Hierarchical Semiparametric Regression of Test Statistics


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Documentation for package ‘hisemi’ version 1.0-319

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hisemi-package The package includes functions for fitting hierarchical semiparametric regression model to a large number of parametric test statistics.
coef.hisemit Extracts fitted parameters from a hisemit object
confint.hisemit Extract Wald-type asymptotic confidence intervals from a hisemit object
directSum Direct sum of matrices
EMupdate Utility function performing EM algorithm updates
fitted.hisemit Extract fitted values from a hisemit object
hisemi The package includes functions for fitting hierarchical semiparametric regression model to a large number of parametric test statistics.
logistic.enp Fit a logistic curve to the raw effective number of parameters over log smoothing parameter
logit Logit link and its inverse
logit.inv Logit link and its inverse
logLik.hisemit Extract the log likelihood from a hisemit object
n.knots Number of spline knots
NRupdate Utility function performing Newton-Raphson algorithm updates
OsplinePen O-spline penalty matrix
penLik.EMNewton Fits hierarchical semiparametric regression model to t-statistics
plot.hisemit Plot a hisemit object
plotHisemitResid Plot a hisemit object
plotHisemitTuning Plot a hisemit object
print.hisemit Print a summary of a hisemit object
residuals.hisemit Extract residuals from a hisemit object
scaledTMix.null Fit the null model to t-statistics
scaledTMix.psat Fits a partially saturated model to t-statistics
scaledTMix.sat Fits saturated model to t-statistics
summary.hisemit Print a summary of a hisemit object
tPoly.newton Fits hierarchical global polynomial regression model to t-statistics
vcov.hisemit Extract the asymptotic variance-covariance matrix of a hisemit object