As a statistical programming language, R allows users to access precise statistics instead of simply printing a mass of output to the screen. The examples below highlight how to create a complex sample survey design object and then directly query specific coefficients, error terms, and other survey design-related information as needed.
This page uses the survey package. Make sure that you can load
it before trying to run the examples on this page. If you do not have
a package installed, run: install.packages("packagename"), or
if you see the version is out of date, run: update.packages().
library(survey)
Version info: Code for this page was tested in R version 3.5.3 (2019-03-11)
On: 2019-03-28
With: survey 3.35-1; survival 2.43-3; Matrix 1.2-17; knitr 1.22
Example
This example is taken from Lehtonen and Pahkinen’s Practical Methods for Design and Analysis of Complex Surveys. Page 46 Table 2.6 Estimates from a systematic sample drawn from the Province’91 population using implicit stratification.
Import the dataset from text directly into R using the read.table function and the text= parameter specifying the entire data set. The syntax \n indicates the end of one line of data.
mydata <- read.table( text = "id str clu wt ue91 lab91 1 1 1 4 4123 33786 2 1 5 4 721 4930 3 2 9 4 194 2069 4 2 13 4 129 927 5 2 17 4 239 2144 6 2 21 4 61 573 7 2 25 4 262 1737 8 2 29 4 166 1615" , header = TRUE )
More detail about read.table or any other R function can be viewed by typing a question mark in front of the function name in the R console. For example: ?read.table.
In the R language, individual data sets are stored as data.frame objects, allowing users to load as many tables into working memory as necessary for the analysis. After loading the mydata table into memory, R functions can be run directly on this data table.
# the `class` of the `mydata` object class(mydata)
## [1] "data.frame"
# the first six rows of the data head(mydata)
## id str clu wt ue91 lab91 ## 1 1 1 1 4 4123 33786 ## 2 2 1 5 4 721 4930 ## 3 3 2 9 4 194 2069 ## 4 4 2 13 4 129 927 ## 5 5 2 17 4 239 2144 ## 6 6 2 21 4 61 573
# the last six rows of the data tail(mydata)
## id str clu wt ue91 lab91 ## 3 3 2 9 4 194 2069 ## 4 4 2 13 4 129 927 ## 5 5 2 17 4 239 2144 ## 6 6 2 21 4 61 573 ## 7 7 2 25 4 262 1737 ## 8 8 2 29 4 166 1615
# the number of columns ncol(mydata)
## [1] 6
View a simple summary of your variable of interest. This summary function accesses the ue91 column (variable) stored inside the mydata data.frame object.
summary(mydata$ue91)
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 61 157 216 737 377 4123
Initiate your svydesign object for a systematic sample design. This `mydesign` object will be used for all subsequent analysis commands:
mydata$fpc = 8/32 mydesign <- svydesign(id = ~clu, weight = ~wt, strata = ~str, fpc = ~fpc, data = mydata)
From this point forward, the sampling specifications of the mydata data set’s survey design have been fixed and most analysis commands will simply use the set of tools outlined on the R survey package homepage, referring to the object `mydesign` at the design= parameter of the specific R function or method.
View the survey design structure:
mydesign
## Stratified Independent Sampling design ## svydesign(id = ~clu, weight = ~wt, strata = ~str, fpc = ~fpc, ## data = mydata)
View the survey design’s object class or type:
class(mydesign)
## [1] "survey.design2" "survey.design"
View the weighted total population of this survey design, by referring to the wt column from the original data.frame:
sum(mydata$wt)
## [1] 32
View the number of unique PSUs (clusters) in this survey design, by referring to the clu column from the original data.frame:
length(unique(mydata$clu))
## [1] 8
View the number of unique strata in this survey design, by referring to the str column from the original data.frame:
length(unique(mydata$str))
## [1] 2
View the degrees of freedom for this survey design object:
degf(mydesign)
## [1] 6
Count the number of unweighted observations where the variable ue91 is not missing:
unwtd.count(~ue91, mydesign)
## counts SE ## counts 8 0
Print the mean and standard error of the ue91 variable:
svymean(~ue91, mydesign)
## mean SE ## ue91 737 369
Print the weighted total and standard error of the ue91 variable:
svytotal(~ue91, mydesign)
## total SE ## ue91 23580 11801
Alternatively, the result of a function call like svymean or svytotal can be stored into a secondary R object.
mysvymean <- svymean(~ue91, mydesign, deff = TRUE)
Once created, this svymean can be queried independently from the mydesign object. For example, the coef and SE functions can directly extract those attributes:
coef(mysvymean)
## ue91 ## 737
SE(mysvymean)
## ue91 ## ue91 369
The design effect extraction function deff can only be used if the original svymean call that created the object mysvymean included the parameter deff = TRUE.
deff(mysvymean)
## ue91 ## 0.759
Also note that the number of decimal places shown can be adjusted by modifying the digits parameter within the options function at any time.
options(digits=10) SE(mysvymean)
## ue91 ## ue91 368.7966062
We can use the confint function to obtain confidence intervals for the coefficient estimates.
confint(mysvymean)
## 2.5 % 97.5 % ## ue91 14.0469343 1459.703066
Save the ratio of ue91 to lab91 into a new object myratio and at the same time print it to the screen by encapsulaing the entire statement in parentheses.
(myratio <- svyratio(~ue91, ~lab91, mydesign))
## Ratio estimator: svyratio.survey.design2(~ue91, ~lab91, mydesign) ## Ratios= ## lab91 ## ue91 0.1233754003 ## SEs= ## lab91 ## ue91 0.003332479648
We can then use the confint function to obtain confidence intervals for the ratio.
confint(myratio)
## 2.5 % 97.5 % ## ue91/lab91 0.1168438602 0.1299069404
We can specify the df= parameter to use the survey design’s degrees of freedom (instead of the default df=Inf) to replicate Stata’s confidence interval calculation method.
# matches stata confint(myratio, df = degf(mydesign))
## 2.5 % 97.5 % ## ue91/lab91 0.1152211163 0.1315296842
Print the median of the ue91 variable, including the confidence interval in the output.
svyquantile(~ue91, mydesign, quantiles = 0.5, ci = TRUE)
## $quantiles ## 0.5 ## ue91 194 ## ## $CIs ## , , ue91 ## ## 0.5 ## (lower 119.0017579 ## upper) 329.4881344
See also
- The R survey package homepage
- Lumley, T. Complex Surveys: A Guide to Analysis Using R (Wiley Series in Survey Methodology)
- Damico, A. Step-by-step instructions to analyze major public-use survey data sets with the R language