psurvey.analysis.fcn(psurvey.analysis)S-PLUS Documentation
Create an Object of Class psurvey.analysis

Create an Object of Class psurvey.analysis

DESCRIPTION:

This function creates an object of class psurvey.analysis that contains all of the information necessary to use the analysis functions in the psurvey.analysis library.

USAGE:

psurvey.analysis(sites=NULL, subpop=NULL, design=NULL, data.cat=NULL,
   data.cont=NULL, siteID=NULL, wgt=NULL, sigma=NULL, var.sigma=NULL,
   xcoord=NULL, ycoord=NULL, stratum=NULL, cluster=NULL, N.cluster=NULL,
   wgt1=NULL, xcoord1=NULL, ycoord1=NULL, popsize=NULL, stage1size=NULL,
   popcorrect=FALSE, support=NULL, sizeweight=FALSE, swgt=NULL, swgt1=NULL,
   unitsize=NULL, vartype="Local", conf=95, pctval=c(5,10,25,50,75,90,95))

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. If this data frame is not provided, then the data frame will be created, where (1) site IDs are obtained either from the design argument, the siteID argument, or both (when siteID is a formula); and (2) all sites will be used 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 and 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. If this data frame is not provided, then the data frame will be created, where (1) site IDs are obtained either from the design argument, the siteID argument, or both (when siteID is a formula); and (2) a single Type variable named All.Sites that consists of all sites will be created. The default is NULL.
design
a data frame consisting of design variables. If variable names are provided as formulas in the corresponding arguments, then the formulas are interpreted using this data frame. If this data frame is not provided, then the data frame will be created from inputs to the design variables in the argument list. The default is NULL. If variable names are not provided as formulas, then 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
data.cat
a data frame of categorical response variables. The first variable is site IDs. Subsequent variables are response variables. Missing data (NA) is allowed. If psurvey.obj is not provided, then this argument is required. The default is NULL.
data.cont
a data frame of continuous response variables. The first variable is site IDs. Subsequent variables are response variables. Missing data (NA) is allowed. If psurvey.obj is not provided, then this argument is required. The default is NULL.
siteID
site IDs. This variable can be input directly or as a formula and must be supplied either as this argument or in the design data frame. The default is NULL.
wgt
the final adjusted weight (inverse of the sample inclusion probability) for each site, which is either the weight for a single-stage sample or the stage two weight for a two-stage sample. The default is NULL.
sigma
measurement error variance. This variable must be a vector containing a value for each response variable and must have the names attribute set to identify the response variable names. Missing data (NA) is allowed. The default is NULL.
var.sigma
variance of the measurement error variance. This variable must be a vector containing a value for each response variable and must have the names attribute set to identify the response variable names. Missing data (NA) is allowed. The default is NULL.
xcoord
x-coordinate for location for each site, which is either the x-coordinate for a single-stage sample or the stage two x-coordinate for a two-stage sample. The default is NULL.
ycoord
y-coordinate for location for each site, which is either the y-coordinate for a single-stage sample or the stage two y-coordinate for a two-stage sample. The default is NULL.
stratum
the stratum codes. This variable can be input directly or as a formula. The default is NULL.
cluster
the stage one sampling unit (primary sampling unit or cluster) codes. This variable can be input directly or as a formula. 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.
wgt1
the final adjusted stage one weights. This variable can be input directly or as a formula. The default is NULL.
xcoord1
the stage one x-coordinates for location. This variable can be input directly or as a formula. The default is NULL.
ycoord1
the stage one y-coordinates for location. This variable can be input directly or as a formula. The default is NULL.
popsize
the known size of the resource - the total number of sampling units of a finite resource or the measure of an extensive resource, which is used to adjust estimators for the known size of a resource. This argument also is required for calculation of finite and continuous population correction factors for a single-stage sample. 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),
"Pop2"=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, "Pop2"=list("SubPop 1"=750, "SubPop 2"=500, "SubPop 3"=375), "Pop 3"=NULL)
stage1size
the known 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.
support
the support value for each site - the value one (1) for a site from a discrete resource or the measure of the sampling unit associated with a site from a continuous resource, which is required for calculation of finite and continuous population correction factors. The default is NULL.
swgt
the size-weight for each site, which is the stage two size-weight for a two-stage sample. The default is NULL.
swgt1
the stage one size-weight for each site. The default is NULL.
unitsize
the known sum of the size-weights of the 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 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.
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%.
pctval
the set of values at which percentiles are estimated. The default set is: {5, 25, 50, 75, 95}.

VALUE:

A list of class psurvey.analysis. Only those sites indicated by the logical variable in the sites data frame are retained in the output. The sites, subpop, and design data frames will always exist in the output. At least one of the data.cat and data.cont data frames will exist. Depending upon values of the input variables, other elements in the output may be NULL. The output list is composed of the following elements:

sites
the sites data frame.
subpop
the subpop data frame.
design
the design data frame.
data.cat
the data.cat data frame.
type.cat
the type of categorical response variables.
data.cont
the data.cont data frame.
N.cluster
the number of stage one sampling units in the resource.
popsize
the known size of the resource.
stage1size
the known size of the stage one sampling units.
unitsize
the known sum of the size-weights of the resource
stratum.ind
a logical value that indicates whether the sample is stratified, where TRUE = a stratified sample and FALSE = not a stratified sample.
cluster.ind
a logical value that indicates whether the sample is a two-stage sample, where TRUE = a two-stage sample and FALSE = not a two-stage sample.
pcfactor.ind
a logical value that indicates whether the population correction factor is used during variance estimation, where TRUE = use the population correction factor and FALSE = do not use the factor.
swgt.ind
a logical value that indicates whether the sample is a size-weighted sample, where TRUE = a size-weighted sample and FALSE = not a size-weighted sample.
vartype
the choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator.
conf
the confidence level.
pctval
the set of values at which percentiles are estimated. The default set is: {5, 25, 50, 75, 95}.

SIDE EFFECTS:

None

Author

Tom Kincaid Kincaid.Tom@epa.gov

REFERENCES:

Diaz-Ramos, S., D.L. Stevens, Jr., and A.R. Olsen. (1996). EMAP Statistical Methods Manual. EPA/620/R-96/XXX. Corvallis, OR: U.S. Environmental Protection Agency, Office of Research and Development, National Health Effects and Environmental Research Laboratory, Western Ecology Division.

SEE ALSO:

cat.analysis.fcn, cont.analysis.fcn

EXAMPLES:

# Categorical variable example
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("Good","Poor"), 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.cat <- data.frame(siteID=mysiteID, CatVar= rep(c("north", "south",
   "east", "west"), 25))
mypopsize <- list(All.Sites=c(Stratum1=3500, Stratum2=2000),
   Resource.Class=list(Good=c(Stratum1=2500, Stratum2=1500),
   Poor=c(Stratum1=1000, Stratum2=500)))
psurvey.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
   data.cat=mydata.cat, popsize=mypopsize)

# Continuous variable example - including deconvolution estimates
mydesign <- data.frame(ID=mysiteID, wgt=runif(100, 10, 100),
   xcoord=runif(100), ycoord=runif(100), stratum=rep(c("Stratum1",
   "Stratum2"), 50))
ContVar <- rnorm(100, 10, 1)
mydata.cont <- data.frame(siteID=mysiteID, ContVar=ContVar,
   ContVar.1=ContVar + rnorm(100, 0, sqrt(0.25)),
   ContVar.2=ContVar + rnorm(100, 0, sqrt(0.50)))
mysigma <- c(ContVar=NA, ContVar.1=0.25, ContVar.2=0.50)
psurvey.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
   data.cont=mydata.cont, siteID=~ID, sigma=mysigma,
   popsize=mypopsize)

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