spsurvey (version 1.6.2)

cont.analysis: Continuous Data Analysis for Probability Survey Data

Description

This function organizes input and output for analysis of continuous data generated by a probability survey. Input can be either an object belonging to class psurvey.analysis (see the documentation for function psurvey.analysis) or through use of the other arguments to this function.

Usage

cont.analysis(sites=NULL, subpop=NULL, design=NULL, data.cont=NULL, sigma=NULL,
   var.sigma=NULL, N.cluster=NULL, popsize=NULL, stage1size=NULL,
   popcorrect=FALSE, sizeweight=FALSE, unitsize=NULL, vartype="Local", conf=95,
   pctval=c(5,10,25,50,75,90,95), psurvey.obj=NULL)

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 psurvey.obj is not provided, then this argument is required. The defa

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 identif

design

a data frame consisting of design variables. If psurvey.obj is not provided, then this argument is required. The default is NULL. Variables should be named as follows: siteID = site IDs wgt = final adjusted weights, which are either the weight

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 NU

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

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

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

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.

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}.

psurvey.obj

A list of class psurvey.analysis that was produced by the function psurvey.analysis. Depending on input to that function, some elements of the list may be NULL. The default is NULL.

Value

Value is a list containing either two or four data frames of population estimates for all combinations of population Types, subpopulations within Types, and response variables. The data frames containing deconvoluted CDF estimates and deconvoluted percentile estimates are only included in the output list when an input value for measurement error variance is provided to the function. CDF estimates are calculated for both proportion and size of the population. Standard error estimates and confidence interval estimates also are calculated. The four data frames are:

• CDF- a data frame containing the CDF estimates
• Pct- a data frame containing the percentile estimates plus population mean, standard deviation, and variance estimates
• CDF.D- a data frame containing the deconvoluted CDF estimates
• Pct.D- a data frame containing the deconvoluted percentile estimates

Details

CDF estimates are calculated for all unique values of a response variable.

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

cdf.est, total.est

Examples

# Continuous 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))
ContVar <- rnorm(100, 10, 1)
mydata.cont <- data.frame(siteID=mysiteID, ContVar=ContVar)
mypopsize <- list(All.Sites=c(Stratum1=3500, Stratum2=2000),
   Resource.Class=list(Good=c(Stratum1=2500, Stratum2=1500),
   Poor=c(Stratum1=1000, Stratum2=500)))
cont.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
   data.cont=mydata.cont, popsize=mypopsize)

# Include deconvolution estimates
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(NA, 0.25, 0.50)
names(mysigma) <- c("ContVar", "ContVar.1", "ContVar.2")
cont.analysis(sites=mysites, subpop=mysubpop[,1:2], design=mydesign,
   data.cont=mydata.cont, sigma=mysigma, popsize=mypopsize[1])