Help matchit

For example, does taking aspirin daily reduce the chance of a heart attack? Does more sleep lead to better academic performance for teenagers? Does smoking increase the risk of chronic obstructive pulmonary disease COPD?

help matchit

To truly answer such questions, we need a time machine and a lack of ethics. We would need to be able to take a subject and, say, make him or her smoke for several years and observe whether or not they get COPD. To estimate the average effect of smoking on COPD, we would need to do the same with many subjects.

Clearly the laws of physics and all IRBs forbid this.

help matchit

This involves taking observational data, such as data from surveys, and matching people who have similar characteristics but different treatments. But I can identify two people who are similar in almost every way except their smoking status. It stands to reason the effects of smoking on the smoker can approximate the effect of smoking on the non-smoker, and vice versa. This is the idea of matching methods: create two such groups that are similar in every respect but their treatment.

In this brief article, we demonstrate how to get started with matching methods using R and the MatchIt package.

help matchit

To begin we need to load some data. The entire survey has overrecords and over variables. We wish to investigate the effect of smoking on COPD. Does smoking increase the chance of contracting COPD? Obviously this is not something we could carry out as a randomized experiment.

But perhaps using this observational data and matching methods we can simulate what our data might look like had we carried out a randomized experiment. We can create two groups, smokers and non-smokers, who are roughly equivalent in age, race, sex, weight and alcohol consumption. Please note this is only for educational purposes. While the data is real and the methods are sound, we would certainly include several other variables as well as enlist the help of doctors and epidemiologists to investigate a research question like this.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. The page of the matchit plugin says:. This enables filetype plugins, many of which tell matchit. I started having the same issue after I updated some of my vim plugings to the latest version for 7. This site helped me a lot.

The following steps, taken from this forum post written by this guysolved it for me and also pretty much explained the way the whole thing works:. I've had similar problem. So I've downloaded this script in version 1. Learn more. Matchit not working Ask Question.

Asked 8 years, 7 months ago. Active 1 year, 10 months ago. Viewed 12k times. I am using Macvim 7. I can't seem to get matchit to work in any of my files. It doesn't take me to the closing tag We only need " to configure it. Christian Fazzini. Christian Fazzini Christian Fazzini Active Oldest Votes.Login or Register Log in with. Forums FAQ. Search in titles only.

Posts Latest Activity. Page of 1. Filtered by:. Navid Asgari. Hi, I have two datasets each containing data on certain firms. I would like to merge the two datasets using the only available option: the name of the firms in the two datasets. Unfortunately, the spellings of firm names are different across the two datasets.

Therefor, I looked for a command in Stata that can match the string variables. I found the command -matchit- and tried it with its several options. But, it under-performs to the extent that it cannot match even the most obvious cases and sometimes it does the matching correctly.

I am not sure if I am making using the command correctly, because the names that I have are not terribly difficult to match. The first dataset has has two variables: idfocal codes idntifying a firmfocal string variable for the name of a firm The second dataset has two variables: idlicensor codes identifying a firmlicensor string variable for the name of firm Code:. Attached Files. Tags: matchitname matchingstring. Julio Raffo. Hi Navid, -matchit- is case sensitive.

That's why you're getting low scores for Genentech and Alk-abello. My suggestion would be to put everything lower or uppercase. If you think there are no misspellings in your name variables I suggest token as function. On the contrary case go for bigram. In both cases, I suggest using weights to limit the impact of the "inc", "Corp" and other less informative segments of the strings. In all cases have in mind that there are no miracles in string matching and sooner or later you need to get your hands dirty and learn to live with type I and II errors.

Comment Post Cancel. Andrew Lover. Navid, you might also try -reclink- from SSC ; I've had good luck in the past. That said, as Julio highlights, you may be forced to use 'hammer and tongs' and manually rename some. Moniek Bresser. Dear all, In most of the string similarity discussions, users are trying to find similarities between variables.

I however, would like to get a similarity score for observations within the same string variable. My data set contains more than person records and most likely there will be hundreds of people that occur in the data set multiple times, but with slightly different spelled names.

Do you have any experience with checking for similarity within the same variable and may I ask what package you decided using in the end? Thank you for sharing your experience! Best wishes, Moniek. Mike Lacy. I thought Moniek's question would have a simple answer, as the program -dtalink- from SSC has a "deduplication" mode, which seems to fit her situation exactly: Detect observations that are near duplicates of one another, based in this case on just one variable.

However, while the help for -dtalink- is extensive, I wasn't able to figure out how to apply it.Login or Register Log in with. Forums FAQ.

Causality - Inferring Causal Effects from Data - 3.2.3 - Propensity score matching in R

Search in titles only. Posts Latest Activity. Page of 3. Filtered by:. Julio Raffo. Matchit: new. Dear all, Let me share with you matchit which is an ado command I have just written. In a nutshell, matchit provides a similarity score between two different text strings by performing many different string-based matching techniques. These two variables can be from the same dataset or from two different ones. This latter option makes it a convenient tool to join observations when the string variables are not always exactly the same.

You can get it here: Code:. Last edited by Julio Raffo ; 12 Mar Comment Post Cancel. David Radwin. SuiteBerkeley, CA Phone: www. Hi David, You are right! Back then I did my homework and checked if someone has used "matchit" to name anything in Stata. But evidently R escaped my scrutiny. InI started coding in php the ancestor of my ado which I distributed as "match".

When I started adapting it to Stata last year, I decided to add the "it" to follow Stata's naming guidelines. But if people feel really strongly about it, I guess I should consider other options.

This is, of course, not my preferred solution. Kourosh Shafi. Laurence Lester. Any one any ideas please? Regards Laurence.Login or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. And can anybody see anything wrong with my reclink syntax? So I have one dataset with 7, institutions, and another with 2, individuals sometimes several in the same institution. Last edited by ben earnhart ; 18 Dec Tags: None. BTW -- this is Stata Both packages reclink and matchit are from SSC.

Comment Post Cancel. William Lisowski.

Subscribe to RSS

Any luck, Ben - any more dots emerge from matchit since you wrote? I'm dubious, but if nothing else works, it might be worth a try. Actually, what I might do in circumstances like yours would be eliminate the individuals who match using merge start with, say, 20 individuals to make sure there isn't some underlying problem It also occurs to me that if there were some a priori way of reducing the size of the Allinstitutions file you might speed things up.

Let us know what happens. I can imagine myself wanting something like this someday, it will be good to know your experience. You're right, I should have removed to perfect matches beforehand to minimize the job. I think I will stop it and start over with the cases that don't match. If it's non-linear, then that should help immensely. What took between five and thirteen hours just ran in about five minutes. Very interesting.

The author of matchitJulio Raffowhile not active on Statalist, did announce it here and has participated in discussions. If the pace you've established holds up, it might be interesting to ask him if having a high percentage of actual matches impedes flow of the algorithm; or if it would have been more effective with a different string matching method than the default.

It finished up, more slowly than it appeared at first, but much faster than doing the whole thing. The default setting is. So it's not a panacea, but it will save our brute-force-human matcher project assistant from about matches or so. I see that help matchit suggests that phonetic methods like Soundex are more efficient at matching misspellings based on similar sounds. If your data was collected by interviewers transcribing responses, that could help.

Might be worth a try on a small sample of your data. Thanks again for following up with your results. Julio Raffo. Apologies in advance, but I'm writing from my phone and without a great connection My first reaction is that I'm surprised that reclink didn't match anything.

It should at least replicate your --merge-- results. Concerning -matchit- performance, it is always worth remembering that it attempts to produce matching candidates through a computationally heavy process. So, yes, it can take some time depending on the size of the files and the similarity algorithm chosen. Of course your hardware also plays a role.MatchIt implements the suggestions of Ho, Imai, King, and Stuart 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.

The software also easily fits into existing research practices since, after preprocessing with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. Both the treatment indicator and pre-treatment covariates must be contained in the same data frame, which is specified as data see below.

All of the usual R syntax for formula works.

plot.matchit

See help formula for details. This argument specifies the data frame containing the variables called in formula. This argument specifies a matching method. Currently, "exact" exact matching"full" full matching"genetic" genetic matching"nearest" nearest neighbor matching"optimal" optimal matchingand "subclass" subclassification are available. The default is "nearest". Note that within each of these matching methods, MatchIt offers a variety of options.

This argument specifies the method used to estimate the distance measure.

help matchit

The default is logistic regression, "logit". A variety of other methods are available. This optional argument specifies the optional arguments that are passed to the model for estimating the distance measure. The input to this argument should be a list. This argument specifies whether to discard units that fall outside some measure of support of the distance score before matching, and not allow them to be used at all in the matching procedure.

Note that discarding units may change the quantity of interest being estimated. The options are: "none" defaultwhich discards no units before matching, "both"which discards all units treated and control that are outside the support of the distance measure, "control"which discards only control units outside the support of the distance measure of the treated units, and "treat"which discards only treated units outside the support of the distance measure of the control units.

This argument specifies whether the model for distance measure should be re-estimated after units are discarded. The input must be a logical value. There are a number of matching options, detailed below. The output of the model used to estimate the distance measure. Each column stores the name s of the control unit s matched to the treatment unit of that row. For example, when the ratio input for nearest neighbor or optimal matching is specified as 3, the three columns of match.

NA indicates that the treatment unit was not matched. Unmatched units have weights equal to 0. Matched treated units have weight 1. Each matched control unit has weight proportional to the number of treatment units to which it was matched, and the sum of the control weights is equal to the number of uniquely matched control units. The subclass index in an ordinal scale from 1 to the total number of subclasses as specified in subclass or the total number of subclasses from full or exact matching.

Unmatched units have NA. The covariates used for estimating the distance measure the right-hand side of formula. Political Analysis 15 3 : Please use help. Created by DataCamp.Search everywhere only in this topic. Advanced Search.

Classic List Threaded. Thyago Moraes. Am I missing anything about the means generated by the summary command after match? There is no missing data. Thank you in advance for your help and patience. Uwe Ligges Re: Matchit. On Please provide the code and a toy example so that we know what you actually did.

Otherwise it is hard to help.

Subscribe to RSS

Sorry for that. The differences are in red. Dear Friends, I'm using the following code for generate a Figure where the x-axis is divided in intervals of 12 months package cmprsk. However, the same numbers also appears vertically in the top of the figure.

help.matchit

Not sure what I'm missing to avoid this. Thanks in advance Thyago plot. Optimal Match - Matchit. In reply to this post by Thyago Moraes. Dear Friends, I'm having a problem when trying to match data using the optimal Match. Any one could kindly try to help me with this?

In reply to this post by Thyago Moraes Dear Friends, I'm having a problem when trying to match data using the optimal Match. Free forum by Nabble.

Edit this page.


thoughts on “Help matchit

Leave a Reply

Your email address will not be published. Required fields are marked *