imposeMissing(simsem)
imposeMissing()所属R语言包：simsem
Impose MAR, MCAR, planned missingness, or attrition on a data set
并处MCAR，MAR，计划missingness，或自然减员对数据集
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function imposes missing values on a data based on the known missing data types, including MCAR, MAR, planned, and attrition.
功能对数据的基础上已知的丢失的数据类型，包括MCAR，MAR，计划和磨损的遗漏值。
用法----------Usage----------
imposeMissing(data.mat, cov = 0, pmMCAR = 0, pmMAR = 0, nforms = 0,
itemGroups = 0, twoMethod = 0, prAttr = 0, timePoints = 1,
ignoreCols = 0, threshold = 0, logical = new("NullMatrix"))
参数----------Arguments----------
参数：data.mat
Data to impose missing upon. Can be either a matrix or a data frame.
数据实施后失踪。可以是一个矩阵或一个数据框。
参数：cov
Column indices of a covariate to be used to impose MAR missing, or MAR attrition. Will not be included in any removal procedure. See details.
列索引的协变量，用于强制MAR缺失，或MAR磨损。不包括在任何清除程序。查看详细信息。
参数：pmMCAR
Decimal percent of missingness to introduce completely at random on all variables.
十进制％的missingness介绍完全随机对所有的变量。
参数：pmMAR
Decimal percent of missingness to introduce using the listed covariate as predictor. See details.
十进制％的missingness介绍使用列出的协变量的预测。查看详细信息。
参数：nforms
The number of forms for planned missing data designs, not including the shared form.
计划丢失的数据设计的形式，但不包括共享的形式。
参数：itemGroups
List of lists of item groupings for planned missing data forms. Unless specified, items will be divided into groups sequentially (e.g. 1-3,4-6,7-9,10-12)
项目组计划丢失的数据形式的列表名单。除非特别指定，否则的项目将被分为组顺序（例如1-3,4-6,7-9,10-12）
参数：twoMethod
Vector of (column index, percent missing). Will put a given percent missing on that column in the matrix to simulate a two method planned missing data research design.
矢量（列的索引，缺少％）。会放一个给定的百分比在该列中缺少的矩阵两种方法来模拟一个计划丢失数据的研究设计。
参数：prAttr
Probability (or vector of probabilities) of an entire case being removed due to attrition at a given time point. When a covariate is specified along with this argument, attrition will be predicted by the covariate (MAR attrition). See details.
被删除的概率（或概率的向量）的整个壳体由于磨损在给定的时间点。当协变量一起指定此参数，减员预测的的协（MAR磨损）。查看详细信息。
参数：timePoints
Number of timepoints items were measured over. For longitudinal data, planned missing designs will be implemented within each timepoint. All methods to impose missing values over time assume an equal number of variables at each time point.
时间点项目的数量进行测量。纵向数据，将实施计划缺少的设计在每一个时间点。的所有方法来施加缺失值随着时间的推移，假设在每个时间点的相等数目的变量。
参数：ignoreCols
The columns not imposed any missing values for any missing data patterns.
列不施加任何缺失值的任何丢失的数据模式。
参数：threshold
The threshold of the covariate used to impose missing values. Values on the covariate above this threshold are eligible to be deleted. The default threshold is the mean of the variable.
阈值的协征收缺少的值。超过这个阈值的协的值有资格将被删除。默认的阈值的变量的平均值。
参数：logical
A matrix of logical values (TRUE/FALSE). If a value in the dataset is corresponding to the TRUE in the logical matrix, the value will be missing.
矩阵的逻辑值（TRUE/FALSE“）。如果在数据集中的值是对应的TRUE的逻辑矩阵，该值将丢失。
Details
详细信息----------Details----------
Without specifying any arguments, no missing values will be introduced.
不指定任何参数的情况下，没有缺失值将会推出。
A single covariate is required to specify MAR missing - this covariate can be distributed in any way. This covariate can be either continuous or categorical, as long as it is numerical. If the covariate is categorical, the threshold should be specified to one of the levels.
单一的协变量所需的指定MAR失踪的 - 这协可以以任何方式分发。此协变量可以是连续的或绝对的，只要它是数字。如果协变量是分类的，应指定的阈值，其中一个级别。
MAR missingness is specified using the threshold method - any value on the covariate that is above the specified threshold indicates a row eligible for deletion. If the specified total amount of MAR missingness is not possible given the total rows eligible based on the threshold, the function iteratively lowers the threshold until the total percent missing is possible.
，MAR missingness指定使用阈值法 - 这是上面指定的阈值表示要删除的行资格的协变量的任何值。如果指定的总的MAR missingness量是不可能的给定的总的行此基于阈值的，该函数迭代的阈值降低，直到总百分比缺少的是可能的。
Planned missingness is parameterized by the number of forms (n). This is used to divide the cases into n groups. If the column groupings are not specified, a naive method will be used that divides the columns into n+1 equal forms sequentially (1-4,5-9,10-13..), where the first group is the shared form.The first list of column indices given will be used as the shared group. If this is not desired, this list can be left empty.
计划missingness被参数化的表格数目（n）的。这是用来划分成n组的情况下。如果没有指定列分组，一个天真的方法将被用来为N +1等于形式顺序（1-4,5-9,10-13 ...），其中第一组是共享的形式分列。给定的列索引的第一列表中，将使用作为共享组。如果这是不希望的，这个列表可以为空。
For attrition, the probability can be specified as a single value or as a vector. For a single value, the probability of attrition will be the same across time points, and affects only cases not previously lost due to attrition. If this argument is a vector, this specifies different probabilities of attrition for each time point. Values will be recycled if this vector is smaller than the specified number of time points.
磨损，的概率可以被指定为一个单一的值，或作为一个向量。一个单一的值，消耗的概率是相同的时间点之间，只影响的情况下不丢失，由于磨损。如果该参数是一个向量，指定不同的概率，每个时间点的磨损。值将被再循环如果此向量小于指定数目的时间点。
An MNAR processes can be generated by specifying MAR missingness and then dropping the covariate from the subsequent analysis.
可以产生一种MNAR进程指定MAR missingness的，然后滴从随后的分析中的协变量。
Currently, if MAR missing is imposed along with attrition, both processes will use the same covariate and threshold.
目前，如果施加随着磨损MAR缺少的是，这两个进程会使用相同的协变量和阈值。
Currently, all types of missingness (MCAR, MAR, planned, and attrition) are imposed independently. This means that specified global values of percent missing will not be additive (10 percent MCAR + 10 percent MAR does not equal 20 percent total missing).
目前，所有类型的missingness（MCAR，MAR，计划和自然减员）独立适用。这意味着指定的全局％，缺失值将不会添加剂MCAR（10％+ 10％MAR不等于20％总丢失）。
值----------Value----------
A data matrix with NAs introduced in the way specified by the arguments.
一个数据矩阵与NA的介绍中的参数所指定的方式。
（作者）----------Author(s)----------
Patrick Miller(University of Kansas; <a href="mailto:patr1ckm@ku.edu">patr1ckm@ku.edu</a>)
Alexander M. Schoemann (University of Kansas; <a href="mailto:schoemann@ku.edu">schoemann@ku.edu</a>)
参见----------See Also----------
SimMissing for the alternative way to save missing data feature for using in the simResult function.
SimMissing的另一种方式，以节省使用simResult功能的缺失数据的功能。
runMI for imputing missing data by multiple imputation and analyze the imputed data.
runMI填充缺失数据的多重插补和分析估算数据。
实例----------Examples----------
data <- matrix(rep(rnorm(10,1,1),19),ncol=19)
datac <- cbind(data,rnorm(10,0,1),rnorm(10,5,5))
# Imposing Missing with the following arguments produces no missing values[征收失踪以下的参数没有缺失值]
imposeMissing(data)
imposeMissing(data,cov=c(1,2))
imposeMissing(data,pmMCAR=0)
imposeMissing(data,pmMAR=0)
imposeMissing(data,nforms=0)
#Some more usage examples[一些更多的使用范例]
imposeMissing(data,cov=c(1,2),pmMCAR=.1)
imposeMissing(data,nforms=3)
imposeMissing(data,nforms=3,itemGroups=list(c(1,2,3,4,5),c(6,7,8,9,10),c(11,12,13,14,15),c(16,17,18,19)))
imposeMissing(datac,cov=c(20,21),nforms=3)
imposeMissing(data,twoMethod=c(19,.8))
imposeMissing(datac,cov=21,prAttr=.1,timePoints=5)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 09:21:09
