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Simulates data in group testing form ready to be fit by gtreg.halving.
sim.halving(x = NULL, gshape = 20, gscale = 2, par,
linkf = c("logit", "probit", "cloglog"),
sample.size, group.size, sens = 1, spec = 1,
sens.ind = NULL, spec.ind = NULL)
a matrix of user-submitted covariates to simulate the data with, defaults to NULL in which case a gamma distribution is used to generate the covariates automatically
shape parameter of gamma distribution, must be non-negative, set to be 20 by default
scale parameter of gamma distribution, must be strictly positive, set to be 2 by default
the true coefficients in the linear predictor
sample size of simulated data
a character string specifying one of the three link functions to be used: "logit" (default) or "probit" or "cloglog"
group size in pooling individual samples
sensitivity of the group tests, set to be 1 by default.
specificity of the group tests, set to be 1 by default.
sensitivity of the individual retests, set to be equal to sens if not specified otherwise.
specificity of the individual retests, set to be equal to spec if not specified otherwise.
sim.halving returns a data frame with the following columns:
the group response
the covariate
the group number
the actual individual response
the results of individual retests
the subgroup number
sim.halving generates group testing data for the halving protocol. The covariates are either specified by the x argument or they are generated from a gamma distribution with a given gshape and gscale. The individual probabilities are calculated from the covariates, the coefficients given in par, and the link function specified through linkf. The true binary individual responses are then simulated from the individual probabilities. The group, subgroup, and individual retests are simulated using the given sens and spec under the halving protocol.
# NOT RUN {
set.seed(46)
gt.data <- sim.halving(par = c(-6, .1), gshape = 17, gscale = 1.4,
sample.size = 5000, group.size = 5,
sens = 0.95, spec = 0.95)
# }
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