Quantitative Methods Workshop. Graphical Methods for Investigating Missing Data

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Background Missing data analysis inR rggobi: Data Visualistion Amelia: a program for missing data conclusion

Quantitative Methods Workshop

Graphical Methods for Investigating Missing Data

Graeme Hutcheson

School of Education University of Manchester

Graeme D. Hutcheson Manchester University

missing data data imputation

missing data

I Data sets with missing values are very common in the social sciences.

I Missing data is commonly ‘dealt with’ by using:

I list-wise deletion

I simple data replacement (random values, mean values or values predicted directly from regression models)

I removing variables with relatively large amounts of missing data from the analysis.

None of these techniques is adequate.

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King et al., 2001: American Political Science Review

‘...approximately 94% (of analyses) use listwise deletion to

eliminate entire observations... List-wise deletion discards one-third of cases on average, which deletes both the few nonresponses and the many responses in those cases. The result is a loss of valuable information at best and severe selection bias at worst.’

Objectives

I Even though missing data is important, it is rarely dealt with or even acknowledged in educational research. Why is this?

I There is a general ignorance as to the damaging effects that missing data can have on analyses.

I There is a lack of training about imputation techniques and available ‘useable’ software.

I There is a general reluctance from reviewers to accept data imputation (without detailed justifications they are often make

‘easy targets’ for criticism).

I Data imputation is not easy and should not be achieved by

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data imputation

I Data imputation is now accepted (particularly multiple imputation), but has been very slow to be adopted by researchers.

I The reason for this is only in part a lack of information and training. A bigger issue is that... in practice it can take many hours or days to run and cannot be fully automated.... no commercial software includes a correct implementation of multiple imputation (King et al., 2001).

introduction R: programs conclusion

Missing data analysis in R

I The problem of software is now being addressed by the R package. Researchers working on a number of techniques over the last decade now have a platform on which to publish their software. This has led, in the last year or so, to many

techniques becoming accessible to researchers.

I A simple search for data imputation and missing data on CRAN shows the following (a selection of results are provided - note that these are only the packages that have the target words in the title):

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Missing data analysis in R

I Amelia II: A Program for Missing Data

I arrayImpute: Missing imputation for microarray data

I cat: Analysis of categorical-variable datasets with missing values

I EMV: Estimation of Missing Values for a Data Matrix

I impute: Imputation for microarray data

I mi: Missing Data Imputation and Model Checking

I mice: Multivariate Imputation by Chained Equations

Missing data analysis in R

I mirf: Multiple imputation and random forests for unobservable phase, high-dimensional data.

I mitools: Tools for multiple imputation of missing data

I mix: Estimation/multiple Imputation for Mixed Categorical and Continuous Data

I pan: Multiple imputation for multivariate panel or clustered data

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Missing data analysis in R

I rggobi: Interface between R and GGobi (missing data tools)

I SeqKnn: Sequential KNN imputation method

I SimHap: A comprehensive modeling framework for

epidemiological outcomes and a multiple-imputation approach to haplotypic analysis of population-based data

I VIM: Visualization and Imputation of Missing Values

I yaImpute: An R Package for k-NN Imputation

R add-on packages

I This is an exciting time in statistics and data analysis as these techniques are only now being made available to all

researchers (most of these programs have been uploaded in the last year).

I Many of the packages listed above also have point-and-click interfaces which makes them simple to operate (see, for example, rggobi, VIM, Amelia) and all have comprehensive manuals available for download from CRAN.

I This seminar will briefly demonstrate two packages rggobi, a data visualization package and Amelia II, a data imputation package.

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missing data shown in margin plots visualising missing data

Imputing data

checking imputed data (simple imputation) checking imputed data (multiple imputation)

rggobi: A data visualistion program

Full details of ‘rggobi’ can be found at:

http://www.ggobi.org/rggobi

Information about R and installing packages can be found at:

http://www.r-project.org http://www.rgsweb.net

Imputing data

rggobi: A data visualistion program

The following analyses are taken directly from the ggobi website http://www.ggobi.org/ and the book:

Cook, D. and Swayne, D. F. (2007). Interactive and Dynamic Graphics for Data Analysis: With Examples Using R and GGobi.

Springer.

The data show environmental readings for two years (an el-nino year (1997) and a non-el-nino year (1993)).

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Imputing data

missing data shown in margin plots

Imputing data

rggobi: visualising missing data

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Imputing data

rggobi: imputing data

Imputing data

rggobi: imputing data

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Imputing data

rggobi: checking imputed data (simple imputation)

Imputing data

rggobi: checking imputed data (multiple imputation)

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introduction GUI input options output

Multiple imputation

I Methodologists and statisticians agree that ‘multiple

imputation’ is a superior approach to the problem of missing data scattered through ones explanatory and dependent variables than the methods currently used in applied data analysis (King et al., 2001: American Political Science Review).

I Amelia II is a package that implements a sophisticated multiple imputation of missing data and also allows diagnostics to assess the utility of the imputed data.

Amelia: a simple GUI

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Amelia: data input

Amelia: options - variables

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Amelia: options - Time Series/Cross Sectional data

With amelia, time series and cross-sectional indices can be set.

Researchers often also have additional prior information about missing data values based on previous research, academic consensus, or personal experience. This information can be

incorporated into the data imputation algorithm to produce vastly improved imputations.

Amelia: options - priors

Case priors and distributional priors can be easily coded using the GUI.

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Amelia: output

The multiple imputed data files can be saved in a number of formats.

Amelia: diagnostics - comparing imputed and observed

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Amelia: diagnostics - overimputation

Amelia and rggobi

The values imputed using amelia can easily be saved and inspected using the graphical capabilities of rggobi. Data can be multiply imputed and also checked graphically for fit.

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King et al., 2001: American Political Science Review

‘For political scientists, almost any disciplined statistical model of multiple imputation would serve better than current practices. The threats to the validity of inferences from listwise deletion are of roughly the same magnitude as those from the much better known problems of omitted variable bias.’

Conclusion

I That was just 2 of the many programs available for data imputation and missing data analysis.

I If you are interested in missing data analysis, investigate the available packages (see the manuals) and install those that might be of use.

I see www.RGSweb.net (for data coding and general information about R).