Svyset in r

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Svyset in r

These functions perform weighted estimation, with each observation being weighted by the inverse of its sampling probability. Except for the table functions, these also give precision estimates that incorporate the effects of stratification and clustering. Factor variables are converted to sets of indicator variables for each category in computing means and totals. Combining this with the interaction function, allows crosstabulations. See ftable. With na. When using replicate weights and na. The svytotal and svreptotal functions estimate a population total.

Use predict on svyratio and svyglmto get ratio or regression estimates of totals. The object returned includes the full matrix of estimated population variances and covariances, but by default only the diagonal elements are printed. To display the whole matrix use as.

"survey" package -- doesn't appear to match svy

The design effect compares the variance of a mean or total to the variance from a study of the same size using simple random sampling without replacement. Note that the design effect will be incorrect if the weights have been rescaled so that they are not reciprocals of sampling probabilities. This with-replacement design effect is the square of Kish's "deft". The cv function computes the coefficient of variation of a statistic such as ratio, mean or total.

The default method is for any object with methods for SE and coef.

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This is useful because formulas as the best way to specify variables to the survey functions. Objects of class "svystat" or "svrepstat"which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic. These objects have methods for vcovSEcoefconfintsvycontrast. Don't compute standard errors useful when svyvar is used to estimate the design effect.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I call. Why are these alternative weights -- which work in Stata when resetting svyset -- not working in R?

Any insights would be appreciated!! Learn more. Asked 5 years, 8 months ago. Active 5 years, 8 months ago. Viewed times. New to R and have encountered two issues in coding using the "survey" package: 1 Code from svytable using "survey" package does not correspond to Stata estimates from svy: tab.

I call svyd. Any ideas as to what I've done incorrectly in R? Many thanks! Looks like in one instance you are working with "weights" that are inverse-weightings and perhaps in the other case with one where "weights" are case-weightings? Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow Checkboxland.

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Allow bountied questions to be closed by regular users. Hot Network Questions. Question feed. Stack Overflow works best with JavaScript enabled.It is important that analysts be familiar with certain key aspects of DHS data to be able to calculate accurately the indicators described in further chapters. The following sections describe some of the key elements to pay attention to in analyzing DHS data.

DHS sample designs are usually two-stage probability samples drawn from an existing sample frame, generally the most recent census frame. A probability sample is defined as one in which the units are selected randomly with known and nonzero probabilities. A sampling frame is a complete list of all sampling units that entirely covers the target population. Stratification is the process by which the sampling frame is divided into subgroups or strata that are as homogeneous as possible using certain criteria.

Within each stratum, the sample is designed and selected independently. The principal objective of stratification is to reduce sampling errors. In a stratified sample, the sampling errors depend on the population variance existing within the strata but not between the strata.

Within each stratum, the sample design specifies an allocation of households to be selected. Most DHS surveys use a fixed take of households per cluster of about households, determining the number of clusters to be selected. In the first stage of selection, the primary sampling units PSUs are selected with probability proportional to size PPS within each stratum.

The PSU forms the survey cluster. In the second stage, a complete household listing is conducted in each of the selected clusters. Following the listing of the households a fixed number of households is selected by equal probability systematic sampling in the selected cluster. The overall selection probability for each household in the sample is the probability of selecting the cluster multiplied by the probability of selecting the household within the cluster.

The overall probability of selection of a household will differ from cluster to cluster. DHS dataset users should be aware that, in most cases, the data must be weighted. This is because the overall probability of selection of each household is not a constant.

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The following describes how DHS weights are constructed and when they should be used. Sampling weights are adjustment factors applied to each case in tabulations to adjust for differences in probability of selection and interview between cases in a sample, due to either design or happenstance.

svyset in r

In DHS surveys, in most surveys the sample is selected with unequal probability to expand the number of cases available and hence reduce sample variability for certain areas or subgroups for which statistics are needed. In this case, weights need to be applied when tabulations are made of statistics to produce the proper representation.

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When weights are calculated because of sample design, corrections for differential response rates are also made. There may be additional sampling weights for sample subsets, such as anthropometry, biomarkers, HIV testing, etc. There is only a need for the additional sample weights if there is a differential probability in selecting the subsamples.

For example, if one in five households is selected in the whole sample for doing biomarkers, then an additional sample weight is not necessary. However, if one in five households in urban areas and one in two households in rural areas are selected, then an additional sample weight is necessary when estimating national levels or for any group that includes cases from both urban and rural areas.Search everywhere only in this topic. Advanced Search. Classic List Threaded.

Hi everyone, apologies if the answer to this is in an obvious place. I've been searching for about a day and haven't found anything. I'm trying to replicate Stata's confidence intervals in R with the survey package, and the numbers are very very close but not exact.

Everything lines up, except for the confidence intervals, and I'm wondering if there's a relatively straightforward reason why the numbers are different. To review the two major cross-package comparison documents on Dr. I tried running the analysis example below in SUDAAN as well, which calculated confidence intervals not matching either R or Stata -- I'm confused why all three would be different. I understand as the report above quotes these differences are statistically inconsequential, but I aim to convince people to switch to R, so hitting numbers dead-on might provide some reassurance to skeptics.

I've pasted some ultra-simple Stata code below and more importantly the output it generates. Below that, I've pasted some commented R code that shows how everything matches exactly except for the confidence intervals, and then displays a number of my failed attempts at hitting the numbers right on the nose. Thomas Lumley Hi Dr. Lumley, you're obviously correct about all of that. Thank you for cluing me into it! And sorry for overlooking that part of the documentation. I'm unfortunately still struggling with matching numbers exactly, and I foolishly provided a dataset without a weight variable - thinking there was only one issue preventing R from matching Stata precisely.

If you or anyone else still has any energy to look at this issue, I've pasted three analysis examples with both Stata and R code that aim to calculate a survey-adjusted confidence interval. All three examples match coefficient and standard error values precisely, for both weighted counts and percents. In the first example, the confidence intervals don't match for either the counts or the percents. In the second, the CIs match for counts but not percents.

In the third, CIs match for both. Because of this erratic behavior on relatively straightforward datasets, I'm worried that Stata is making some weird non-reproducible calculations that would obviously be outside of the scope of this list. The agreement isn't perfect for the counts in the first example, but since Stata's default numeric type is single-precision, it's accurate enough. Free forum by Nabble. Edit this page.Login or Register Log in with. Forums FAQ. Search in titles only.

Posts Latest Activity. Page of 1. Filtered by:. Andrew Kenny. Can I get a pseudo r-squared in SVY logistic? It seems that the standard way to use the data that I am using is to use it in weighted fashion using SVY rather than unweighted.

However, it seems that, while logistic regression produces a pseudo r-squared statisticSVY logisitc does not. Am I mistaken? Any suggestions for how to handle this? As a side note, I gather that there are differing views regarding how useful pseudo r-squared is. However, it is relevant to note that the journal I am aiming to publish in is read primarily by non-statisticians.

The focus of my study is more on the coefficients than the r-squared and I would think that readers of the journal I am aiming for will think similarly.

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If there is a reason that r-squared is not reported by SVY logistic, might it be quite standard eg acceptable in many journals to just let the r-squared go unreported? Tags: None. Richard Williams. That is also why you suddenly start getting Wald chi-squares or F values instead of LR chi-squares when you use the cluster option or svy: prefix. This struck me as really bizarre at first until I more or less understood it.

Comment Post Cancel. Joseph Luchman. This is to say that there are ways to get R 2 s for clustered-data designs. Survey models have the advantage building the aspects which affect the log-likelihoods for simpler models like -logit- at least into the survey weights. Thus, for many models, the pseudo-R 2 can be obtained as Richard notes - with the -pweight-s alone and the non-svy-prefixed command.

I discuss a related issue with simulation as demonstration here e. I am sure there are applications where more than merely the -pweight-s must be used, but for many standard -svy- models -regress- -logit- -ologit- -poisson- this logic should apply.

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Interested in hearing other counterpoints or cautions on this issue - but it seems that when used for primarily descriptive purposes as is usually the case; not estimating sampling variances of pseudo-R 2using the -pweight-s alone without -svy- prefix would seem to be fine and should not be frowned upon. Explained variance measures for multilevel models.

Organizational Research Methods17 4 Luchman, J. Determining subgroup difference importance with complex survey designs: An application of weighted dominance analysis. Survey Practice8 5.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

However I am unsure whether this is a correct translation and if I am applying this correctly in R. Any insight and suggestions would be greatly appreciated! That is svrepdesign for a survey with replicate weights, sampling weights wgtpand replicate weights wgtp1-wgtp80and "successive difference" weights. Learn more. Asked 2 years, 9 months ago. Active 1 month ago. Viewed times. I'm trying to weight survey data in R. I'm using Stata code as a reference. Nick Cox Matt Matt 11 2 2 bronze badges.

Active Oldest Votes. Thomas Lumley Thomas Lumley 4 4 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook.

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svyset in r

The new moderator agreement is now live for moderators to accept across the…. Allow bountied questions to be closed by regular users. Hot Network Questions. Question feed. Stack Overflow works best with JavaScript enabled.R in Action 2nd ed significantly expands upon this material.

Stata: Data Analysis and Statistical Software

R has powerful indexing features for accessing object elements. These features can be used to select and exclude variables and observations. The following code snippets demonstrate ways to keep or delete variables and observations and to take random samples from a dataset. To practice this interactively, try the selection of data frame elements exercises in the Data frames chapter of this introduction to R course. The subset function is the easiest way to select variables and observations.

svyset in r

In the following example, we select all rows that have a value of age greater than or equal to 20 or age less then We keep the ID and Weight columns. In the next example, we select all men over the age of 25 and we keep variables weight through income weight, income and all columns between them.

To practice the subset function, try this this interactive exercise. Use the sample function to take a random sample of size n from a dataset. Kabacoff, Ph. Subsetting Data R has powerful indexing features for accessing object elements. Random Samples Use the sample function to take a random sample of size n from a dataset.


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