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: ...
总结来说，多重插补是处理缺失值的科学方法，而mice包则是R语言中的一项强大工具。通过熟练运用，我们能够在数据清洗的道路上更进一步，确保数据的完整性和准确性，为后续的数据分析和建模奠定坚实的基础。
One of the most popular methods for MI is MICE (Multiple Imputation by Chained Equations), which is an iterative algorithm that imputes each variable in turn, using the other variables as predictors. MICE can handle different types of variables, complex relationships, and interactions among variabl...
MICE AlgorithmMultiple imputations compensate for missing data and produce multiple datasets by regression model and are considered the solver of the old problem of univariate imputation. The univariate imputes data only from a specific column where the data cell was missing. Multivariate imputation ...
using multiple built-in imputation methods (i.e., pmm, cart, rf, norm, norm.nob, norm.boot, and norm.predict) of the mice package in R. The... HR Kim,H Soh,M Kwak,... - 《Water》 被引量: 0发表: 2022年 Applied Multiple Imputation In addition, the book features numerous practic...
MICE步骤如下: 步骤1:所有缺失值都初始化为常用的统计方法(例如,mean表示数字,mode表示分类)。这种填补可被视为“占位符”(临时值); 步骤2:逐列返回NA。将缺失值最少的变量(“var”)设置回缺少起始值; 步骤3:"var"是回归/分类模型中的因变量,所有其他变量是回归模型中的自变量; ...
R. (2011). Multiple imputation by chained equations (MICE): implementation in Stata. Journal of Statistical Software, 45(4), 1-20.Royston, P., & White, I. R. (2011). Multiple imputation by chained equations (MICE): Implemen- tation in Stata. Journal of Statistical Software, 45, 1-20...
Continue with the question in the previous article (Multiple Imputaton - Linear Regression in R), where we just discussed how to compute the pooled coefficients of ANCOVA using mice package but left o
For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in Stata of the MICE approach to multiple imputation. Real data ...
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...