gmeta (version 2.3-1)

np.gmeta: Make inference for the study-level parameters with no distributional assumption

Description

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

Usage

np.gmeta(Thetahat, se, alpha = c(0.025, 0.975), n, m, 
band_pwr = 0.5, resample = 200, B = 40, len = 10)

Arguments

Thetahat

input, point estimate of the true parameter for all K studies. For the kth study, the kth Thetahat is consistent estimator for the true parameter, the kth Theta.

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 se, which is associated with the kth Thetahat..

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.

Value

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.

Details

The function produces point or quantile estimation for the parameter whether tie or near tie condition exists or not.

References

Brian Claggett, Minge Xie & Lu Tian(2014) Meta-Analysis With Fixed, Unknown, Study-Specific Parameters. Journal of the American Statistical Association, 109 1660-1671.

See Also

gmeta

Examples

# NOT RUN {
 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)
# }