- Referenced in 806 articles
- ARFIMA, business calendars, chained equations for multiple imputation, contour plots, automatic memory management, importing...
- Referenced in 132 articles
- package mice: Multivariate Imputation by Chained Equations. Multiple imputation using Fully Conditional Specification (FCS) implemented ... MICE algorithm. Each variable has its own imputation model. Built-in imputation models are provided ... categorical data (proportional odds). MICE can also impute continuous two-level data (normal model ... second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic...
- Referenced in 100 articles
- with a few missing values. Methods for imputing missing data are needed, therefore, to minimize ... sets, and assessed the robustness of the imputation methods to the amount of missing data...
- Referenced in 63 articles
- approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses...
- Referenced in 60 articles
- estimates of model parameters. Offers various data imputation methods—to create different data sets...
- Referenced in 35 articles
- Program for Missing Data , Amelia II ”multiply imputes” missing data in a single cross-section ... Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please ... useful diagnostics of the fit of multiple imputation models. The program works from...
- Referenced in 37 articles
- Computing asymptotic covariance matrices of sample moments; Imputation by matching; Multiple imputation; Multiple linear regression...
- Referenced in 26 articles
- Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black ... allow the user to get inside the imputation process and evaluate the reasonableness ... resulting models and imputations. These features include: choice of predictors, models, and transformations for chained ... imputation models; standard and binned residual plots for checking the fit of the conditional distributions...
- Referenced in 29 articles
- package missForest: Nonparametric Missing Value Imputation using Random Forest. The function ’missForest’ in this package ... used to impute missing values particularly in the case of mixed-type data. It uses ... missing values. It can be used to impute continuous and/or categorical data including complex interactions ... yields an out-of-bag (OOB) imputation error estimate without the need of a test...
- Referenced in 41 articles
- computing sample size and power, importing datasets, imputing missing values, advanced table making, variable clustering...
- Referenced in 17 articles
- Imputation in the survey on living conditions. The Spanish “European Statistics on Income and Living ... income components. Components with missing values are imputed when they cannot be estimated with ... between them. The statistical software used for imputation is the IVEware. The IVEware implements ... model involving a multiple regression sequence where imputation is carried out variable by variable generating...
- Referenced in 32 articles
- their patterns can be examined; multiple imputations can be given to XGobi for rapid visual...
- Referenced in 22 articles
- Some functions can also be used to impute missing values in data sets through ... deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample...
- Referenced in 16 articles
- package VIM: Visualization and Imputation of Missing Values. This package introduces new tools ... visualization of missing and/or imputed values, which can be used for exploring the data ... structure of the missing and/or imputed values. Depending on this structure of the missing values ... missing values. In addition, the quality of imputation can be visually explored using various univariate...
- Referenced in 15 articles
- with/in multivariate data analysis (principal component methods). Imputation of incomplete continuous or categorical datasets; Missing ... values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model ... multiple factor analysis (MFA) model; Perform multiple imputation with...
- Referenced in 13 articles
- Multiple Imputation by Chained Equations (MICE): Implementation in Stata. Missing data are a common occurrence ... prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models ... Stata of the MICE approach to multiple imputation. Real data from an observational study ... database architecture and procedures for multiple imputation introduced in releases 11 and 12 of Stata...
- Referenced in 11 articles
- IMPUTE is a program for estimating (”imputing”) unobserved genotypes in SNP association studies. The program ... currently three different versions of the IMPUTE software available for download: version 0.5 implements...
- Referenced in 13 articles
- algorithm), Haley-Knott regression, and multiple imputation. All of this may be done ... higher-order QTL models by multiple imputation and Haley-Knott regression. R/qtl is distributed...
- Referenced in 9 articles
- REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types. Multiple imputation is becoming ... model of interest, but also in the imputation model. In particular, the imputation model should ... This led us to develop the REALCOM-IMPUTE software, which we describe in this article ... This software performs multilevel multiple imputation, and handles ordinal and unordered categorical data appropriately...
- Referenced in 10 articles
- Multiple imputation of missing values. .. This article describes five ado-files. mvis creates multiple multivariate ... imputations. uvis imputes missing values for a single variable as a function of several covariates ... variety of regression models to a multiply imputed dataset, combining the estimates using Rubin...