Simulate data for the purpose of testing the package.
sim_data(
n,
fit = list(Pri = list(c(0, 0, 0, 0.5, 0.25, 0.25, 0, 0)), Tran = list(matrix(c(0.74,
0.16, 0.04, 0.01, 0, 0, 0, 0, 0.19, 0.63, 0.15, 0.04, 0.01, 0, 0, 0, 0.05, 0.16,
0.61, 0.15, 0.04, 0.01, 0, 0, 0.02, 0.04, 0.15, 0.6, 0.15, 0.04, 0.01, 0, 0, 0.01,
0.04, 0.15, 0.6, 0.15, 0.04, 0, 0, 0, 0.01, 0.04, 0.15, 0.61, 0.16, 0, 0, 0, 0, 0.01,
0.04, 0.15, 0.63, 0, 0, 0, 0, 0, 0.01, 0.04, 0.16, 1), 8, 8))),
strata = c(s1 = 0.2, s2 = 0.8),
days = paste0("D", 1:28),
bin = rep(1, length(days) - 1),
drop_out_rate = 0.01,
sporatic_rate = 0.05,
miss_not_dead_rate = 0.2
)Number of samples.
Contains transition and prior parameters.
Named vector with probabilities to simulate strata.
Names of the output columns that contain the score for each day.
The assigned bin for pooling together information across transitions. Must be a numeric vector of length=(length(days)-1). By defualt all transitions are pooled together.
Parameter for simulating dropout. Rate is per day.
Parameter for simulating missing.
Parameter to control missing but not dead rate.
Returns wide format data. Possible niad os values may be:
Not missing:An integer from 1 to 8.
Missing:NA
Partially Missing: range which may be code as a characters string such as '[1,7]' or '[1,2]'. Such a character string indicates that while the actual value is unknown, it is known that the value falls within the specified range.
This simulation function is used to generated data for examples and testing of the package.
# NOT RUN {
test <- sim_data(200)
# }
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