runMI(semTools)
runMI()所属R语言包：semTools
Multiply impute and analyze data using lavaan
乘推诿和分析数据，使用lavaan
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function takes data with missing observations, multiple imputes the data, runs a SEM using lavaan and combines the results using Rubin's rules.
此功能需要的数据缺失观察多种责难的数据，使用lavaan运行的SEM和鲁宾的规则相结合的结果。
用法----------Usage----------
runMI(data.mat,data.model, m, miPackage="Amelia", digits=3, seed=12345,
std.lv = FALSE, estimator = "ML", group = NULL, group.equal = "", ...)
参数----------Arguments----------
参数：data.mat
Data frame with missing observations or a list of data frames where each data frame is one imputed data set (for imputed data generated outside of the function). If a list of data frames is supplied, then other options can be left at the default.
失踪的意见或数据框的列表，其中的每个数据框是一个估算的数据集（估算数据的功能外）的数据框。如果提供的数据框，然后其他选项可以保留为默认。
参数：data.model
lavaan syntax for the the model to be analyzed.
lavaan语法的模型来进行分析。
参数：m
Number of imputations wanted.
数的估算想要的。
参数：miPackage
Package to be used for imputation. Currently runMI only uses Amelia or mice for imputation.
用于归集的包装。 ，目前runMI使用阿米莉亚或小鼠进行归集。
参数：digits
Number of digits to print in the results.
数位打印的结果。
参数：seed
Random number seed to be used in imputations.
要使用的随机数种子的插补。
参数：std.lv
lavaan option. If TRUE, the metric of each latent variable is determined by fixing their variances to 1.0. If FALSE, the metric of each latent variable is determined by fixing the factor loading of the first indicator to 1.0.
lavaan选项。如果是TRUE，每个潜变量的度量值是由固定方差为1.0。如果为FALSE，每个潜变量的度量值是由固定的因子载荷的第一个指标为1.0。
参数：estimator
lavaan option. The estimator to be used. Can be one of the following: "ML" for maximum likelihood, "GLS" for generalized least squares, "WLS" for weighted least squares (sometimes called ADF estimation), "MLM" for maximum likelihood estimation with robust standard errors and a Satorra-Bentler scaled test statistic, "MLF" for maximum likelihood estimation with standard errors based on first-order derivatives and a conventional test statistic, "MLR" for maximum likelihood estimation with robust 'Huber-White' standard errors and a scaled test statistic which is asymptotically equivalent to the Yuan-Bentler T2-star test statistic. Note that the "MLM", "MLF" and "MLR" choices only affect the standard errors and the test statistic.
lavaan选项。要使用该估计。可以是下列之一：“ML”最大似然法，广义最小二乘的“GLS”，“WLS”加权最小二乘法（有时称为ADF估计），“传销”最大似然估计的稳健标准错误和Satorra特勒规模测试统计，“多边基金资助的”基于一阶导数和常规的检验统计量，“国土资源部”强大的“胡贝尔白色的标准误差的最大似然估计的最大似然估计标准误差是渐近等价的元特勒T2-星级检验统计量和检验统计量的比例。需要注意的是“传销”，“MLF”和“MLR”的选择只影响的标准误差及检验统计量。
参数：group
lavaan option. A variable name in the data frame defining the groups in a multiple group analysis.
lavaan选项。在多组分析数据框中的一个变量名定义的组。
参数：group.equal
lavaan option. A vector of character strings. Only used in a multiple group analysis. Can be one or more of the following: "loadings", "intercepts", "means", "regressions", "residuals", "residual.covariances", "lv.variances" or "lv.covariances", specifying the pattern of equality constraints across multiple groups.
lavaan选项。一个字符串向量。仅用于多组分析。可以是一个或多个以下操作：“负荷”，“截获”，“装置”，“回归”，“残差”，“residual.covariances”，“lv.variances”或“ lv.covariances“，指定在多个组的等式约束的格局。
参数：...
Other arguments to be passed to the imputation package
其他参数被传递到归集包
值----------Value----------
runMI returns a list with pooled fit indices, estimates, standard errors and fraction missing information.
runMI返回一个列表，汇集拟合指数，估计标准误差和部分丢失的信息。
参数：fit
Pooled fit information. The first set of fit information are simply averaged across imputations and are not trustworthy. The second set of fit information, is a pooled Chi-square statistic based on Li, Meng, Raghunathan, & Rubin (1991)
汇集合适的信息。第一套合适的信息仅仅是平均的估算，是不值得信任的。第二组合适的信息，是一个汇集了卡方统计的基础上李猛，35 - 鲁宾（1991）
参数：parameters
Pooled parameter estimates and standard errors. Wald statistics and p values are computed from the pooled estimates and standard errors. Also contains two estimates of Fraction of Missing Information (FMI). The first estimate of FMI (FMI.1) is asymptotic FMI and the second estimate of FMI (FMI.2) is corrected for small numbers of imputation
汇集参数估计值和标准差。 Wald统计量和p值的计算从汇集的估计和标准差。还包含两个分数的估计丢失信息（FMI）。第一次估算FMI（FMI.1）的是渐进FMI和FMI（FMI.2）的第二次估计的修正小的数字估算
（作者）----------Author(s)----------
Alexander M. Schoemann (University of Kansas; <a href="mailto:schoemann@ku.edu">schoemann@ku.edu</a>)
Patrick Miller (University of Kansas; <a href="mailto:patr1ckm@ku.edu">patr1ckm@ku.edu</a>)
Sunthud Pornprasertmanit (University of Kansas; <a href="mailto:psunthud@ku.edu">psunthud@ku.edu</a>)
Mijke Rhemtulla (University of Kansas; <a href="mailto:mijke@ku.edu">mijke@ku.edu</a>)
Alexander Robitzsch (Federal Institute for Education Research, Innovation, and Development of the Austrian School System, Salzburg, Austria; <a href="mailto:a.robitzsch@bifie.at">a.robitzsch@bifie.at</a>)
Craig Enders (Arizona State University; <a href="mailto:Craig.Enders@asu.edu">Craig.Enders@asu.edu</a>)
Mauricio Garnier Villarreal (University of Kansas; <a href="mailto:mgv@ku.edu">mgv@ku.edu</a>)
参考文献----------References----------
实例----------Examples----------
library(lavaan)
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
HSMiss <- HolzingerSwineford1939[,paste("x", 1:9, sep="")]
randomMiss <- rbinom(prod(dim(HSMiss)), 1, 0.1)
randomMiss <- matrix(as.logical(randomMiss), nrow=nrow(HSMiss))
HSMiss[randomMiss] <- NA
out <- runMI(HSMiss, HS.model, m = 3)
HSMiss2 <- cbind(HSMiss, school = HolzingerSwineford1939[,"school"])
out2 <- runMI(HSMiss2, HS.model, m = 3, group="school", noms="school")
library(Amelia)
modsim <- '
f1 =~ 0.7*y1+0.7*y2+0.7*y3
f2 =~ 0.7*y4+0.7*y5+0.7*y6
f3 =~ 0.7*y7+0.7*y8+0.7*y9'
mod <- '
f1 =~ y1+y2+y3
f2 =~ y4+y5+y6
f3 =~ y7+y8+y9'
datsim <- simulateData(modsim,model.type="cfa", meanstructure=TRUE,
std.lv=TRUE, sample.nobs=c(200,200))
randomMiss2 <- rbinom(prod(dim(datsim)), 1, 0.1)
randomMiss2 <- matrix(as.logical(randomMiss2), nrow=nrow(datsim))
datsim[randomMiss2] <- NA
datsimMI <- amelia(datsim,m=3, noms="group")
out3 <- runMI(datsimMI$imputations, mod, group="group")
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 00:46:38
