relrisk.analysis: Relative Risk Analysis for Probability Survey Data

Description

This function organizes input and output for relative risk analysis of categorical data generated by a probability survey.

Usage

relrisk.analysis(sites, subpop, design, data.rr, response.var, stressor.var,
   response.levels=rep(list(c("Poor","Good")), length(response.var)),
   stressor.levels=rep(list(c("Poor","Good")), length(stressor.var)),
   popsize=NULL, popcorrect=FALSE, pcfsize=NULL, N.cluster=NULL,
   stage1size=NULL, sizeweight=FALSE, vartype="Local", conf=95)

Arguments

sites

a data frame consisting of two variables: the first variable is site IDs, and the second variable is a logical vector indicating which sites to use in the analysis. The default is NULL.

subpop

a data frame describing sets of populations and subpopulations for which estimates will be calculated. The first variable is site IDs. Each subsequent variable identifies a Type of population, where the variable name is used to identif

design

a data frame consisting of design variables. The default is NULL. Variables should be named as follows: siteID = site IDs wgt = final adjusted weights, which are either the weights for a single-stage sample or the stage two weights for a

data.rr

data frame of categorical response and stressor variables, where each variable consists of two categories. If response or stressor variables include more than two categories, occurrences of those categories must be removed or replaced w

response.var

character vector providing names of columns in argument data.rr that contain a response variable, where names may be repeated. Each name in this argument is matched with the corresponding value in the stressor.var argument.

stressor.var

character vector providing names of columns in argument data.rr that contain a stressor variable, where names may be repeated. Each name in this argument is matched with the corresponding value in the response.var argument. This argumen

response.levels

list providing the category values (levels) for each element in the response.var argument. This argument must be the same length as argument response.var. The first level for each element in the list is used for calculating the numerat

stressor.levels

list providing the category values (levels) for each element in the stressor.var argument. This argument must be the same length as argument response.var. The first level for each element in the list is used for calculating the numerat

popsize

known size of the resource, which is used to perform ratio adjustment to estimators expressed using measurement units for the resource. For a finite resource, this argument is either the total number of sampling units or the known sum of s

popcorrect

a logical value that indicates whether finite or continuous population correction factors should be employed during variance estimation, where TRUE = use the correction factors and FALSE = do not use the correction factors. The default

pcfsize

size of the resource, which is required for calculation of finite and continuous population correction factors for a single-stage sample. For a stratified sample this argument must be a vector containing a value for each stratum and must h

N.cluster

the number of stage one sampling units in the resource, which is required for calculation of finite and continuous population correction factors for a two-stage sample. For a stratified sample this variable must be a vector containing a

stage1size

size of the stage one sampling units of a two-stage sample, which is required for calculation of finite and continuous population correction factors for a two-stage sample and must have the names attribute set to identify the stage one samp

sizeweight

a logical value that indicates whether size-weights should be used in the analysis, where TRUE = use the size-weights and FALSE = do not use the size-weights. The default is FALSE.

vartype

the choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator. The default is "Local".

conf

the confidence level. The default is 95%.

Value

Value is a data frame of relative risk estimates for all combinations of population Types, subpopulations within Types, and response variables. Standard error and confidence interval estimates also are provided.

References

Sarndal, C.E., B. Swensson, and J. Wretman. (1992). Model Assisted Survey Sampling. Springer-Verlag, New York.

Examples

Run this code

mysiteID <- paste("Site", 1:100, sep="")
mysites <- data.frame(siteID=mysiteID, Active=rep(TRUE, 100))
mysubpop <- data.frame(siteID=mysiteID, All.Sites=rep("All Sites", 100),
  Resource.Class=rep(c("Agr", "Forest"), c(55,45)))
mydesign <- data.frame(siteID=mysiteID, wgt=runif(100, 10, 100),
  xcoord=runif(100), ycoord=runif(100), stratum=rep(c("Stratum1",
  "Stratum2"), 50))
mydata.rr <- data.frame(siteID=mysiteID, RespVar1=sample(c("Poor", "Good"),
  100, replace=TRUE), RespVar2=sample(c("Poor", "Good"), 100, replace=TRUE),
  StressVar=sample(c("Poor", "Good"), 100, replace=TRUE), wgt=runif(100, 10,
  100))
mypopsize <- list(All.Sites=c(Stratum1=3500, Stratum2=2000),
  Resource.Class=list(Agr=c(Stratum1=2500, Stratum2=1500),
  Forest=c(Stratum1=1000, Stratum2=500)))
relrisk.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
  data.rr=mydata.rr, response.var=c("RespVar1", "RespVar2"),
  stressor.var=rep("StressVar", 2), popsize=mypopsize)