Change Analysis for Probability Survey Data

Description

This function organizes input and output for analysis of change between two probability surveys.

Usage

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change.analysis(sites, repeats=NULL, subpop=NULL, design, data.cat=NULL,
   data.cont=NULL, revisitwgt=FALSE, test="mean", popsize_1=NULL,
   popsize_2=NULL, popcorrect_1=FALSE, popcorrect_2=FALSE, pcfsize_1=NULL,
   pcfsize_2=NULL, N.cluster_1=NULL, N.cluster_2=NULL, stage1size_1=NULL,
   stage1size_2=NULL, sizeweight_1=FALSE, sizeweight_2=FALSE, vartype_1="Local",
   vartype_2="Local", conf=95)

Arguments

sites

a data frame consisting of three variables: the first variable is site IDs, and the other variables are logical vectors indicating which sites to use in the analysis. The first logical vector indicates the complete set of sites for the first survey. The second logical vector indicates the complete set of sites for the second survey.

repeats

a data frame that identifies site IDs for repeat visit sites from the two surveys. The first variable is site IDs for survey one. The second variable is site IDs for survey two. For each row of the data frame, the two site IDs must correspond to the same site. This argument should equal NULL when repeat visit sites are not present. 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. 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.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.

revisitwgt

a logical value that indicates whether each repeat visit site has the same survey design weight in the two surveys, where TRUE = the weight for each repeat visit site is the same and FALSE = the weight for each repeat visit site is not the same. When this argument is FALSE, all of the repeat visit sites are assigned equal weights when calculating the covariance component of the change estimate standard error. The default is FALSE.

test

a character string or character vector providing the location measure(s) to use for change estimation for continuous variables. The choices are "mean", "median", or c("mean", "median"). The default is "mean".

popsize_1

known size of the resource for survey one, which is used to perform ratio adjustment to estimators expressed using measurement units for the resource and to calculate strata proportions for calculating estimates for a stratified sample. 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)

popsize_2

known size of the resource for survey two. The default is NULL.

popcorrect_1

a logical value that indicates whether finite or continuous population correction factors should be employed during variance estimation for survey one, 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_1 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_1 and stage1size_1 and for the support variable of the design argument.

popcorrect_2

a logical value that indicates whether finite or continuous population correction factors should be employed during variance estimation for survey two, 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_2 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_2 and stage1size_2 and for the support variable of the design argument.

pcfsize_1

size of the resource for survey one, 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.

pcfsize_2

size of the resource for survey two. The default is NULL.

N.cluster_1

the number of stage one sampling units in the resource for survey one, 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.

N.cluster_2

the number of stage one sampling units in the resource for survey two. The default is NULL.

stage1size_1

size of the stage one sampling units of a two-stage sample for survey one, 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.

stage1size_2

size of the stage one sampling units of a two-stage sample for survey two. The default is NULL.

sizeweight_1

a logical value that indicates whether size-weights should be used in the analysis of survey one, where TRUE = use the size-weights and FALSE = do not use the size-weights. The default is FALSE.

sizeweight_2

a logical value that indicates whether size-weights should be used in the analysis of survey two. The default is FALSE.

vartype_1

the choice of variance estimator for survey one, where "Local" = local mean estimator and "SRS" = SRS estimator. The default is "Local".

vartype_2

the choice of variance estimator for survey two. The default is "Local".

conf

the confidence level. The default is 95%.

Value

Value is a data frame of change estimates for all combinations of population Types, subpopulations within Types, response variables, and categories within each response variable (for categorical variables only). Estimates provided plus standard error and confidence interval estimates.

Author(s)

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

change.est

Examples

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# Categorical variable example for three resource classes:
mysiteID <- paste("Site", 1:200, sep="")
mysites <- data.frame(siteID=mysiteID,
                      Survey1=rep(c(TRUE, FALSE), c(100,100)),
                      Survey2=rep(c(FALSE, TRUE), c(100,100)))
myrepeats <- data.frame(siteID_1=paste("Site", 1:40, sep=""),
                        siteID_2=paste("Site", 101:140, sep=""))
mysubpop <- data.frame(siteID=mysiteID,
                       All_Sites=rep("All Sites", 200),
                       Region=rep(c("North","South"), 100))
mydesign <- data.frame(siteID=mysiteID,
                       wgt=runif(200, 10, 100),
                       xcoord=runif(200),
                       ycoord=runif(200),
                       stratum=rep(rep(c("Stratum1", "Stratum2"), c(2,2)), 50))
mydata.cat <- data.frame(siteID=mysiteID,
                         Resource_Class=sample(c("Good","Fair","Poor"),
                            200, replace=TRUE))
change.analysis(sites=mysites, repeats=myrepeats, subpop=mysubpop,
   design=mydesign, data.cat=mydata.cat, data.cont=NULL)

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