Analysis of Complex Survey Samples
Model comparison for glms.
Package sample and population size data
Convert a survey design to use replicate weights
Update to the new survey design format
Barplots and Dotplots
Compute survey bootstrap weights
Compute replicate weights
Calibration (GREG) estimators
Compress replicate weight matrix
Confidence intervals for regression parameters
Dimensions of survey designs
Estimated weights for missing data
Lay out tables of survey statistics
Hadamard matrices
Wrappers for specifying PPS designs
Calibration metrics
Standardised predictions (predictive margins) for regression models.
Experimental: Construct non-response weights
Deprecated implementation of quantiles
Open and close DBI connections
Paley-type Hadamard matrices
Distribution of quadratic forms
Specify Poisson sampling design
Post-stratify a survey
Pseudo-Rsquareds
Raking of replicate weight design
Wald test for a term in a regression model
Extract standard errors
Small area estimation via basic area level model
Smooth via basic unit level model
Take a stratified sample
Subset of survey
Options for the survey package
Summary statistics for sample surveys
Specify survey design with replicate weights
Compute variance from replicates
Sandwich variance estimator for glms
Survey statistics on subsets
Cumulative Distribution Function
Contingency tables for survey data
Confidence intervals for proportions
Linear and nonlinearconstrasts of survey statistics
Conditioning plots of survey data
Survey-weighted Cox models.
Computations for survey variances
Cronbach's alpha
Survey sample analysis.
Factor analysis in complex surveys (experimental).
Survey-weighted generalised linear models.
Test of fit to known probabilities
Histograms and boxplots
Two-stage least-squares for instrumental variable regression
Cohen's kappa for agreement
Estimate survival function.
Loglinear models
Compare survival distributions
Maximum pseudolikelihood estimation in complex surveys
Probability-weighted nonlinear least squares
Proportional odds and related models
Plots for survey data
Sampling-weighted principal component analysis
Predictive marginal means
Quantile-quantile plots for survey data
Quantiles under complex sampling.
Design-based rank tests
Ratio estimation
Variance estimation for multistage surveys
Score tests in survey regression models
Scatterplot smoothing and density estimation
Direct standardization within domains
Fit accelerated failure models to survey data
Design-based t-test
Trim sampling weights
Two-phase designs
Add variables to a survey design
Survey design weights
Analyse multiple imputations
Analyse plausible values in surveys
Compute variances by replicate weighting
Crossed effects and other sparse correlations
Summary statistics, two-sample tests, rank tests, 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. PPS sampling without replacement. Small-area estimation.