Description Usage Arguments Value Author(s) See Also Examples
This function generates grouped, misclassified current status data with a finite number of fixed censoring times. Probability of an event is determined from a user-provided "distribution" - only requirement is the vector true.F
must be monotonically increasing and between 0 and 1.
1 | gen.data.fixed.Rd(n, k, Cs, true.F, alpha=1, beta=1)
|
n |
number of individuals |
k |
grouping size |
Cs |
a vector of the observed censoring times |
true.F |
a vector of event probabilities at each one of the |
alpha |
Sensitivity: probability of a positive test results given that the individual is truly diseased (or that the group contains at least one person who is truly diseased). Default is 1 - no misclassification |
beta |
Specificity: probability of a negative test results given that the individual is truly not diseased (or that the group contains noone who is truly diseased). Default is 1 - no misclassification |
This function returns a data frame with the following columns:
Cs: | individual observation times |
groups: | group identifier |
initial.p: | initial values for the EM-PAV hybrid algorithm |
delta.ind: | indicator of event (1) or censoring (0) - true test result |
y.ind: | misclassified test result |
delta.group: | true group test result, indicator that at least one individual had delta.ind==1 |
y.group: | misclassified group test result |
Lucia Petito
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | #Generate data on 10 individuals with 5% misclassification rates each
data <- gen.data.fixed(10, 2, 1:5, seq(0.1, 0.5, 0.1), 0.95, 0.95)
data
#Now examine generated data in 1,000 individuals
data <- gen.data.fixed(1000, 2, 1:5, seq(0.1, 0.5, 0.1), 0.9, 0.9)
#Look at true individual test results
with(data, xtabs(~Cs + delta.ind))
#Look at misclassification
with(data, xtabs(~delta.ind + y.ind + Cs))
#Do the same in the grouped tests
with(data, xtabs(~Cs + delta.group))
with(data, xtabs(~delta.group + y.group + Cs))
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