4 . . 18 ; run; /* 使用PROC MI进行多重填补 */ proc mi data=example out=mi_datasets seed=12345; class id; /* 指定分类变量 */ var x y z; /* 指定需要填补的变量 */ numimputations 5; /* 指定填补的次数 */ run; /* 查看填补后的数据集 */ proc p
在近二、三十年来，多重填补(multiple imputation, MI)方法被认为是解决这一问题的首选方法，该方法由Donald B. Rubin 在20世纪70年代首先提出[1,2]。与通常用平均值代替缺失值或其它简单填补(simple imputation)方法的不同之处在于，MI 方法对每一个缺失值用一套可能的值进行填补，以反映缺失值的不确定性，...
Introduction There are plenty of methods that could be applied to the missing data, depending on the goal of the clinical trial. The most common and recommended is multiple imputation (MI), and other
The MNAR statement imputes missing values by using the pattern-mixture model approach, assuming the missing data are missing not at random (MNAR), which is described in the section Multiple Imputation with Pattern-Mixture Models. By comparing inferential results for these values to results for impu...
http://www.jstatsoft.org/Multiple Imputation Using SAS SoftwareYang YuanSAS Institute Inc.AbstractMultiple imputation provides a useful strategy for dealing with data sets that havemissing values. Instead of filling in a single value for each missing value, a multiple imputa-tion procedure replaces...
The tipping point analysis has been a useful sensitivity analysis for multiple imputation to assess the robustness of the deviations from the MCAR or MAR assumptions. It aims to find out how severe de
Then using the data set created by the multiple imputation, I create the factor scores. I've pasted the sequence of my code below: *Create imputed data set* proc mi data=l out=iexample nimpute=2 seed=9455;class f1exp f1rgpp2 mathhisp mathteach f3attain enghisp engteachmathtext math...
MULTIPLE Imputation of Missing Data Using SAS (Book)BERGLUND, PatriciaHEERINGA, StevenSAS (Computer program language)NONFICTIONNo abstract is available for this item.doi:10.1111/insr.12111_3MukhopadhyayPurnaJohn Wiley & Sons, Ltd.International Statistical Review...
Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical ...
Multiple imputation provides a useful strategy for dealing with data sets that have missing values. Instead of filling in a single value for each missing value, a multiple imputation procedure replaces each missing value with a set of plausible values that represent theuncertainty about the right ...