relrisk_analysis: Relative risk analysis
In spsurvey: Spatial Sampling Design and Analysis

relrisk_analysis

R Documentation

Relative risk analysis

Description

This function organizes input and output for relative risk 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.

Usage

relrisk_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
)

Arguments

`dframe`	Data to be analyzed (analysis data). A data frame or `sf` object containing survey design variables, response variables, stressor variables, and subpopulation (domain) variables.
`vars_response`	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.
`vars_stressor`	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.
`response_levels`	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.
`stressor_levels`	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.
`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`.
`siteID`	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.
`weight`	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"`.
`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 t-coordinate). The default value is `NULL`.
`stratumID`	Character value providing the 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 the 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 the 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"`.
`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

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:

Type: subpopulation (domain) name
Subpopulation: subpopulation name within a domain
Response: response variable
Stressor: stressor variable
nResp: sample size
Estimate: relative risk estimate
Estimate_num: relative risk numerator estimate
Estimate_denom: relative risk denominator estimate
StdError: relative risk standard error
MarginofError: relative risk margin of error
LCBxxPct: xx% (default 95%) lower confidence bound
UCBxxPct: xx% (default 95%) upper confidence bound
WeightTotal: sum of design weights
Count_RespPoor_StressPoor: number of observations in the poor response and poor stressor group
Count_RespPoor_StressGood: number of observations in the poor response and good stressor group
Count_RespGood_StressPoor: number of observations in the good response and poor stressor group
Count_RespGood_StressGood: number of observations in the good response and good stressor group
Prop_RespPoor_StressPoor: weighted proportion of observations in the poor response and poor stressor group
Prop_RespPoor_StressGood: weighted proportion of observations in the poor response and good stressor group
Prop_RespGood_StressPoor: weighted proportion of observations in the good response and poor stressor group
Prop_RespGood_StressGood: weighted proportion of observations in the good response and good stressor group

Details

Relative risk measures the relative 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). Relative risk is defined as the ratio of two conditional probabilities. The numerator of the ratio is the probability that the response variable is in poor condition given that the stressor variable is in poor condition. The denominator of the ratio is the probability that the response variable is in poor condition given that the stressor variable is in good condition. A relative risk value equal to one indicates that the response variable is independent of the stressor variable. Relative risk values greater than one measure the extent to which poor condition of the stressor variable is associated with poor condition of the response variable.

Author(s)

Tom Kincaid Kincaid.Tom@epa.gov

Examples

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")
relrisk_analysis(dframe,
  vars_response = myresponse,
  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
  stratumID = "stratum"
)

spsurvey documentation built on May 31, 2023, 6:25 p.m.