This function creates an object of class spsurvey.analysis that contains all of the information necessary to use the analysis functions in the spsurvey library.
spsurvey.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, wgt1=NULL, xcoord1=NULL,
ycoord1=NULL, popsize=NULL, popcorrect=FALSE, pcfsize=NULL, N.cluster=NULL,
stage1size=NULL, support=NULL, sizeweight=FALSE, swgt=NULL, swgt1=NULL,
vartype="Local", conf=95, pctval=c(5,10,25,50,75,90,95))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. 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 variable named use.sites that contains the value TRUE for all sites is created. 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 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 contains the value "All Sites" for all sites is created. The default is NULL.
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 xcoord = x-coordinates for location ycoord = y-coordinates for location stratum = stratum codes cluster = stage one sampling unit codes wgt1 = final adjusted stage one weights xcoord1 = stage one x-coordinates for location ycoord1 = stage one y-coordinates for location support = support values swgt = size-weights swgt1 = stage one size-weights
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.
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.
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.
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.
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.
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.
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.
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.
the stratum codes. This variable can be input directly or as a formula. The default is NULL.
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.
the final adjusted stage one weights. This variable can be input directly or as a formula. The default is NULL.
the stage one x-coordinates for location. This variable can be input directly or as a formula. The default is NULL.
the stage one y-coordinates for location. This variable can be input directly or as a formula. The default is NULL.
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)
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.
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 population correction factors. This variable can be input directly or as a formula. 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 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.
the stage one size-weight for each site. This variable can be input directly or as a formula. The default is NULL.
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%.
the set of values at which percentiles are estimated. The default set is: {5, 10, 25, 50, 75, 90, 95}.
Value is a list of class spsurvey.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
sigma - measurement error variance
var.sigma - variance of the estimated measurement error
variance
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
popsize - the known size of the resource
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
pcfsize - size of the resource, which is required for
calculation of finite and continuous population correction factors for a
single-stage sample
N.cluster - the number of stage one sampling units in the
resource
stage1size - the known size of the stage one sampling units
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
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.
# NOT RUN {
# 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)))
spsurvey.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)
spsurvey.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
data.cont=mydata.cont, siteID=~ID, sigma=mysigma,
popsize=mypopsize)
# }
Run the code above in your browser using DataLab