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survey (version 3.22-4)

analysis of complex survey samples

Description

Summary statistics, generalised linear models, cumulative link 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 subsampling designs. Graphics. Predictive margins by direct standardization. PPS sampling without replacement. Principal components, factor analysis.

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Version

Install

install.packages('survey')

Monthly Downloads

62,531

Version

3.22-4

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

October 13th, 2010

Functions in survey (3.22-4)

Wrappers for specifying PPS designs

Convert a survey design to use replicate weights

Hadamard matrices

Package sample and population size data

US 2004 presidential election data at state or county level

Factor analysis in complex surveys (experimental).

Loglinear models

Take a stratified sample

Extract standard errors

Update to the new survey design format

Household crowding

Proportional odds and related models

Student performance in California schools

Survey statistics on subsets

Compute variance from replicates

Lay out tables of survey statistics

Specify survey design with replicate weights

Variance estimation for multistage surveys

subset.survey.design

Subset of survey

Standardised predictions (predictive margins) for regression models.

Quantiles for sample surveys

Experimental: Construct non-response weights

Scatterplot smoothing and density estimation

Post-stratify a survey

Maximum pseudolikelihood estimation in complex surveys

Sandwich variance estimator for glms

Plots for survey data

Linear and nonlinearconstrasts of survey statistics

Compute survey bootstrap weights

Survey sample analysis.

Survey-weighted Cox models.

Contingency tables for survey data

Options for the survey package

Cumulative Distribution Function

Histograms and boxplots

dimnames.DBIsvydesign

Dimensions of survey designs

Estimated weights for missing data

Compute replicate weights

Survey-weighted generalised linear models.

Paley-type Hadamard matrices

Computations for survey variances

Confidence intervals for proportions

open.DBIsvydesign

Open and close DBI connections

Two-phase designs

Compute variances by replicate weighting

Calibration metrics

Raking of replicate weight design

Distribution of quadratic forms

Design-based t-test

Summary statistics for sample surveys

Sampling-weighted principal component analysis

Cohen's kappa for agreement

barplot.svystat

Barplots and Dotplots

Wald test for a term in a regression model

Conditioning plots of survey data

compressWeights

Compress replicate weight matrix

Estimate survival function.

weights.survey.design

Survey design weights

with.svyimputationList

Analyse multiple imputations

update.survey.design

Add variables to a survey design

Confidence intervals for regression parameters

Calibration (GREG) estimators

Two-stage sample from MU284

Ratio estimation

Trim sampling weights

Small survey example

Survival in cardiac arrest

Sample of obstetric hospitals