# Change Analysis for Probability Survey Data

### Description

This function organizes input and output for analysis of change between two probability surveys.

### Usage

1 2 3 4 5 6 | ```
change.analysis(sites, repeats=NULL, subpop=NULL, design, data.cat=NULL,
data.cont=NULL, revisitwgt=FALSE, test="mean", popsize_1=NULL,
popsize_2=NULL, popcorrect_1=FALSE, popcorrect_2=FALSE, pcfsize_1=NULL,
pcfsize_2=NULL, N.cluster_1=NULL, N.cluster_2=NULL, stage1size_1=NULL,
stage1size_2=NULL, sizeweight_1=FALSE, sizeweight_2=FALSE, vartype_1="Local",
vartype_2="Local", conf=95)
``` |

### Arguments

`sites` |
a data frame consisting of three variables: the first variable is site IDs, and the other variables are logical vectors indicating which sites to use in the analysis. The first logical vector indicates the complete set of sites for the first survey. The second logical vector indicates the complete set of sites for the second survey. |

`repeats` |
a data frame that identifies site IDs for repeat visit sites from the two surveys. The first variable is site IDs for survey one. The second variable is site IDs for survey two. For each row of the data frame, the two site IDs must correspond to the same site. This argument should equal NULL when repeat visit sites are not present. The default is NULL. |

`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 identify Type. A Type variable identifies each site with one of the subpopulations of that Type. The default is NULL. |

`design` |
a data frame consisting of design variables. Variables should
be named as follows: |

`data.cat` |
a data frame of categorical response variables. The first variable is site IDs. Subsequent variables are response variables. Missing data (NA) is allowed. The default is NULL. |

`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. The default is NULL. |

`revisitwgt` |
a 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". |

`popsize_1` |
known size of the resource for survey one, 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. |

`popsize_2` |
known size of the resource for survey two. 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 argument pcfsize_1 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_1 and stage1size_1 and for the support variable of the design argument. |

`popcorrect_2` |
a 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 argument pcfsize_2 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_2 and stage1size_2 and for the support variable of the design argument. |

`pcfsize_1` |
size of the resource for survey one, 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. |

`pcfsize_2` |
size of the resource for survey two. The default is NULL. |

`N.cluster_1` |
the number of stage one sampling units in the resource for survey one, 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. |

`N.cluster_2` |
the number of stage one sampling units in the resource for survey two. 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. |

`stage1size_2` |
size of the stage one sampling units of a two-stage sample for survey two. The default is NULL. |

`sizeweight_1` |
a logical value that indicates whether size-weights should be used in the analysis of survey one, where TRUE = use the size-weights and FALSE = do not use the size-weights. The default is FALSE. |

`sizeweight_2` |
a logical value that indicates whether size-weights should be used in the analysis of survey two. The default is FALSE. |

`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` |
the confidence level. The default is 95%. |

### Value

Value is a data frame of change estimates for all combinations of population Types, subpopulations within Types, response variables, and categories within each response variable (for categorical variables only). Estimates provided plus standard error and confidence interval estimates.

### Author(s)

Tom Kincaid Kincaid.Tom@epa.gov

### 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

`change.est`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# Categorical variable example for three resource classes:
mysiteID <- paste("Site", 1:200, sep="")
mysites <- data.frame(siteID=mysiteID,
Survey1=rep(c(TRUE, FALSE), c(100,100)),
Survey2=rep(c(FALSE, TRUE), c(100,100)))
myrepeats <- data.frame(siteID_1=paste("Site", 1:40, sep=""),
siteID_2=paste("Site", 101:140, sep=""))
mysubpop <- data.frame(siteID=mysiteID,
All_Sites=rep("All Sites", 200),
Region=rep(c("North","South"), 100))
mydesign <- data.frame(siteID=mysiteID,
wgt=runif(200, 10, 100),
xcoord=runif(200),
ycoord=runif(200),
stratum=rep(rep(c("Stratum1", "Stratum2"), c(2,2)), 50))
mydata.cat <- data.frame(siteID=mysiteID,
Resource_Class=sample(c("Good","Fair","Poor"),
200, replace=TRUE))
change.analysis(sites=mysites, repeats=myrepeats, subpop=mysubpop,
design=mydesign, data.cat=mydata.cat, data.cont=NULL)
``` |