Understanding and using time series analyses in addiction research
Time series analyses are statistical methods used to assess trends in repeated
measurements taken at regular intervals and their associations with other trends or events …
measurements taken at regular intervals and their associations with other trends or events …
missMDA: a package for handling missing values in multivariate data analysis
We present the R package missMDA which performs principal component methods on
incomplete data sets, aiming to obtain scores, loadings and graphical representations …
incomplete data sets, aiming to obtain scores, loadings and graphical representations …
Imputation with the R Package VIM
Abstract The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to
explore and analyze the structure of missing values in data using visualization methods, to …
explore and analyze the structure of missing values in data using visualization methods, to …
mice: Multivariate imputation by chained equations in R
S Van Buuren, K Groothuis-Oudshoorn - Journal of statistical software, 2011 - jstatsoft.org
The R package mice imputes incomplete multivariate data by chained equations. The
software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R …
software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R …
Imputation of missing data in life‐history trait datasets: which approach performs the best?
Despite efforts in data collection, missing values are commonplace in life‐history trait
databases. Because these values typically are not missing randomly, the common practice …
databases. Because these values typically are not missing randomly, the common practice …
Comparison of imputation methods for missing laboratory data in medicine
Objectives Missing laboratory data is a common issue, but the optimal method of imputation
of missing values has not been determined. The aims of our study were to compare the …
of missing values has not been determined. The aims of our study were to compare the …
Model checking in multiple imputation: an overview and case study
Background Multiple imputation has become very popular as a general-purpose method for
handling missing data. The validity of multiple-imputation-based analyses relies on the use …
handling missing data. The validity of multiple-imputation-based analyses relies on the use …
Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review
KL Masconi, TE Matsha, JB Echouffo-Tcheugui… - EPMA Journal, 2015 - Springer
Missing values are common in health research and omitting participants with missing data
often leads to loss of statistical power, biased estimates and, consequently, inaccurate …
often leads to loss of statistical power, biased estimates and, consequently, inaccurate …
Iterative stepwise regression imputation using standard and robust methods
Imputation of missing values is one of the major tasks for data pre-processing in many areas.
Whenever imputation of data from official statistics comes into mind, several (additional) …
Whenever imputation of data from official statistics comes into mind, several (additional) …
Improved methods for the imputation of missing data by nearest neighbor methods
G Tutz, S Ramzan - Computational Statistics & Data Analysis, 2015 - Elsevier
Missing data raise problems in almost all fields of quantitative research. A useful
nonparametric procedure is the nearest neighbor imputation method. Improved versions of …
nonparametric procedure is the nearest neighbor imputation method. Improved versions of …