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matchit is the main function of MatchIt and performs pairing, subset selection, and subclassification with the aim of creating treatment and control groups balanced on included covariates. MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods.

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. In a previous post, I demonstrated how to create a propensity score matching, test balance, and analyze the outcome variable using the optmatch and RItools packages. The same strategy can be used with other matching algorithms, for example the various methods included in the MatchIt package..

Matchit package

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MatchIt package - RDocumentation. Selects matched samples of the original treated andcontrol groups with similar covariate distributions -- can beused to match exactly on covariates, to match on … matchit is the main function of MatchIt and performs pairing, subset selection, and subclassification with the aim of creating treatment and control groups balanced on included covariates. MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt provides a simple and straightforward interface to various methods of matching for covariate balance in observational studies. Matching is one way to reduce confounding and model dependence when estimating treatment effects.

Thepackage also implements a series of recommendations offered inHo, Imai, King, MatchIt reduces the dependence of causal inferences on commonly made, but hard-to-justify, statistical modeling assumptions using a large range of sophisticated matching methods. The package includes several popular approaches to matching and provides access to methods implemented in other packages through its single, unified, and easy-to-use interface. MatchIt provides a simple and straightforward interface to various methods of matching for covariate balance in observational studies. Matching is one way to reduce confounding and model dependence when estimating treatment effects. So, conveniently the R matchit propensity score matching package comes with a subset of the Lalonde data set referenced in MHE. Based on descriptives, it looks like this data matches columns (1) and (4) in table 3.3.2. MatchIt works in conjunction with the R programming language and statistical software R Development Core Team(2011) and will run on any platform where R is installed (Windows, Unix, or Mac OS X). MatchIt is available from the Comprehensive R Archive Network at http://CRAN.R-project.org/package=MatchIt.
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I am not sure whether you can do age +/-5. One possibility is grouping the age and doing exact matching.

It also calculates ratio of variances and ratio of residual variances. In addition, it draws SMD comparison plots and QQ 2014-11-03 · In this post, we will perform first steps with the MatchIt package. First, we have to make sure that R and ideally RStudio is already installed. Then, we can install MatchIt via the command (or RStudio -> packages -> Install -> MatchIt).
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MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. In addition to the tools available in matchIt, the cobalt package has a suite of functions designed to assess and display balance and is directly compatible with MatchIt objects.

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(During the installation process you may either decide to keep or discard the installation files, which will not affect the way M ATCH I T runs.) This function set is supplementary to 'MatchIt'. Its functions conduct several computations that 'MatchIt' does not provide. It takes the 'MatchIt' result object and/or the original data to compute standardized mean differences (SMD) between groups before and after matching. It also calculates ratio of variances and ratio of residual variances.