relrisk.analysis {spsurvey} | R Documentation |
This function organizes input and output for relative risk analysis of categorical data generated by a probability survey.
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)
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 identify Type. A Type variable identifies each site with one of the subpopulations of that Type. The default is NULL. |
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 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.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 with missing values. The first column of this argument is site IDs. Subsequent columns are response and stressor variables. Missing data (NA) is allowed. |
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 argument must be the same length as argument response.var. |
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 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. |
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 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. |
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 size-weights. For an extensive
resource, this argument is the measure of the resource, i.e., either known
total length for a linear resource or known total area for an areal
resource. The argument must be in the form of a list containing an
element for each population Type in the subpop data frame, where NULL is a
valid choice for a population Type. The list must be named using the
column names for the population Types in subpop. If a population Type
doesn't contain subpopulations, then each element of the list is either a
single value for an unstratified sample or a vector containing a value for
each stratum for a stratified sample, where elements of the vector are
named using the stratum codes. If a population Type contains
subpopulations, then each element of the list is a list containing an
element for each subpopulation, where the list is named using the
subpopulation names. The element for each subpopulation will be either a
single value for an unstratified sample or a named vector of values for a
stratified sample. The default is NULL. Example popsize for a stratified sample: popsize = list("Pop 1"=c("Stratum 1"=750, "Stratum 2"=500, "Stratum 3"=250), "Pop 2"=list("SubPop 1"=c("Stratum 1"=350, "Stratum 2"=250, "Stratum 3"=150), "SubPop 2"=c("Stratum 1"=250, "Stratum 2"=150, "Stratum 3"=100), "SubPop 3"=c("Stratum 1"=150, "Stratum 2"=150, "Stratum 3"=75)), "Pop 3"=NULL) Example popsize for an unstratified sample: popsize = list("Pop 1"=1500, "Pop 2"=list("SubPop 1"=750, "SubPop 2"=500, "SubPop 3"=375), "Pop 3"=NULL) |
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 is FALSE. |
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 have the names attribute set to identify the stratum codes. The default is NULL. |
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 value for each stratum and must have the names attribute set to identify the stratum codes. The default is NULL. |
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 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. |
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 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.
Tom Kincaid Kincaid.Tom@epa.gov
Sarndal, C.E., B. Swensson, and J. Wretman. (1992). Model Assisted Survey Sampling. Springer-Verlag, New York.
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)