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spsurvey (version 4.0.0)

total.est: Estimators for Population Total, Mean, Variance, and Standard Deviation

Description

This function calculates estimates of the population total, mean, variance, and standard deviation of a response variable, where the response variable may be defined for either a finite or an extensive resource. In addition the standard error of the population estimates and confidence bounds are calculated. The Horvitz-Thompson estimator is used to calculate the total, variance, and standard deviation estimates. The Horvitz-Thompson ratio estimator, i.e., the ratio of two Horvitz-Thompson estimators, is used to calculate the mean estimate. Variance estimates are calculated using either the local mean variance estimator or the simple random sampling (SRS) variance estimator. The choice of variance estimator is subject to user control. The local mean variance estimator requires the x-coordinate and the y-coordinate of each site. The SRS variance estimator uses the independent random sample approximation to calculate joint inclusion probabilities. Confidence bounds are calculated using a Normal distribution multiplier. The function can accommodate a stratified sample. For a stratified sample, separate estimates and standard errors are calculated for each stratum, which are used to produce estimates and standard errors for all strata combined. Strata that contain a single value are removed. For a stratified sample, when either the size of the resource or the sum of the size-weights of the resource is provided for each stratum, those values are used as stratum weights for calculating the estimates and standard errors for all strata combined. For a stratified sample when neither the size of the resource nor the sum of the size-weights of the resource is provided for each stratum, estimated values are used as stratum weights for calculating the estimates and standard errors for all strata combined. The function can accommodate single-stage and two-stage samples for both stratified and unstratified sampling designs. Finite population and continuous population correction factors can be utilized in variance estimation. The function checks for compatibility of input values and removes missing values.

Usage

total.est(z, wgt, x = NULL, y = NULL, stratum = NULL,
  cluster = NULL, wgt1 = NULL, x1 = NULL, y1 = NULL,
  popsize = NULL, popcorrect = FALSE, pcfsize = NULL,
  N.cluster = NULL, stage1size = NULL, support = NULL,
  sizeweight = FALSE, swgt = NULL, swgt1 = NULL, vartype = "Local",
  conf = 95, check.ind = TRUE, warn.ind = NULL, warn.df = NULL,
  warn.vec = NULL)

Arguments

z

Vector of the response value for each site.

wgt

Vector of 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.

x

Vector of 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

Vector of 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.

stratum

Vector of the stratum for each site. The default is NULL.

cluster

Vector of the stage one sampling unit (primary sampling unit or cluster) code for each site. The default is NULL.

wgt1

Vector of the final adjusted stage one weight for each site. The default is NULL.

x1

Vector of the stage one x-coordinate for location for each site. The default is NULL.

y1

Vector of the stage one y-coordinate for location for each site. The default is NULL.

popsize

Known size of the resource, which is used 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. 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.

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 arguments pcfsize and support. To employ the correction factor for a two-stage sample, values must be supplied for arguments N.cluster, stage1size, and support.

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.

support

Vector of 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. 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.

swgt

Vector of the size-weight for each site, which is the stage two size-weight for a two-stage sample. The default is NULL.

swgt1

Vector of the stage one size-weight for each site. The default is NULL.

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.

check.ind

Logical value that indicates whether compatability checking of the input values is conducted, where TRUE = conduct compatibility checking and FALSE = do not conduct compatibility checking. The default is TRUE.

warn.ind

Logical value that indicates whether warning messages were generated, where TRUE = warning messages were generated and FALSE = warning messages were not generated. The default is NULL.

warn.df

Data frame for storing warning messages. The default is NULL.

warn.vec

Vector that contains names of the population type, the subpopulation, and an indicator. The default is NULL.

Value

If the function was called by the cont.analysis function, then output is an object in list format composed of the Results data frame, which contains estimates and confidence bounds, and the warn.df data frame, which contains warning messages. If the function was called directly, then output is the Results data frame.

Other Functions Required

input.check

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

wnas

remove missing values

vecprint

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

total.var

calculate variance of the total, mean, variance, and standard deviation estimates

Examples

Run this code
# NOT RUN {
z <- rnorm(100, 10, 1)
wgt <- runif(100, 10, 100)
total.est(z, wgt, vartype="SRS")

x <- runif(100)
y <- runif(100)
total.est(z, wgt, x, y)

# }

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