This function organizes input and output for risk difference analysis (of
categorical 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.
diffrisk_analysis(
dframe,
vars_response,
vars_stressor,
response_levels = NULL,
stressor_levels = NULL,
subpops = NULL,
siteID = NULL,
weight = "weight",
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",
conf = 95,
All_Sites = FALSE
)The analysis results. A data frame of population estimates for all combinations of subpopulations, categories within each subpopulation, response variables, and categories within each response variable. Estimates are provided for proportion and size of the population plus standard error, margin of error, and confidence interval estimates. The data frame contains the following variables:
subpopulation (domain) name
subpopulation name within a domain
response variable
stressor variable
sample size
risk difference estimate
risk estimate for poor condition stressor
risk estimate for good condition stressor
risk difference standard error
risk difference margin of error
xx% (default 95%) lower confidence bound
xx% (default 95%) upper confidence bound
sum of design weights
number of observations in the poor response and poor stressor group
number of observations in the poor response and good stressor group
number of observations in the good response and poor stressor group
number of observations in the good response and good stressor group
weighted proportion of observations in the poor response and poor stressor group
weighted proportion of observations in the poor response and good stressor group
weighted proportion of observations in the good response and poor stressor group
weighted proportion of observations in the good response and good stressor group
Data to be analyzed (analysis data). A data frame or
sf object containing survey design
variables, response variables, stressor variables, and subpopulation
(domain) variables.
Vector composed of character values that identify the
names of response variables in dframe. Each response
variable must have two category values (levels), where one level is
associated with poor condition and the other level is associated with good
condition.
Vector composed of character values that identify the
names of stressor variables in dframe. Each stressor
variable must have two category values (levels), where one level is
associated with poor condition and the other level is associated with good
condition.
List providing the category values (levels) for each
element in the vars_response argument. Each element in the list
must contain two values, where the first value identifies poor condition,
and the second value identifies good condition. This argument must be
named and must be the same length as argument vars_response. Names
for this argument must match the values in the vars_response
argument. If this argument equals NULL, then a named list is created that
contains the values "Poor" and "Good" for the first and
second levels, respectively, of each element in the vars_response
argument and that uses values in the vars_response argument as names
for the list. If response_levels is provided without names,
then the names of response_levels are set to vars_response.
The default value is NULL.
List providing the category values (levels) for each
element in the vars_stressor argument. Each element in the list
must contain two values, where the first value identifies poor condition,
and the second value identifies good condition. This argument must be
named and must be the same length as argument vars_stressor. Names
for this argument must match the values in the vars_stressor
argument. If this argument equals NULL, then a named list is created that
contains the values "Poor" and "Good" for the first and
second levels, respectively, of each element in the vars_stressor
argument and that uses values in the vars_stressor argument as names
for the list. If stressor_levels is provided without names,
then the names of stressor_levels are set to vars_stressor.
The default value is NULL.
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.
Character value providing the 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 NULL, which
assumes that each row in dframe represents a unique site.
Character value providing the 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".
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.
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 t-coordinate). The default
value is NULL.
Character value providing the name of the stratum ID
variable in dframe. The default value is
NULL.
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.
Character value providing the name of the stage one weight
variable in dframe. The default value is
NULL.
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.
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.
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.
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.
Character value providing the name of the stage one size
weight variable in dframe. The default value is
NULL.
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))
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)
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".
Numeric value providing the Gaussian-based confidence level. The default value
is 95.
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.
Tom Kincaid Kincaid.Tom@epa.gov
Risk difference measures the absolute strength of association between conditional probabilities defined for a response variable and a stressor variable, where the response and stressor variables are classified as either good (i.e., reference condition) or poor (i.e., different from reference condition). Risk difference is defined as the difference between two conditional probabilities: the probability that the response variable is in poor condition given that the stressor variable is in poor condition and the probability that the response variable is in poor condition given that the stressor variable is in good condition. Risk difference values close to zero indicate that the stressor variable has little or no impact on the probability that the response variable is in poor condition. Risk difference values much greater than zero indicate that the stressor variable has a significant impact on the probability that the response variable is in poor condition.
attrisk_analysisfor attributable risk analysis
relrisk_analysisfor relative risk analysis
dframe <- data.frame(
siteID = paste0("Site", 1:100),
wgt = runif(100, 10, 100),
xcoord = runif(100),
ycoord = runif(100),
stratum = rep(c("Stratum1", "Stratum2"), 50),
RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
All_Sites = rep("All Sites", 100),
Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
)
myresponse <- c("RespVar1", "RespVar2")
mystressor <- c("StressVar")
mysubpops <- c("All_Sites", "Resource_Class")
diffrisk_analysis(dframe,
vars_response = myresponse,
vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
stratumID = "stratum"
)
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