write.svystat.RdPrints survey statistics to a file or connection.
write.svystat(x, ...)
| x | An object containing survey statistics. |
|---|---|
| ... | Arguments to |
This function is just a convenience wrapper to write.table, designed to export objects which have been returned by survey statistics functions (e.g. svystatTM, svystatR, svystatS, svystatSR, svystatB, svystatQ, svystatL, svySigma, svySigma2).
write.table and the 'R Data Import/Export' manual.
# Creation of a design object: data(sbs) des<-e.svydesign(data=sbs,ids=~id,strata=~strata,weights=~weight, fpc=~fpc) # Estimation of the average value added per employee # for economic activity region and macro-sectors, # with SE, CV% and standard confidence intervals: stat <- svystatR(des,~va.imp2,~emp.num,by=~region:nace.macro, vartype=c("se","cvpct"),conf.int=TRUE) stat#> region nace.macro va.imp2/emp.num SE.va.imp2/emp.num #> North.Agriculture North Agriculture 60.73814 3.9277681 #> Center.Agriculture Center Agriculture 66.29121 9.3007336 #> South.Agriculture South Agriculture 40.93788 5.4752941 #> North.Industry North Industry 51.58483 0.8132740 #> Center.Industry Center Industry 34.32244 1.2946269 #> South.Industry South Industry 57.80820 2.1040120 #> North.Commerce North Commerce 220.89970 15.2905919 #> Center.Commerce Center Commerce 243.14733 28.7693634 #> South.Commerce South Commerce 245.46210 37.1096192 #> North.Services North Services 35.24294 0.6594013 #> Center.Services Center Services 48.75319 1.6642295 #> South.Services South Services 39.25642 1.5866207 #> CI.l(95%).va.imp2/emp.num CI.u(95%).va.imp2/emp.num #> North.Agriculture 53.03985 68.43642 #> Center.Agriculture 48.06211 84.52032 #> South.Agriculture 30.20650 51.66926 #> North.Industry 49.99085 53.17882 #> Center.Industry 31.78502 36.85987 #> South.Industry 53.68441 61.93199 #> North.Commerce 190.93069 250.86871 #> Center.Commerce 186.76041 299.53424 #> South.Commerce 172.72858 318.19561 #> North.Services 33.95053 36.53534 #> Center.Services 45.49136 52.01502 #> South.Services 36.14670 42.36614 #> CV%.va.imp2/emp.num #> North.Agriculture 6.466725 #> Center.Agriculture 14.030115 #> South.Agriculture 13.374639 #> North.Industry 1.576576 #> Center.Industry 3.771954 #> South.Industry 3.639643 #> North.Commerce 6.921961 #> Center.Commerce 11.832071 #> South.Commerce 15.118269 #> North.Services 1.871017 #> Center.Services 3.413581 #> South.Services 4.041685# In order to export the summary statistics above # into a CSV file for input to Excel one can use: if (FALSE) { write.svystat(stat,file="stat.csv",sep=";") } # ...and to read this file back into R one needs if (FALSE) { stat.back <- read.table("stat.csv",header=TRUE,sep=";", check.names=FALSE) stat.back } # Notice, however, that the latter object has # lost a lot of meta-data as compared to the # original one, so that e.g.: if (FALSE) { confint(stat.back) } # ...while, on the contrary: confint(stat)#> 2.5 % 97.5 % #> North.Agriculture:va.imp2/emp.num 53.03985 68.43642 #> Center.Agriculture:va.imp2/emp.num 48.06211 84.52032 #> South.Agriculture:va.imp2/emp.num 30.20650 51.66926 #> North.Industry:va.imp2/emp.num 49.99085 53.17882 #> Center.Industry:va.imp2/emp.num 31.78502 36.85987 #> South.Industry:va.imp2/emp.num 53.68441 61.93199 #> North.Commerce:va.imp2/emp.num 190.93069 250.86871 #> Center.Commerce:va.imp2/emp.num 186.76041 299.53424 #> South.Commerce:va.imp2/emp.num 172.72858 318.19561 #> North.Services:va.imp2/emp.num 33.95053 36.53534 #> Center.Services:va.imp2/emp.num 45.49136 52.01502 #> South.Services:va.imp2/emp.num 36.14670 42.36614