spsurvey (version 2.1-2)

cont.cdftest: Cumulative Distribution Function Inference for Probability Survey Data

Description

This function organizes input and output for conducting inference regarding cumulative distribution functions (CDFs) generated by a probability survey. Input can be either an object of class spsurvey.analysis (see the documentation for function spsurvey.analysis) or through use of the other arguments to this function.

Usage

cont.cdftest(sites=NULL, subpop=NULL, design=NULL, data.cont=NULL, popsize=NULL,
  popcorrect=FALSE, pcfsize=NULL, N.cluster=NULL, stage1size=NULL,
  sizeweight=FALSE, vartype="Local", testname="Wald_F", nclass=3,
  spsurvey.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 spsurvey.obj is not provided, then this argument is required. The def

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 spsurvey.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 weigh

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 spsurvey.obj is not provided, then this argument is required. The default is N

popsize

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 s

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

pcfsize

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 h

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

stage1size

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 samp

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.

vartype

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

testname

name of the test statistic to be reported in the output data frame. Choices for the name are: "Wald", "Wald_F", "Mean_Eigenvalue", "Mean_Eigenvalue_F", "Satterthwaite", and "Satterthwaite_F". The default is "Wald_F".

nclass

number of classes for the CDF. The default is 3.

spsurvey.obj

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

Value

• A data frame of CDF test results for all pairs of subpopulations within each population Type for every response variable. The data frame includes the test statistic specified by argument testname plus its degrees of freedom and p-value.

Details

For every response variable and every population Type, differences between CDFs are tested for every pair of subpopulations within a Type. The inferential procedures divide the CDFs into a discrete set of intervals (classes) and then utilize procedures that have been developed for analysis of categorical data from probability surveys. Choices for inference are the Wald, Rao-Scott first order corrected (mean eigenvalue corrected), and Rao-Scott second order corrected (Satterthwaite corrected) test statistics. Both standard versions of the three statistics, which are distributed as Chi-squared random variables, and alternate version of the statistics, which are distributed as F random variables, are available. The default test statistic is the F distribution version of the Wald statistic.

References

Kincaid, T.M. (2000). Testing for differences between cumulative distribution functions from complex environmental sampling surveys. In 2000 Proceeding of the Section on Statistics and the Environment, American Statistical Association, Alexandria, VA.

See Also

cdf.test

Examples

n <- 200
mysiteID <- paste("Site", 1:n, sep="")
mysites <- data.frame(siteID=mysiteID, Active=rep(TRUE, n))
mysubpop <- data.frame(siteID=mysiteID,  Resource_Class=sample(c("Agr",
  "Forest", "Urban"), n, replace=TRUE))
mydesign <- data.frame(siteID=mysiteID, wgt=runif(n, 10, 100),
  xcoord=runif(n), ycoord=runif(n), stratum=rep(c("Stratum1",
  "Stratum2"), n/2))
mypopsize <- list(Resource_Class=list(Agr=c(Stratum1=2500, Stratum2=1500),
  Forest=c(Stratum1=1000, Stratum2=500), Urban=c(Stratum1=600, Stratum2=450)))
ContVar <- numeric(n)
tst <- mysubpop$Resource_Class == "Agr"
ContVar[tst] <- rnorm(sum(tst), 10, 1)
tst <- mysubpop$Resource_Class == "Forest"
ContVar[tst] <- rnorm(sum(tst), 10.1, 1)
tst <- mysubpop$Resource_Class == "Urban"
ContVar[tst] <- rnorm(sum(tst), 10.5, 1)
mydata.cont <- data.frame(siteID=mysiteID, ContVar=ContVar)
cont.cdftest(sites=mysites, subpop=mysubpop, design=mydesign,
  data.cont=mydata.cont, popsize=mypopsize, testname="Mean_Eigenvalue")