This function calculates the attributable risk estimate for a 2x2 table of cell counts defined by a categorical response variable and a categorical explanatory (stressor) variable for an unequal probability design (Van Sickle and Paulsen, 2008). The attributable risk of the stressor variable is the percent reduction in the first level of the response variable that would result from elimination of the stressor variable. Cell totals are estimated using the Horvitz-Thompson estimator. The standard error of the log of the attributable risk estimate and confidence limits for the estimate also are calculated. The standard error is calculated using a first-order Taylor series linearization (Sarndal et al, 1992).
attrisk.est(response, stressor, response.levels = c("Poor", "Good"),
stressor.levels = c("Poor", "Good"), wgt, xcoord = NULL,
ycoord = NULL, stratum = NULL, cluster = NULL, wgt1 = NULL,
xcoord1 = NULL, ycoord1 = NULL, popcorrect = FALSE,
pcfsize = NULL, N.cluster = NULL, stage1size = NULL,
support = NULL, sizeweight = FALSE, swgt = NULL, swgt1 = NULL,
vartype = "Local", conf = 95, check.ind = TRUE, warn.ind = NULL,
warn.df = NULL, warn.vec = NULL)Vector of the categorical response variable values.
Vector of the categorical explanatory (stressor) variable values.
Vector of category values (levels) for the categorical response variable, where the first level is used for calculating the numerator and the denominator of the attributable risk estimate. If response.levels is not supplied, then values "Poor" and "Good" are used for the first level and second level of the response variable, respectively. The default is c("Poor", "Good").
Vector of category values (levels) for the categorical stressor variable, where the first level is used for calculating the numerator of the attributable risk estimate and the second level is used for calculating the denominator of the estimate. If stressor.levels is not supplied, then values "Poor" and "Good" are used for the first level and second level of the stressor variable, respectively. The default is c("Poor", "Good").
Vector of the final adjusted weight (inverse of the sample inclusion probability) for each site, which is either the weight for a single-stage sample or the stage two weight for a two-stage sample.
Vector of x-coordinates for location for each 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.
Vector of y-coordinate for location for each 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.
Vector of the stratum values for each site. The default is NULL.
Vector of the stage one sampling unit (primary sampling unit or cluster) code for each site. The default is NULL.
Vector of the final adjusted stage one weight for each site. The default is NULL.
Vector of the stage one x-coordinate for location for each site. The default is NULL.
Vector of the stage one y-coordinate for location for each site. The default is NULL.
Logical value that indicates whether finite or continuous population correction factors should be employed during variance estimation, 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 and support. To employ the correction factor for a two-stage sample, values must be supplied for arguments N.cluster, stage1size, and support.
The size of the 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.
The number of stage one sampling units in the resource, which is required for calculation of finite and continuous population correction factors for a two-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.
The size of the stage one sampling units of a two-stage sample, 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.
The support value for each 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.
Logical value that indicates whether size-weights should be used in the analysis, where TRUE = use the size-weights and FALSE = do not use the size-weights. The default is FALSE.
The size-weight for each site, which is the stage two size-weight for two-stage sample. The default is NULL.
The stage one size-weight for each site. The default is NULL.
The choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator. The default is "Local".
Numeric value for the confidence level. The default is 95.
A 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.
A 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.
A data frame for storing warning messages. The default is NULL.
A vector that contains names of the population type, the subpopulation, and an indicator. The default is NULL.
If the function was called directly, then value is the Results list, which contains the following components:
AttRisk - the attributable risk estimate
ARlog.se - standard error for the log of the attributable
risk estimate
ConfLimits - confidence limits for the attributable risk
estimate
WeightTotal - sum of the final adjusted weights
CellCounts - cell and margin counts for the 2x2 table
CellProportions - estimated cell proportions for the 2x2
table
If the function was called by the attrisk.analysis function, then the result is a list containing the following components:
Results - Results list. See above for list contents
warn.ind - a logical value indicating whether warning
messages were generated
warn.df - a data frame containing warning messages
Van Sickle, J. and S. G. Paulsen. (2008). Assessing the attributable risks, relative risks, and regional extent of aquatic stressors. Journal of the North American Benthological Society 27, 920-931.
Sarndal, C.E., B. Swensson, and J. Wretman. (1992). Model Assisted Survey Sampling. Springer-Verlag, New York.
# NOT RUN {
response <- sample(c("Poor", "Good"), 100, replace=TRUE)
stressor <- sample(c("Poor", "Good"), 100, replace=TRUE)
wgt <- runif(100, 10, 100)
attrisk.est(response, stressor, wgt=wgt, vartype="SRS")
xcoord <- runif(100)
ycoord <- runif(100)
attrisk.est(response, stressor, wgt=wgt, xcoord=xcoord, ycoord=ycoord)
# }
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