cont.analysis: Continuous Variable Data Analysis for Probability Survey Data

In mhweber/spsurvey: Spatial Survey Design and Analysis

Description

This function organizes input and output for analysis of continuous data 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.analysis(sites = NULL, subpop = NULL, design = NULL,
  data.cont = NULL, sigma = NULL, var.sigma = NULL, popsize = NULL,
  popcorrect = FALSE, pcfsize = NULL, N.cluster = NULL,
  stage1size = NULL, sizeweight = FALSE, total = FALSE,
  vartype = "Local", conf = 95, pctval = c(5, 10, 25, 50, 75, 90,
  95), spsurvey.obj = NULL)

Arguments

sites	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 default is NULL.
subpop	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. If spsurvey.obj is not provided, then this argument is required. The default is NULL.
design	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 Vector of site IDs wgt Vector of weights, which are either the weights for a single-stage sample or the stage two weights for a two-stage sample xcoord Vector of 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 Vector of 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 Vector of the stratum codes for each site cluster Vector of the stage one sampling unit (primary sampling unit or cluster) codes for each site wgt1 Vector of stage one weights in a two-stage design xcoord1 Vector of the stage one x-coordinates for location in a two-stage design ycoord1 Vector of the stage one y-coordinates for location in a two-stage design support Vector of support values - for a finite resource, the value one (1) for a for site. For an extensive resource, the measure of the sampling unit associated with a site. Required for calculation of finite and continuous population correction factors. swgt Vector of size-weights, which is the stage two size-weight for a two-stage design. swgt1 Vector of stage one size-weights for a two-stage design.
data.cont	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.
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 default is NULL.
popsize	Known size of the resource, 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)
popcorrect	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.
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 have the names attribute set to identify the stratum codes. 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 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.
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 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.
sizeweight	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.
total	Logical value that indicates whether the population total estimate should be included in the output Pct data frame, where TRUE = include the total estimate and FALSE = do not include the estimate. The default is FALSE.
vartype	The choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator. The default is "Local".
conf	Numeric value for the confidence level. The default is 95.
pctval	Vector of the set of values at which percentiles are estimated. The default set is: 5, 10, 25, 50, 75, 90, 95.
spsurvey.obj	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 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

data frame containing the CDF estimates

Pct

data frame containing the percentile estimates plus population mean, standard deviation, and variance estimates

CDF.D

data frame containing the deconvoluted CDF estimates

Pct.D

data frame containing the deconvoluted percentile estimates

If an input value for measurement error variance is not provided to the function, then CDF.D and Pct.D are assigned the value NULL.

Other Functions Required

dframe.check

check site IDs, the sites data frame, the subpop data frame, and the data.cat data frame to assure valid contents and, as necessary, create the sites data frame and the subpop data frame

vecprint

takes an input vector and outputs a character string with line breaks inserted

uniqueID

creates unique site IDs by appending a unique number to each occurrence of a site ID

input.check

check input values for errors, consistency, and compatibility with analytical functions

cdf.est

estimate the cumulative distribution function (CDF) for the proportion (expressed as percent) and the total of a response variable

cdf.decon

estimate the deconvoluted CDF for the proportion and the total of a response variable

total.est

estimate the population total, mean, variance, and standard deviation of a response variable

Author(s)

Tony Olsen [e-mail]
Tom Kincaid [e-mail]

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,
  ContVar1=ContVar + rnorm(100, 0, sqrt(0.25)),
  ContVar2=ContVar + rnorm(100, 0, sqrt(0.50)))
mysigma <- c(ContVar=NA, ContVar1=0.25, ContVar2=0.50)
cont.analysis(sites=mysites, subpop=mysubpop[,1:2], design=mydesign,
  data.cont=mydata.cont, sigma=mysigma, popsize=mypopsize[1])

mhweber/spsurvey documentation built on Sept. 17, 2020, 4:24 a.m.