Description Usage Arguments Details Value Note Author(s) References See Also Examples
This method synthesizes information from multiple studies and make inference that is not dependent on any distributional assumption for the study-level parameters. Specifically, the study-level parameters are assumed to be unknown, fixed parameters, it draws inferences about the quantiles of this set of parameters using study-specific summary statistics
1 2 |
Thetahat |
input, point estimate of the true parameter for all K studies. For the kth study, the kth |
se |
input, standard error estimate of th e true parameter estimator for all K studies. For the kth study, there is a standard error estimate, denoted as the kth |
alpha |
quantile vector by default. |
n |
total sample size of all K studies. |
m |
mth ordered parameter. |
band_pwr |
a constant in (0,1). |
resample |
tuning parameter: R realization of CD-random variables for a confidence distribution for the mth ordered parameter. |
B |
tuning parameter: process with B new "observed" data. |
len |
tuning parameter: grid search for possible pairs. |
The function produces point or quantile estimation for the parameter whether tie or near tie condition exists or not.
An object of class "gmeta.interval", which is a list of following elements:
percentils
A three dimensional array containing the min.unif, max.smooth, mean.smooth.
min.unif
contains quantiles using minimum pair.
max.smooth
contains quantiles using maximum pair.
mean.smooth
contains quantiles using mean of all satisfied pairs.
shrink
The shrinkage used to obtain presumed "true values".
smoohlist
A sequence.
distance
The ten by ten dimensional array containing results of loss function.
elig.ind
A fourteen by two dimensional array containing counts that how many pairs are smaller than threshold.
Revised on 2017/10/01.
Wei Qian <wq24@rutgers.edu>, Jerry Q. Cheng <jcheng1@rwjms.rutgers.edu>
Brian Claggett, Minge Xie & Lu Tian(2014) Meta-Analysis With Fixed, Unknown, Study-Specific Parameters. Journal of the American Statistical Association, 109 1660-1671.
1 2 3 4 5 6 7 8 9 10 | Thetahat = c(-0.102158346, 0.020746333, 0.009118397, 0.163044549, -0.098892204,
0.161200470, 0.237464018, 0.162000380, -0.088128443, 0.337433537,
0.032277816, 0.142151631, -0.225430197, -0.057114409, 0.100874063,
0.177078003, -0.752891602, 0.069141934, 0.114787217, 0.239802656, -0.029858223)
se = c(0.1742206, 0.1342359, 0.1660455, 0.1372563, 0.1756401,
0.1288152, 0.2001390, 0.1603925, 0.1276070, 0.1470815,
0.1666182, 0.1694948, 0.1602534, 0.1688223, 0.1512014,
0.1857019, 0.1236781, 0.1510481, 0.1733524, 0.1627342, 0.1905231)
np.gmeta(Thetahat = Thetahat, se = se, m=10,
n =rep(40,21), band_pwr = 0.5, resample=200, B=40, len=10)
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