Simulation Function for Group Testing Data with Matrix Pooling Design
Simulates data in group testing form ready to be fit by gtreg.mp.
sim.mp(x = NULL, gshape = 20, gscale = 2, par,
linkf = c("logit", "probit", "cloglog"),
n.row, n.col, sens = 1, spec = 1,
sens.ind = NULL, spec.ind = NULL)x |
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. |
gshape |
shape parameter of gamma distribution, must be non-negative, set to be 20 by default |
gscale |
scale parameter of gamma distribution, must be strictly positive, set to be 2 by default |
par |
the true coefficients in the linear predictor |
linkf |
a character string specifying one of the three link functions to be used: "logit" (default) or "probit" or "cloglog" |
n.row |
a vector that specifies the number of rows in each matrix, a scalar if only one matrix is simulated |
n.col |
a vector that specifies the number of columns in each matrix, a scalar if only one matrix is simulated |
sens |
sensitivity of the group tests, set to be 1 by default. |
spec |
specificity of the group tests, set to be 1 by default. |
sens.ind |
sensitivity of the individual retests, set to be equal to sens if not specified otherwise. |
spec.ind |
specificity of the individual retests, set to be equal to spec if not specified otherwise. |
sim.mp generates group testing data in matrix pooling form. 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 individuals are organized into (by column) one or more matrices specified by n.row and n.col, and the true group responses are found (i.e., if at least one response is positive, the group is positive; otherwise, the group response is negative). The observed row and column group responses are then simulated using the given sens and spec values. Individual retests are simulated from sens.ind and spec.ind for individuals that lie on the intersection of an observed positive row and an observed positive column. In the case where no column (row) tests positive in a matrix, all the individuals in any observed positive rows (columns) will be assigned a simulated retest result. If no column or row is observed positive, NULL is returned.
sim.mp returns a list with the components dframe: the data frame that is actually to be fit, ind: the true individual responses presented in matrices and prob: the individual probabilities.
dframe is a data frame with columns
col.resp |
the column group response |
row.resp |
the row group response |
x |
the covariate |
arrayn |
the array number |
coln |
the column group number |
rown |
the row group number |
retest |
the results of individual retests |
Boan Zhang
gtreg.mp for the corresponding function to fit the model.
# 5*6 and 4*5 matrix set.seed(9128) sa1a<-sim.mp(par=c(-7,0.1), n.row=c(5,4), n.col=c(6,5), sens=0.95, spec=0.95) sa1<-sa1a$dframe
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