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survey (version 3.17)

analysis of complex survey samples

Description

Summary statistics, generalised linear models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase designs. Graphics. Predictive margins by direct standardization. PPS sampling without replacement.

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Version

Install

install.packages('survey')

Monthly Downloads

62,531

Version

3.17

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

September 18th, 2009

Functions in survey (3.17)

Experimental: Construct non-response weights

Extract standard errors

Wrappers for specifying PPS designs

Confidence intervals for regression parameters

Two-stage sample from MU284

Post-stratify a survey

Compute variance from replicates

Survey-weighted generalised linear models.

Update to the new survey design format

US 2004 presidential election data at state or county level

Distribution of quadratic forms

Convert a survey design to use replicate weights

dimnames.DBIsvydesign

Dimensions of survey designs

Summary statistics for sample surveys

Estimated weights for missing data

Calibration metrics

Specify survey design with replicate weights

Compute survey bootstrap weights

Ratio estimation

Confidence intervals for proportions

update.survey.design

Add variables to a survey design

Plots for survey data

Raking of replicate weight design

Survey sample analysis.

barplot.svystat

Barplots and Dotplots

Linear and nonlinearconstrasts of survey statistics

Standardised predictions (predictive margins) for regression models.

weights.survey.design

Survey design weights

Survey statistics on subsets

Compute replicate weights

Scatterplot smoothing and density estimation

with.svyimputationList

Analyse multiple imputations

Survey-weighted Cox models.

Hadamard matrices

Estimate survival function.

open.DBIsvydesign

Open and close DBI connections

Histograms and boxplots

Contingency tables for survey data

Proportional odds and related models

Compute variances by replicate weighting

Wald test for a term in a regression model

Lay out tables of survey statistics

Student performance in California schools

Cohen's kappa for agreement

Variance estimation for multistage surveys

Household crowding

subset.survey.design

Subset of survey

Package sample and population size data

Loglinear models

compressWeights

Compress replicate weight matrix

Paley-type Hadamard matrices

Cumulative Distribution Function

Options for the survey package

Maximum pseudolikelihood estimation in complex surveys

Sandwich variance estimator for glms

Conditioning plots of survey data

Calibration (GREG) estimators

Computations for survey variances

Quantiles for sample surveys

Design-based t-test

Two-phase designs

Survival in cardiac arrest

Small survey example

Sample of obstetric hospitals