Multiple Imputation inferencemitools
本文介绍一种可利用整个数据集的方法——多重插补(Multiple Imputation, MI)。 多重插补是一种处理缺失值的方法,它使用模型估计和重复模拟来生成一组完整的数据集。每个数据集中的缺失数据会通过估计模型的方法进行填补。 本文使用R语言中的mice包来执行这些操作,首先来看mice包的操作流程: ...
随机数种子设为1234，插补次数设定为5次，这样我们就能看到imp$imp$Span中生成的插补值。而complete()函数则是查看每个完整数据集的窗口，例如，我们可以观察到第2个完整数据集的独特面貌，即dataset2。在实践中，Kabacoff (2015)的《R in Action: Data Analysis and Graphics with R》(第二版)是值得...
multiple imputationmodel diagnosticschained equationsweakly informative priormiOur mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, ...
Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research. BMC Med Res Methodol. 2012; 12 (1):184.Hardt J, Herke M, Leonhart R. Auxiliary variables in multiple imputation in regression with missing X: A warning ...
Our article (by Yu-Sung, Jennifer, Masanao, and myself, and based also on work with Kobi, Grazia, and Peter Messeri) will be appearing in the Journal of Statistical Software, in a special issue on missing-data imputation. Here's the abstract: ...
Donald B. RubinElizabeth R. ZellCHANCEBaccini, M., S. Cook, C. Frangakis, F. Li, F. Mealli, D. Rubin, and E. Zell (2010): "Multiple imputation in the anthrax vaccine research program," CHANCE, 23, 16-23, 10.1007/s00144-010-0004-3....
Survival analysis using auxiliary variables via non-parametric multiple imputation We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variables to r... CH Hsu,JMG Taylor,S Murray,... - 《Statistics in Medicine》...
I have successfully completed a multiple imputation on the missing data of my questionnaire research using the MICE package in R and performed a linear regression on the pooled imputed variables. I can't seem to work out how to extract single pooled variables and plot in a graph. Any ideas...
Fortunately, there are methods available to get around the problem of missing data, for example imputation. So we can predict and fill in missing values before implementing any method to analyse the multi-phenotype data. Additionally, an R package, pcaMethods, allows performing PCA on incomplete ...