Description Usage Arguments Value Author(s) Examples
BinCalcICC: calculate ICC values for data from CRTs with binary outcomes.
1 2 3 4 5 6 7 8 9 10 11 12 |
data |
A dataframe of the sort generated
by |
method |
The method to be used to compute ICC. A single or multiple methods can be used at a time. By default, all 16 methods will be used. See Details for more information. |
ci.type |
The type of confidence interval to be computed. By default all 5 types will be reported. See Details for more information. |
alpha |
The significance level to be used while computing the confidence interval. Default value is 0.05. |
kappa |
Value of Kappa to be used in computing Stabilized ICC when the method stab is chosen. Default value is 0.45. |
nAGQ |
An integer, as in glmer function of package lme4, denoting the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Used when the method lin is chosen. Default value is 1. |
sim.min |
Optional, integer. The number of the first simulation for which ICC will be calculated. Default is 1. |
sim.max |
Optional, integer. The number of the last simulation for which ICC
will be calculated. Default is |
nsim |
Number of Monte Carlo replicates used in ICC computation method.
|
A list with the following components:
A dataframe containing the name of methods used and corresponding estimates of Intracluster Correlation coefficients
A dataframe containing names of confidence interval types and corresponding estimated confidence intervals
Alexandria C. Sakrejda (acbro0@umass.edu) and Ken Kleinman (ken.kleinman@gmail.com)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ## Not run:
bin.ma.rct.unbal <- cps.ma.binary(nsim = 3,
nsubjects = list(rep(200, times=15),
rep(150, times=15),
rep(170, times=15)),
narms = 3,
nclusters = 15,
probs = c(0.15, 0.23, 0.22),
sigma_b_sq = c(0.1, 0.1, 0.1),
alpha = 0.05, allSimData = TRUE,
seed = 123, cores="all")
binCalcICC(data = bin.ma.rct.unbal, nsim = 1000)
## End(Not run)
## Not run:
binary.sim = cps.binary(nsim = 100, nsubjects = 20,
nclusters = 10, p1 = 0.8,
p2 = 0.5, sigma_b_sq = 1,
sigma_b_sq2 = 1.2, alpha = 0.05,
method = 'glmm', allSimData = TRUE)
binCalcICC(data = binary.sim, nsim = 1000)
## End(Not run)
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