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

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 ide

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 wi

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. 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. 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 def

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

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 calcu

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

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

support

the support value for each site - 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 pop

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.

swgt

the size-weight for each site, which is the stage two size-weight for a two-stage sample. This variable can be input directly or as a formula. The default is NULL.

swgt1

the stage one size-weight for each site. This variable can be input directly or as a formula. 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

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, 10, 25, 50, 75, 90, 95}.

Value

Value is 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 list is composed of the following components:

sites- the sites data frame
subpop- the subpop data frame
design- the design data frame
data.cat- the data.cat data frame
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, where the default set is: {5, 25, 50, 75, 95}

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.

Examples

Run this code

# 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)