change_analysis: Change analysis
In spsurvey: Spatial Sampling Design and Analysis
View source: R/change_analysis.R
change_analysis R Documentation
Change analysis
Description
This function organizes input and output for the estimation of change between two
samples (for categorical and continuous variables). The analysis data,
dframe, can be either a data frame or a simple features (sf) object. If an
sf object is used, coordinates are extracted from the geometry column in the
object, arguments xcoord and ycoord are assigned values
"xcoord" and "ycoord", respectively, and the geometry column is
dropped from the object.
Usage
change_analysis(
dframe,
vars_cat = NULL,
vars_cont = NULL,
test = "mean",
subpops = NULL,
surveyID = "surveyID",
survey_names = NULL,
siteID = "siteID",
weight = "weight",
revisitwgt = FALSE,
xcoord = NULL,
ycoord = NULL,
stratumID = NULL,
clusterID = NULL,
weight1 = NULL,
xcoord1 = NULL,
ycoord1 = NULL,
sizeweight = FALSE,
sweight = NULL,
sweight1 = NULL,
fpc = NULL,
popsize = NULL,
vartype = "Local",
jointprob = "overton",
conf = 95,
All_Sites = FALSE
)
Arguments
dframe
Data to be analyzed (analysis data). A data frame or
sf object containing survey design variables, response
variables, and subpopulation (domain) variables.
vars_cat
Vector composed of character values that identify the
names of categorical response variables in dframe. The
default is NULL.
vars_cont
Vector composed of character values that identify the
names of continuous response variables in dframe. The
default is NULL.
test
Character string or character vector providing the location
measure(s) to use for change estimation for continuous variables. The
choices are "mean", "total", "median", or some
combination of the three options (e.g., c("mean", "total")).
The default is "mean".
subpops
Vector composed of character values that identify the
names of subpopulation (domain) variables in dframe.
If a value is not provided, the value "All_Sites" is assigned to the
subpops argument and a factor variable named "All_Sites" that takes
the value "All Sites" is added to dframe. The
default value is NULL.
surveyID
Character value providing name of the survey ID variable in
dframe. The default value is "surveyID".
survey_names
Character vector of length two that provides the survey
names contained in the surveyID variable in the dframe data
frame. The two values in the vector identify the first survey and second
survey, respectively. If a value is not provided, unique values of the
surveyID variable are assigned to the survey_names argument.
The default is NULL.
siteID
Character value providing name of the site ID variable in
dframe. For a two-stage sample, the site ID variable
identifies stage two site IDs. The default value is "siteID". If a
unique site is visited in both surveys, the corresponding siteID
should be the same for both entries.
weight
Character value providing name of the design weight
variable in dframe. For a two-stage sample, the
weight variable identifies stage two weights. The default value is
"weight".
revisitwgt
Logical value that indicates whether each repeat visit
site has the same 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.
xcoord
Character value providing name of the x-coordinate variable in
dframe. For a two-stage sample, the x-coordinate
variable identifies stage two x-coordinates. Note that x-coordinates are
required for calculation of the local mean variance estimator. If dframe
is an sf object, this argument is not required (as the geometry column
in dframe is used to find the x-coordinate). The default
value is NULL.
ycoord
Character value providing name of the y-coordinate variable in
dframe. For a two-stage sample, the y-coordinate
variable identifies stage two y-coordinates. Note that y-coordinates are
required for calculation of the local mean variance estimator. If dframe
is an sf object, this argument is not required (as the geometry column
in dframe is used to find the y-coordinate). The default
value is NULL.
stratumID
Character value providing name of the stratum ID variable in
dframe. The default value is NULL.
clusterID
Character value providing the name of the cluster
(stage one) ID variable in dframe. Note that cluster
IDs are required for a two-stage sample. The default value is NULL.
weight1
Character value providing name of the stage one weight
variable in dframe. The default value is NULL.
xcoord1
Character value providing the name of the stage one
x-coordinate variable in dframe. Note that x
coordinates are required for calculation of the local mean variance
estimator. The default value is NULL.
ycoord1
Character value providing the name of the stage one
y-coordinate variable in dframe. Note that
y-coordinates are required for calculation of the local mean variance
estimator. The default value is NULL.
sizeweight
Logical value that indicates whether size weights should be
used during estimation, where TRUE uses size weights and
FALSE does not use size weights. To employ size weights for a
single-stage sample, a value must be supplied for argument weight. To
employ size weights for a two-stage sample, values must be supplied for
arguments weight and weight1. The default value is FALSE.
sweight
Character value providing the name of the size weight variable
in dframe. For a two-stage sample, the size weight
variable identifies stage two size weights. The default value is
NULL.
sweight1
Character value providing name of the stage one size weight
variable in dframe. The default value is NULL.
fpc
Object that specifies values required for calculation of the
finite population correction factor used during variance estimation. The
object must match the survey design in terms of stratification and whether
the design is single-stage or two-stage. For an unstratified design, the
object is a vector. The vector is composed of a single numeric value for a
single-stage design. For a two-stage unstratified design, the object is a
named vector containing one more than the number of clusters in the sample,
where the first item in the vector specifies the number of clusters in the
population and each subsequent item specifies the number of stage two units
for the cluster. The name for the first item in the vector is arbitrary.
Subsequent names in the vector identify clusters and must match the cluster
IDs. For a stratified design, the object is a named list of vectors, where
names must match the strata IDs. For each stratum, the format of the
vector is identical to the format described for unstratified single-stage
and two-stage designs. Note that the finite population correction factor
is not used with the local mean variance estimator.
Example fpc for a single-stage unstratified survey design:
fpc <- 15000
Example fpc for a single-stage stratified survey design:
fpc <- list(
Stratum_1 = 9000,
Stratum_2 = 6000)
Example fpc for a two-stage unstratified survey design:
fpc <- c(
Ncluster = 150,
Cluster_1 = 150,
Cluster_2 = 75,
Cluster_3 = 75,
Cluster_4 = 125,
Cluster_5 = 75)
Example fpc for a two-stage stratified survey design:
fpc <- list(
Stratum_1 = c(
Ncluster_1 = 100,
Cluster_1 = 125,
Cluster_2 = 100,
Cluster_3 = 100,
Cluster_4 = 125,
Cluster_5 = 50),
Stratum_2 = c(
Ncluster_2 = 50,
Cluster_1 = 75,
Cluster_2 = 150,
Cluster_3 = 75,
Cluster_4 = 75,
Cluster_5 = 125))
popsize
Object that provides values for the population argument of the
calibrate or postStratify functions in the survey package. If
a value is provided for popsize, then either the calibrate or
postStratify function is used to modify the survey design object
that is required by functions in the survey package. Whether to use the
calibrate or postStratify function is dictated by the format
of popsize, which is discussed below. Post-stratification adjusts the
sampling and replicate weights so that the joint distribution of a set of
post-stratifying variables matches the known population joint distribution.
Calibration, generalized raking, or GREG estimators generalize
post-stratification and raking by calibrating a sample to the marginal
totals of variables in a linear regression model. For the calibrate
function, the object is a named list, where the names identify factor
variables in dframe. Each element of the list is a
named vector containing the population total for each level of the
associated factor variable. For the postStratify function, the
object is either a data frame, table, or xtabs object that provides the
population total for all combinations of selected factor variables in the
dframe data frame. If a data frame is used for popsize, the
variable containing population totals must be the last variable in the data
frame. If a table is used for popsize, the table must have named
dimnames where the names identify factor variables in the
dframe data frame. If the popsize argument is equal to NULL,
then neither calibration nor post-stratification is performed. The default
value is NULL.
Example popsize for calibration:
popsize <- list(
Ecoregion = c(
East = 750,
Central = 500,
West = 250),
Type = c(
Streams = 1150,
Rivers = 350))
Example popsize for post-stratification using a data frame:
popsize <- data.frame(
Ecoregion = rep(c("East", "Central", "West"),
rep(2, 3)),
Type = rep(c("Streams", "Rivers"), 3),
Total = c(575, 175, 400, 100, 175, 75))
Example popsize for post-stratification using a table:
popsize <- with(MySurveyFrame,
table(Ecoregion, Type))
Example popsize for post-stratification using an xtabs object:
popsize <- xtabs(~Ecoregion + Type,
data = MySurveyFrame)
vartype
Character value providing the choice of the variance
estimator, where "Local" indicates the local mean estimator and
"SRS" indicates the simple random sampling estimator. The default
value is "Local".
jointprob
Character value providing the choice of joint inclusion
probability approximation for use with Horvitz-Thompson and Yates-Grundy
variance estimators, where "overton" indicates the Overton
approximation, "hr" indicates the Hartley-Rao approximation, and
"brewer" equals the Brewer approximation. The default value is
"overton".
conf
Numeric value providing the Gaussian-based confidence level. The default value
is 95.
All_Sites
A logical variable used when subpops is not
NULL. If All_Sites is TRUE, then alongside the
subpopulation output, output for all sites (ignoring subpopulations) is
returned for each variable in vars. If All_Sites is
FALSE, then alongside the subpopulation output, output for all sites
(ignoring subpopulations) is not returned for each variable in vars.
The default is FALSE.
Value
List of change estimates composed of four items:
(1) catsum contains change estimates for categorical variables,
(2) contsum_mean contains estimates for continuous variables using
the mean, (3) contsum_total contains estimates for continuous
variables using the total, and (4) contsum_median contains estimates for continuous
variables using the median. The items in the list will contain NULL
for estimates that were not calculated. Each data frame includes estimates
for all combinations of population Types, subpopulations within types,
response variables, and categories within each response variable (for
categorical variables and continuous variables using the median). Change
estimates are provided plus standard error estimates and confidence
interval estimates.
The catsum data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Category
category of response variable
- DiffEst.P
proportion difference estimate (in %; second survey - first survey)
- StdError.P
standard error of proportion difference estimate
- MarginofError.P
margin of error of proportion difference estimate
- LCBxxPct.P
xx% (default 95%) lower confidence bound of proportion difference estimate
- UCBxxPct.P
xx% (default 95%) upper confidence bound of proportion difference estimate
- Estimate.U
total difference estimate (second survey - first survey)
- StdError.U
standard error of total difference estimate
- MarginofError.U
margin of error of total difference estimate
- LCBxxPct.U
xx% (default 95%) lower confidence bound of total difference estimate
- UCBxxPct.U
xx% (default 95%) upper confidence bound of total difference estimate
- nResp_1
sample size in the first survey
- Estimate.P_1
proportion estimate (in %) from the first survey
- StdError.P_1
standard error of proportion estimate from the first survey
- MarginofError.P_1
margin of error of proportion estimate from the first survey
- LCBxxPct.P_1
xx% (default 95%) lower confidence bound of proportion estimate from the first survey
- UCBxxPct.P_1
xx% (default 95%) upper confidence bound of proportion estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate.U_1
total estimate from the first survey
- StdError.U_1
standard error of total estimate from the first survey
- MarginofError.U_1
margin of error of total estimate from the first survey
- LCBxxPct.U_1
xx% (default 95%) lower confidence bound of total estimate from the first survey
- UCBxxPct.U_1
xx% (default 95%) upper confidence bound of total estimate from the first survey
- Estimate.P_2
proportion estimate (in %) from the second survey
- StdError.P_2
standard error of proportion estimate from the second survey
- MarginofError.P_2
margin of error of proportion estimate from the second survey
- LCBxxPct.P_2
xx% (default 95%) lower confidence bound of proportion estimate from the second survey
- UCBxxPct.P_2
xx% (default 95%) upper confidence bound of proportion estimate from the second survey
- Estimate.U_2
total estimate from the second survey
- StdError.U_2
standard error of total estimate from the second survey
- MarginofError.U_2
margin of error of total estimate from the second survey
- LCBxxPct.U_2
xx% (default 95%) lower confidence bound of total estimate from the second survey
- UCBxxPct.U_2
xx% (default 95%) upper confidence bound of total estimate from the second survey
The contsum_mean data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Statistic
value of percentile
- nResp
sample size at or below Value
- DiffEst
mean difference estimate
- StdError
standard error of mean difference estimate
- MarginofError
margin of error of mean difference estimate
- LCBxxPct
xx% (default 95%) lower confidence bound of mean difference estimate
- UCBxxPct
xx% (default 95%) upper confidence bound of mean difference estimate
- nResp_1
sample size in the first survey
- Estimate_1
mean estimate from the first survey
- StdError_1
standard error of mean estimate from the first survey
- MarginofError_1
margin of error of mean estimate from the first survey
- LCBxxPct_1
xx% (default 95%) lower confidence bound of mean estimate from the first survey
- UCBxxPct_1
xx% (default 95%) upper confidence bound of mean estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate_2
mean estimate from the second survey
- StdError_2
standard error of mean estimate from the second survey
- MarginofError_2
margin of error of mean estimate from the second survey
- LCBxxPct_2
xx% (default 95%) lower confidence bound of mean estimate from the second survey
- UCBxxPct_2
xx% (default 95%) upper confidence bound of mean estimate from the second survey
The contsum_total data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Statistic
value of percentile
- nResp
sample size at or below Value
- DiffEst
total difference estimate
- StdError
standard error of total difference estimate
- MarginofError
margin of error of total difference estimate
- LCBxxPct
xx% (default 95%) lower confidence bound of total difference estimate
- UCBxxPct
xx% (default 95%) upper confidence bound of total difference estimate
- nResp_1
sample size in the first survey
- Estimate_1
total estimate from the first survey
- StdError_1
standard error of total estimate from the first survey
- MarginofError_1
margin of error of total estimate from the first survey
- LCBxxPct_1
xx% (default 95%) lower confidence bound of total estimate from the first survey
- UCBxxPct_1
xx% (default 95%) upper confidence bound of total estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate_2
total estimate from the second survey
- StdError_2
standard error of total estimate from the second survey
- MarginofError_2
margin of error of total estimate from the second survey
- LCBxxPct_2
xx% (default 95%) lower confidence bound of total estimate from the second survey
- UCBxxPct_2
xx% (default 95%) upper confidence bound of total estimate from the second survey
The contsum_median data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Category
category of response variable
- DiffEst.P
proportion above or below median difference estimate (in %; second survey - first survey)
- StdError.P
standard error of proportion above or below median difference estimate
- MarginofError.P
margin of error of proportion above or below median difference estimate
- LCBxxPct.P
xx% (default 95%) lower confidence bound of proportion above or below median difference estimate
- UCBxxPct.P
xx% (default 95%) upper confidence bound of proportion above or below median difference estimate
- Estimate.U
total above or below median difference estimate (second survey - first survey)
- StdError.U
standard error of total above or below median difference estimate
- MarginofError.U
margin of error of total above or below median difference estimate
- LCBxxPct.U
xx% (default 95%) lower confidence bound of total above or below median difference estimate
- UCBxxPct.U
xx% (default 95%) upper confidence bound of total above or below median difference estimate
- nResp_1
sample size in the first survey
- Estimate.P_1
proportion above or below median estimate (in %) from the first survey
- StdError.P_1
standard error of proportion above or below median estimate from the first survey
- MarginofError.P_1
margin of error of proportion above or below median estimate from the first survey
- LCBxxPct.P_1
xx% (default 95%) lower confidence bound of proportion above or below median estimate from the first survey
- UCBxxPct.P_1
xx% (default 95%) upper confidence bound of proportion above or below median estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate.U_1
total above or below median estimate from the first survey
- StdError.U_1
standard error of total above or below median estimate from the first survey
- MarginofError.U_1
margin of error of total above or below median estimate from the first survey
- LCBxxPct.U_1
xx% (default 95%) lower confidence bound of total above or below median estimate from the first survey
- UCBxxPct.U_1
xx% (default 95%) upper confidence bound of total above or below median estimate from the first survey
- Estimate.P_2
proportion above or below median estimate (in %) from the second survey
- StdError.P_2
standard error of proportion above or below median estimate from the second survey
- MarginofError.P_2
margin of error of proportion above or below median estimate from the second survey
- LCBxxPct.P_2
xx% (default 95%) lower confidence bound of proportion above or below median estimate from the second survey
- UCBxxPct.P_2
xx% (default 95%) upper confidence bound of proportion above or below median estimate from the second survey
- Estimate.U_2
total above or below median estimate from the second survey
- StdError.U_2
standard error of total above or below median estimate from the second survey
- MarginofError.U_2
margin of error of total above or below median estimate from the second survey
- LCBxxPct.U_2
xx% (default 95%) lower confidence bound of total above or below median estimate from the second survey
- UCBxxPct.U_2
xx% (default 95%) upper confidence bound of total above or below median estimate from the second survey
Author(s)
Tom Kincaid Kincaid.Tom@epa.gov
See Also
trend_analysis for trend analysis
Examples
# Categorical variable example for three resource classes
dframe <- data.frame(
surveyID = rep(c("Survey 1", "Survey 2"), c(100, 100)),
siteID = paste0("Site", 1:200),
wgt = runif(200, 10, 100),
xcoord = runif(200),
ycoord = runif(200),
stratum = rep(rep(c("Stratum 1", "Stratum 2"), c(2, 2)), 50),
CatVar = rep(c("North", "South"), 100),
All_Sites = rep("All Sites", 200),
Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE)
)
myvars <- c("CatVar")
mysubpops <- c("All_Sites", "Resource_Class")
change_analysis(dframe,
vars_cat = myvars, subpops = mysubpops,
surveyID = "surveyID", siteID = "siteID", weight = "wgt",
xcoord = "xcoord", ycoord = "ycoord", stratumID = "stratum"
)
spsurvey documentation built on May 31, 2023, 6:25 p.m.
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View source: R/change_analysis.R
| change_analysis | R Documentation |
Change analysis
Description
This function organizes input and output for the estimation of change between two
samples (for categorical and continuous variables). The analysis data,
dframe, can be either a data frame or a simple features (sf) object. If an
sf object is used, coordinates are extracted from the geometry column in the
object, arguments xcoord and ycoord are assigned values
"xcoord" and "ycoord", respectively, and the geometry column is
dropped from the object.
Usage
change_analysis(
dframe,
vars_cat = NULL,
vars_cont = NULL,
test = "mean",
subpops = NULL,
surveyID = "surveyID",
survey_names = NULL,
siteID = "siteID",
weight = "weight",
revisitwgt = FALSE,
xcoord = NULL,
ycoord = NULL,
stratumID = NULL,
clusterID = NULL,
weight1 = NULL,
xcoord1 = NULL,
ycoord1 = NULL,
sizeweight = FALSE,
sweight = NULL,
sweight1 = NULL,
fpc = NULL,
popsize = NULL,
vartype = "Local",
jointprob = "overton",
conf = 95,
All_Sites = FALSE
)
Arguments
dframe |
Data to be analyzed (analysis data). A data frame or
|
vars_cat |
Vector composed of character values that identify the
names of categorical response variables in |
vars_cont |
Vector composed of character values that identify the
names of continuous response variables in |
test |
Character string or character vector providing the location
measure(s) to use for change estimation for continuous variables. The
choices are |
subpops |
Vector composed of character values that identify the
names of subpopulation (domain) variables in |
surveyID |
Character value providing name of the survey ID variable in
|
survey_names |
Character vector of length two that provides the survey
names contained in the |
siteID |
Character value providing name of the site ID variable in
|
weight |
Character value providing name of the design weight
variable in |
revisitwgt |
Logical value that indicates whether each repeat visit
site has the same design weight in the two surveys, where
|
xcoord |
Character value providing name of the x-coordinate variable in
|
ycoord |
Character value providing name of the y-coordinate variable in
|
stratumID |
Character value providing name of the stratum ID variable in
|
clusterID |
Character value providing the name of the cluster
(stage one) ID variable in |
weight1 |
Character value providing name of the stage one weight
variable in |
xcoord1 |
Character value providing the name of the stage one
x-coordinate variable in |
ycoord1 |
Character value providing the name of the stage one
y-coordinate variable in |
sizeweight |
Logical value that indicates whether size weights should be
used during estimation, where |
sweight |
Character value providing the name of the size weight variable
in |
sweight1 |
Character value providing name of the stage one size weight
variable in |
fpc |
Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator. Example fpc for a single-stage unstratified survey design:
Example fpc for a single-stage stratified survey design:
Example fpc for a two-stage unstratified survey design:
Example fpc for a two-stage stratified survey design:
|
popsize |
Object that provides values for the population argument of the
Example popsize for calibration:
Example popsize for post-stratification using a data frame:
Example popsize for post-stratification using a table:
Example popsize for post-stratification using an xtabs object:
|
vartype |
Character value providing the choice of the variance
estimator, where |
jointprob |
Character value providing the choice of joint inclusion
probability approximation for use with Horvitz-Thompson and Yates-Grundy
variance estimators, where |
conf |
Numeric value providing the Gaussian-based confidence level. The default value
is |
All_Sites |
A logical variable used when |
Value
List of change estimates composed of four items:
(1) catsum contains change estimates for categorical variables,
(2) contsum_mean contains estimates for continuous variables using
the mean, (3) contsum_total contains estimates for continuous
variables using the total, and (4) contsum_median contains estimates for continuous
variables using the median. The items in the list will contain NULL
for estimates that were not calculated. Each data frame includes estimates
for all combinations of population Types, subpopulations within types,
response variables, and categories within each response variable (for
categorical variables and continuous variables using the median). Change
estimates are provided plus standard error estimates and confidence
interval estimates.
The catsum data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Category
category of response variable
- DiffEst.P
proportion difference estimate (in %; second survey - first survey)
- StdError.P
standard error of proportion difference estimate
- MarginofError.P
margin of error of proportion difference estimate
- LCBxxPct.P
xx% (default 95%) lower confidence bound of proportion difference estimate
- UCBxxPct.P
xx% (default 95%) upper confidence bound of proportion difference estimate
- Estimate.U
total difference estimate (second survey - first survey)
- StdError.U
standard error of total difference estimate
- MarginofError.U
margin of error of total difference estimate
- LCBxxPct.U
xx% (default 95%) lower confidence bound of total difference estimate
- UCBxxPct.U
xx% (default 95%) upper confidence bound of total difference estimate
- nResp_1
sample size in the first survey
- Estimate.P_1
proportion estimate (in %) from the first survey
- StdError.P_1
standard error of proportion estimate from the first survey
- MarginofError.P_1
margin of error of proportion estimate from the first survey
- LCBxxPct.P_1
xx% (default 95%) lower confidence bound of proportion estimate from the first survey
- UCBxxPct.P_1
xx% (default 95%) upper confidence bound of proportion estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate.U_1
total estimate from the first survey
- StdError.U_1
standard error of total estimate from the first survey
- MarginofError.U_1
margin of error of total estimate from the first survey
- LCBxxPct.U_1
xx% (default 95%) lower confidence bound of total estimate from the first survey
- UCBxxPct.U_1
xx% (default 95%) upper confidence bound of total estimate from the first survey
- Estimate.P_2
proportion estimate (in %) from the second survey
- StdError.P_2
standard error of proportion estimate from the second survey
- MarginofError.P_2
margin of error of proportion estimate from the second survey
- LCBxxPct.P_2
xx% (default 95%) lower confidence bound of proportion estimate from the second survey
- UCBxxPct.P_2
xx% (default 95%) upper confidence bound of proportion estimate from the second survey
- Estimate.U_2
total estimate from the second survey
- StdError.U_2
standard error of total estimate from the second survey
- MarginofError.U_2
margin of error of total estimate from the second survey
- LCBxxPct.U_2
xx% (default 95%) lower confidence bound of total estimate from the second survey
- UCBxxPct.U_2
xx% (default 95%) upper confidence bound of total estimate from the second survey
The contsum_mean data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Statistic
value of percentile
- nResp
sample size at or below
Value- DiffEst
mean difference estimate
- StdError
standard error of mean difference estimate
- MarginofError
margin of error of mean difference estimate
- LCBxxPct
xx% (default 95%) lower confidence bound of mean difference estimate
- UCBxxPct
xx% (default 95%) upper confidence bound of mean difference estimate
- nResp_1
sample size in the first survey
- Estimate_1
mean estimate from the first survey
- StdError_1
standard error of mean estimate from the first survey
- MarginofError_1
margin of error of mean estimate from the first survey
- LCBxxPct_1
xx% (default 95%) lower confidence bound of mean estimate from the first survey
- UCBxxPct_1
xx% (default 95%) upper confidence bound of mean estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate_2
mean estimate from the second survey
- StdError_2
standard error of mean estimate from the second survey
- MarginofError_2
margin of error of mean estimate from the second survey
- LCBxxPct_2
xx% (default 95%) lower confidence bound of mean estimate from the second survey
- UCBxxPct_2
xx% (default 95%) upper confidence bound of mean estimate from the second survey
The contsum_total data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Statistic
value of percentile
- nResp
sample size at or below
Value- DiffEst
total difference estimate
- StdError
standard error of total difference estimate
- MarginofError
margin of error of total difference estimate
- LCBxxPct
xx% (default 95%) lower confidence bound of total difference estimate
- UCBxxPct
xx% (default 95%) upper confidence bound of total difference estimate
- nResp_1
sample size in the first survey
- Estimate_1
total estimate from the first survey
- StdError_1
standard error of total estimate from the first survey
- MarginofError_1
margin of error of total estimate from the first survey
- LCBxxPct_1
xx% (default 95%) lower confidence bound of total estimate from the first survey
- UCBxxPct_1
xx% (default 95%) upper confidence bound of total estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate_2
total estimate from the second survey
- StdError_2
standard error of total estimate from the second survey
- MarginofError_2
margin of error of total estimate from the second survey
- LCBxxPct_2
xx% (default 95%) lower confidence bound of total estimate from the second survey
- UCBxxPct_2
xx% (default 95%) upper confidence bound of total estimate from the second survey
The contsum_median data frame contains the following variables:
- Survey_1
first survey name
- Survey_2
second survey name
- Type
subpopulation (domain) name
- Subpopulation
subpopulation name within a domain
- Indicator
response variable
- Category
category of response variable
- DiffEst.P
proportion above or below median difference estimate (in %; second survey - first survey)
- StdError.P
standard error of proportion above or below median difference estimate
- MarginofError.P
margin of error of proportion above or below median difference estimate
- LCBxxPct.P
xx% (default 95%) lower confidence bound of proportion above or below median difference estimate
- UCBxxPct.P
xx% (default 95%) upper confidence bound of proportion above or below median difference estimate
- Estimate.U
total above or below median difference estimate (second survey - first survey)
- StdError.U
standard error of total above or below median difference estimate
- MarginofError.U
margin of error of total above or below median difference estimate
- LCBxxPct.U
xx% (default 95%) lower confidence bound of total above or below median difference estimate
- UCBxxPct.U
xx% (default 95%) upper confidence bound of total above or below median difference estimate
- nResp_1
sample size in the first survey
- Estimate.P_1
proportion above or below median estimate (in %) from the first survey
- StdError.P_1
standard error of proportion above or below median estimate from the first survey
- MarginofError.P_1
margin of error of proportion above or below median estimate from the first survey
- LCBxxPct.P_1
xx% (default 95%) lower confidence bound of proportion above or below median estimate from the first survey
- UCBxxPct.P_1
xx% (default 95%) upper confidence bound of proportion above or below median estimate from the first survey
- nResp_2
sample size in the second survey
- Estimate.U_1
total above or below median estimate from the first survey
- StdError.U_1
standard error of total above or below median estimate from the first survey
- MarginofError.U_1
margin of error of total above or below median estimate from the first survey
- LCBxxPct.U_1
xx% (default 95%) lower confidence bound of total above or below median estimate from the first survey
- UCBxxPct.U_1
xx% (default 95%) upper confidence bound of total above or below median estimate from the first survey
- Estimate.P_2
proportion above or below median estimate (in %) from the second survey
- StdError.P_2
standard error of proportion above or below median estimate from the second survey
- MarginofError.P_2
margin of error of proportion above or below median estimate from the second survey
- LCBxxPct.P_2
xx% (default 95%) lower confidence bound of proportion above or below median estimate from the second survey
- UCBxxPct.P_2
xx% (default 95%) upper confidence bound of proportion above or below median estimate from the second survey
- Estimate.U_2
total above or below median estimate from the second survey
- StdError.U_2
standard error of total above or below median estimate from the second survey
- MarginofError.U_2
margin of error of total above or below median estimate from the second survey
- LCBxxPct.U_2
xx% (default 95%) lower confidence bound of total above or below median estimate from the second survey
- UCBxxPct.U_2
xx% (default 95%) upper confidence bound of total above or below median estimate from the second survey
Author(s)
Tom Kincaid Kincaid.Tom@epa.gov
See Also
trend_analysisfor trend analysis
Examples
# Categorical variable example for three resource classes
dframe <- data.frame(
surveyID = rep(c("Survey 1", "Survey 2"), c(100, 100)),
siteID = paste0("Site", 1:200),
wgt = runif(200, 10, 100),
xcoord = runif(200),
ycoord = runif(200),
stratum = rep(rep(c("Stratum 1", "Stratum 2"), c(2, 2)), 50),
CatVar = rep(c("North", "South"), 100),
All_Sites = rep("All Sites", 200),
Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE)
)
myvars <- c("CatVar")
mysubpops <- c("All_Sites", "Resource_Class")
change_analysis(dframe,
vars_cat = myvars, subpops = mysubpops,
surveyID = "surveyID", siteID = "siteID", weight = "wgt",
xcoord = "xcoord", ycoord = "ycoord", stratumID = "stratum"
)
R Package Documentation
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