change.est: Estimate Change between Two Surveys
In mhweber/spsurvey: Spatial Survey Design and Analysis

Description Usage Arguments Value Other Functions Required Author(s) Examples

This function estimates change between two probability surveys. The function can accommodate both categorical and continuous response variables. For a categorical response variable, change is estimated by the difference in category estimates for the two surveys, where a category estimate is the estimated proportion of values in a category. Note that a separate change estimate is calculated for each category of a categorical response variable. For a continuous response variable, change can be estimated for the mean, the median, or for both the mean and median. For a continuous response variable using the mean, change is estimated by the difference in estimated mean values for the two surveys. For change estimates using the median, the first step is to calculate an estimate of the median for the first survey. The estimated median from the first survey is then used to define two categories: (1) values that are less than or equal to the estimated median and (2) values that are greater than the estimated median. Once the categories are defined, change analysis for the median is identical to change analysis for a categorical variable, i.e., change is estimated by the difference in category estimates for the two surveys. In addition to change estimates, the standard error of the change estimates and confidence bounds are calculated. 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 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. It is assumed that both surveys employ the same type of survey design. 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.

change.est(resp.ind, z_1, wgt_1, x_1 = NULL, y_1 = NULL, repeat_1, z_2,
  wgt_2, x_2 = NULL, y_2 = NULL, repeat_2, revisitwgt = FALSE,
  test = "mean", stratum_1 = NULL, stratum_2 = NULL,
  cluster_1 = NULL, cluster_2 = NULL, wgt1_1 = NULL, x1_1 = NULL,
  y1_1 = NULL, wgt1_2 = NULL, x1_2 = NULL, y1_2 = NULL,
  popsize_1 = NULL, popsize_2 = NULL, popcorrect_1 = FALSE,
  pcfsize_1 = NULL, N.cluster_1 = NULL, stage1size_1 = NULL,
  support_1 = NULL, popcorrect_2 = FALSE, pcfsize_2 = NULL,
  N.cluster_2 = NULL, stage1size_2 = NULL, support_2 = NULL,
  sizeweight_1 = FALSE, swgt_1 = NULL, swgt1_1 = NULL,
  sizeweight_2 = FALSE, swgt_2 = NULL, swgt1_2 = NULL,
  vartype_1 = "Local", vartype_2 = "Local", conf = 95,
  check.ind = TRUE, warn.ind = NULL, warn.df = NULL,
  warn.vec = NULL)

`resp.ind`	A character value that indicates the type of response variable, where "cat" indicates a categorical variable and "cont" indicates a continuous variable.
`z_1`	Vector of response value for each survey one site.
`wgt_1`	Vector of final adjusted weight (inverse of the sample inclusion probability) for each survey one site, which is either the weight for a single- stage sample or the stage two weight for a two-stage sample.
`x_1`	Vector of x-coordinate for location for each survey one 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_1`	Vector of y-coordinate for location for each survey one 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.
`repeat_1`	Logical variable that identifies repeat visit sites for survey one.
`z_2`	Vector of response value for each survey two site.
`wgt_2`	Vector of final adjusted weight for each survey two site.
`x_2`	Vector of x-coordinate for location for each survey two site. The default is NULL.
`y_2`	Vector of y-coordinate for location for each survey two site. The default is NULL.
`repeat_2`	Logical variable that identifies repeat visit sites for survey two.
`revisitwgt`	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".
`stratum_1`	Vector of the stratum for each survey one site. The default is NULL.
`stratum_2`	Vector of the stratum for each survey two site. The default is NULL.
`cluster_1`	Vector of the stage one sampling unit (primary sampling unit or cluster) code for each survey one site. The default is NULL.
`cluster_2`	Vector of the stage one sampling unit (primary sampling unit or cluster) code for each survey two site. The default is NULL.
`wgt1_1`	Vector of the final adjusted stage one weight for each survey one site. The default is NULL.
`x1_1`	Vector of the stage one x-coordinate for location for each survey one site. The default is NULL.
`y1_1`	Vector of the stage one y-coordinate for location for each survey one site. The default is NULL.
`wgt1_2`	Vector of the final adjusted stage one weight for each survey two site. The default is NULL.
`x1_2`	Vector of the stage one x-coordinate for location for each survey two site. The default is NULL.
`y1_2`	Vector of the stage one y-coordinate for location for each survey two site. The default is NULL.
`popsize_1`	The known size of the survey one resource - the total number of sampling units of a finite resource or the measure of an extensive resource, which is required for calculation of finite and continuous population correction factors for a single-stage sample. For a stratified sample, this variable also is used to calculate strata weights. 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.
`popsize_2`	The known size of the survey two resource. 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 arguments pcfsize_1 and support_1. To employ the correction factor for a two-stage sample, values must be supplied for arguments N.cluster_1, stage1size_1, and support_1.
`pcfsize_1`	Size of the survey one 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_1`	The number of stage one sampling units in the survey one 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 value for each stratum and must have the names attribute set to identify the stratum codes. 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.
`support_1`	Vector of the support value for each survey one 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.
`popcorrect_2`	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 arguments pcfsize_2 and support_2. To employ the correction factor for a two-stage sample, values must be supplied for arguments N.cluster_2, stage1size_2, and support_2.
`pcfsize_2`	Size of the survey two resource. The default is NULL.
`N.cluster_2`	The number of stage one sampling units in the survey two resource. The default is NULL.
`stage1size_2`	Size of the stage one sampling units of a two-stage survey for survey two. The default is NULL.
`support_2`	Vector of the support value for each survey two site. The default is NULL.
`sizeweight_1`	Logical value that indicates whether size-weights should be used in the analysis for survey one, where TRUE = use the size-weights and FALSE = do not use the size-weights. The default is FALSE.
`swgt_1`	Vector of size-weight for each survey one site, which is the stage two size-weight for a two-stage sample. The default is NULL.
`swgt1_1`	Vector of the stage one size-weight for each survey one site. The default is NULL.
`sizeweight_2`	Logical value that indicates whether size-weights should be used in the analysis for survey two. The default is FALSE.
`swgt_2`	Vector of the size-weight for each survey two site. The default is NULL.
`swgt1_2`	Vector of the stage one size-weight for each survey two site. The default is NULL.
`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`	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.

If the function was called by the change.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.

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
category.est: estimate proportion (expressed as percent) and size of a resource in each of a set of categories
changevar.prop: calculate covariance or correlation estimates of the estimated change in class proportion estimates between two probability surveys
cdf.est: estimate the cumulative distribution function (CDF) for the proportion (expressed as percent) and the total of a response variable
total.est: estimate the population total, mean, variance, and standard deviation of a response variable
changevar.mean: calculate the covariance or correlation estimate of the estimated change in means between two probability surveys

Tom Kincaid Kincaid.Tom@epa.gov

z_1 <- sample(c("Good","Fair","Poor"), 100, replace=TRUE)
z_2 <- sample(c("Good","Fair","Poor"), 100, replace=TRUE)
wgt_1 <- runif(100, 10, 100)
wgt_2 <- runif(100, 10, 100)
repeat_1 <- rep(c(TRUE,FALSE), c(20,80))
repeat_2 <- rep(c(TRUE,FALSE), c(20,80))
change.est(resp.ind="cat", z_1=z_1, wgt_1=wgt_1, repeat_1=repeat_1,
  z_2=z_2, wgt_2=wgt_2, repeat_2=repeat_2, vartype_1="SRS", vartype_2="SRS")

z_1 <- rnorm(100, 10,10)
z_2 <- rnorm(100, 12, 10)
change.est(resp.ind="cont", z_1=z_1, wgt_1=wgt_1, repeat_1=repeat_1,
  z_2=z_2, wgt_2=wgt_2, repeat_2=repeat_2, vartype_1="SRS", vartype_2="SRS")