similar to: surveyNG (and survey)

Version 3.3 of "survey" is percolating through CRAN. Since the last announcement on this list, version 2.9, the main additions are calibration estimators: linear, bounded linear, raking ratio, bounded raking ratio, logit. Other updates and bug fixes are described in http://faculty.washington.edu/tlumley/survey/NEWS -thomas Thomas Lumley Assoc. Professor,

Version 3.3 of "survey" is percolating through CRAN. Since the last announcement on this list, version 2.9, the main additions are calibration estimators: linear, bounded linear, raking ratio, bounded raking ratio, logit. Other updates and bug fixes are described in http://faculty.washington.edu/tlumley/survey/NEWS -thomas Thomas Lumley Assoc. Professor,

Version 3.11 of the survey package is making its way through CRAN. Since the last announcement on this list, of version 3.9, last September, there have been many minor bug fixes and usability improvements. The main new features are - loglinear models with svyloglin() - database-backed designs now allow new variables to be created, support ODBC in addition to DBI database interfaces, and

Version 3.11 of the survey package is making its way through CRAN. Since the last announcement on this list, of version 3.9, last September, there have been many minor bug fixes and usability improvements. The main new features are - loglinear models with svyloglin() - database-backed designs now allow new variables to be created, support ODBC in addition to DBI database interfaces, and

Hi, All Thanks to Robert Baskin, Thomas Lumley, and Spencer Graves for the valuable helps. I have learned a lot from this discussion. I put all discussions together without editing, so we can see how things are evolved. Likely, I have a lot of articles to read. As in the discussion, mixed modeling approach is a poosible but may be over-kill in my posted data analyses. I will explore other

Version 3.18 of the survey package is now percolating through CRAN. Since the last announcement on this list, in February, the main additions are - standard errors for survival curves (both Kaplan-Meier and Cox model) - svyciprop() for confidence intervals on proportions, especially in small samples or near 0 or 1. - predictive margins by direct standardization, with marginpred() -

Version 3.18 of the survey package is now percolating through CRAN. Since the last announcement on this list, in February, the main additions are - standard errors for survival curves (both Kaplan-Meier and Cox model) - svyciprop() for confidence intervals on proportions, especially in small samples or near 0 or 1. - predictive margins by direct standardization, with marginpred() -

Version 2.9 of survey is on CRAN. In addition to various minor improvements and bug fixes there are two major changes - full multistage finite-population sampling is supported (as in SUDAAN) - the same analysis commands can be used for all design types (eg svymean instead of svrepmean for replicate weight designs) -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at

Version 2.9 of survey is on CRAN. In addition to various minor improvements and bug fixes there are two major changes - full multistage finite-population sampling is supported (as in SUDAAN) - the same analysis commands can be used for all design types (eg svymean instead of svrepmean for replicate weight designs) -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at

Version 3.20 of the survey package is percolating through CRAN The major additions since the last announcement on this list (3.18, in September) are - database-backed designs can now use replicate weights - some multivariate statistics: principal components, factor analysis. The NEWS file has a more detailed list of additions and changes. -thomas Thomas Lumley Assoc.

Version 3.20 of the survey package is percolating through CRAN The major additions since the last announcement on this list (3.18, in September) are - database-backed designs can now use replicate weights - some multivariate statistics: principal components, factor analysis. The NEWS file has a more detailed list of additions and changes. -thomas Thomas Lumley Assoc.

-------------------------------------------------- From: "Ahoussou Sylvie" <sylvie.ahoussou at antilles.inra.fr> Sent: Friday, September 12, 2008 9:48 AM To: "Thomas Lumley" <tlumley at u.washington.edu> Subject: Re: [R] Complex sampling survey _ Use of survey package > Thanks for your answer > > I think I made a mistake when I recopied the 5 first rows of

Version 1.9 of the survey package, now percolating through CRAN, adds a beta implementation of replication weights. These can either be created from a survey design (using BRR, JK1, or JKn schemes) or provided by the user. These have been tested on only a few examples so far: there seem to be relatively few published datasets with suitable analyses. As with earlier versions of the package, I

Version 1.9 of the survey package, now percolating through CRAN, adds a beta implementation of replication weights. These can either be created from a survey design (using BRR, JK1, or JKn schemes) or provided by the user. These have been tested on only a few examples so far: there seem to be relatively few published datasets with suitable analyses. As with earlier versions of the package, I

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Dear Kevin, In addition to the advice you've received, take a look at the survey package. It's not quite what you're asking for, but in fact it's probably more useful, in that it provides correct statistical inference for data collected in complex surveys. The package is described in an article, T. Lumley (2004), Analysis of complex survey samples, Journal of Statistical Software

I'm studying the calibration function in the survey package in preparation for raking some survey data. Results from the rake function below agree with other sources. When I run calibrate, I get a warning message and the M and F weights seem to be reversed. Even allowing for that, the deviation between calibrated and raked weights is much more than I expected. I see that in the calibrate

Estimados usuarios: Estoy intentando reproducir el ejemplo 6.4 de Thomas Lumley. Complex Survey. Editorial Wiley. 2010 (ver la página en google:

A new package `survey' for analysing complex survey samples is on CRAN. It handles stratification, clustering, and unequal sampling probabilities in descriptive statistics, glms, and general maximum likelihood fitting. The package is still under development: - it doesn't do the finite population correction to variances - it needs some real life worked examples Most importantly,