfold
number of partitions (folds) in nfold-fold cross-validation partitioning
在nfold倍的交叉验证分区的分区数（倍）
参数：return.factor
if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)
如果FALSE（默认），返回一个represampling对象，如果TRUE（内部使用其他sperrorest功能的），返回一个list因子矢量（见值）
参数：repetition
numeric vector: cross-validation repetitions to be generated. Note that this is not the number of repetitions, but the indices of these repetitions. E.g., use repetition=c(1:100) to obtain (the 'first') 100 repetitions, and repetition=c(101:200) to obtain a different set of 100 repetitions.
数字向量：交叉验证的重复产生。注意，这是不重复的次数，但这些重复的指数。例如，使用repetition=c(1:100)（“第一个”）取得100个重复，和repetition=c(101:200)获得一组不同的重复100次。
参数：seed1
seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.
seed1+i是随机的种子，将用于set.seed重复i（irepetition）采样前的数据初始化随机数生成器设置。
值----------Value----------
A represampling object, see also partition.cv. partition.strat.cv, however, stratified with respect to the variable data[,strat]; i.e., cross-validation partitioning is done within each set data[data[,strat]==i,] (i in levels(data[,strat])), and the ith folds of all levels are combined into one cross-validation fold.
Arepresampling对象，也看到partition.cv。 partition.strat.cv，然而，分层变量data[,strat]“，即，交叉验证分区内完成每一组data[data[,strat]==i,]（ilevels(data[,strat])），和i次褶皱各级组合成一个交叉验证倍。
参见----------See Also----------
sperrorest, as.resampling, resample.strat.uniform
sperrorest，as.resampling，resample.strat.uniform
实例----------Examples----------
data(ecuador)
parti = partition.cv.strat(ecuador, strat = "slides", nfold = 5, repetition = 1)
idx = parti[["1"]][[1]]$train
mean(ecuador$slides[idx]=="TRUE") / mean(ecuador$slides=="TRUE")
# always == 1[总是== 1]
# Non-stratified cross-validation:[非分层交叉验证：]
parti = partition.cv(ecuador, nfold = 5, repetition = 1)
idx = parti[["1"]][[1]]$train
mean(ecuador$slides[idx]=="TRUE") / mean(ecuador$slides=="TRUE")
# close to 1 because of large sample size, but with some random variation[因为大样本的大小接近1，但也有一些随机的变化]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 15:02:58
