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survey (version 3.21-2)

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

3.21-2

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

March 19th, 2010

Functions in survey (3.21-2)

Wrappers for specifying PPS designs

Specify survey design with replicate weights

Standardised predictions (predictive margins) for regression models.

update.survey.design

Add variables to a survey design

Sampling-weighted principal component analysis

Linear and nonlinearconstrasts of survey statistics

Wald test for a term in a regression model

Compute replicate weights

Experimental: Construct non-response weights

Survey statistics on subsets

Compute survey bootstrap weights

Summary statistics for sample surveys

Scatterplot smoothing and density estimation

US 2004 presidential election data at state or county level

Convert a survey design to use replicate weights

Options for the survey package

Compute variance from replicates

Survey sample analysis.

weights.survey.design

Survey design weights

Extract standard errors

Survey-weighted Cox models.

Package sample and population size data

Calibration metrics

Calibration (GREG) estimators

Student performance in California schools

Histograms and boxplots

Household crowding

Variance estimation for multistage surveys

Quantiles for sample surveys

Factor analysis in complex surveys (experimental).

Paley-type Hadamard matrices

Post-stratify a survey

Conditioning plots of survey data

Contingency tables for survey data

Trim sampling weights

Estimate survival function.

with.svyimputationList

Analyse multiple imputations

dimnames.DBIsvydesign

Dimensions of survey designs

Raking of replicate weight design

Two-stage sample from MU284

open.DBIsvydesign

Open and close DBI connections

Hadamard matrices

Confidence intervals for proportions

Estimated weights for missing data

Lay out tables of survey statistics

Cohen's kappa for agreement

Computations for survey variances

Cumulative Distribution Function

Plots for survey data

Survey-weighted generalised linear models.

Update to the new survey design format

compressWeights

Compress replicate weight matrix

Distribution of quadratic forms

Loglinear models

Sandwich variance estimator for glms

Maximum pseudolikelihood estimation in complex surveys

Compute variances by replicate weighting

barplot.svystat

Barplots and Dotplots

Confidence intervals for regression parameters

subset.survey.design

Subset of survey

Two-phase designs

Ratio estimation

Proportional odds and related models

Design-based t-test

Sample of obstetric hospitals

Small survey example

Survival in cardiac arrest