This function organizes input and output for relative risk analysis of categorical data generated by a probability survey.
relrisk.analysis(sites=NULL, subpop=NULL, 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)), popcorrect=FALSE, pcfsize=NULL, N.cluster=NULL,
stage1size=NULL, sizeweight=FALSE, vartype="Local", conf=95)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.
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 identify Type. A Type variable identifies each site with one of the subpopulations of that Type. The default is NULL.
a data frame consisting of design variables. 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 two-stage sample xcoord = x-coordinates for location, which are either the x-coordinates for a single-stage sample or the stage two x-coordinates for a two-stage sample ycoord = y-coordinates for location, which are either the y-coordinates for a single-stage sample or the stage two y-coordinates for a two-stage sample stratum = the stratum codes cluster = the stage one sampling unit (primary sampling unit or cluster) codes wgt1 = final adjusted stage one weights xcoord1 = the stage one x-coordinates for location ycoord1 = the stage one y-coordinates for location support = support values - the value one (1) for a site from a finite resource or the measure of the sampling unit associated with a site from an extensive resource, which is required for calculation of finite and continuous population correction factors swgt = size-weights, which is the stage two size-weight for a two- stage sample swgt1 = stage one size-weights
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 with missing values. The first column of this argument is site IDs. Subsequent columns are response and stressor variables. Missing data (NA) is allowed.
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.
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 argument must be the same length as argument response.var.
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 numerator and the denominator of the relative risk estimate. The default is a list containing the values "Poor" and "Good" for the first and second levels, respectively, of each element in the response.var argument.
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 numerator of the relative risk estimate, and the second level for each element in the list is used for calculating the denominator of the estimate. The default is a list containing the values "Poor" and "Good" for the first and second levels, respectively, of each element in the stressor.var argument.
a logical value that indicates whether finite or continuous population correction factors should be employed during variance estimation, where TRUE = use the correction factor and FALSE = do not use the correction factor. The default is FALSE. To employ the correction factor for a single-stage sample, values must be supplied for argument pcfsize and for the support variable of the design argument. To employ the correction factor for a two-stage sample, values must be supplied for arguments N.cluster and stage1size, and for the support variable of the design argument.
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 have the names attribute set to identify the stratum codes. The default is NULL.
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 value for each stratum and must have the names attribute set to identify the stratum codes. The default is NULL.
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 sampling unit codes. For a stratified sample, the names attribute must be set to identify both stratum codes and stage one sampling unit codes using a convention where the two codes are separated by the & symbol, e.g., "Stratum 1&Cluster 1". The default is NULL.
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.
the choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator. The default is "Local".
the confidence level. The default is 95%.
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.
Sarndal, C.E., B. Swensson, and J. Wretman. (1992). Model Assisted Survey Sampling. Springer-Verlag, New York.
# NOT RUN {
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))
relrisk.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
data.rr=mydata.rr, response.var=c("RespVar1", "RespVar2"),
stressor.var=rep("StressVar", 2))
# }
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