Fits hierarchical semiparametric regression model to t-statistics
penLik.EMNewton(tstat, x, df, spar = c(10^seq(-1,8,length=30), Inf),
nknots = n.knots(length(tstat)), starts,
tuning.method = c("NIC", "CV"), cv.fold = 5, pen.order=1,
poly.degree=pen.order*2-1, optim.method =
c("nlminb", "BFGS", "CG", "L-BFGS-B", "Nelder-Mead", "SANN", "NR"),
logistic.correction = TRUE, em.iter.max = 10,
em.beta.iter.max = 1, newton.iter.max = 1500,
scale.conv = 0.001, lfdr.conv = 0.001, NPLL.conv = 0.001,
debugging = FALSE, plotit = TRUE, ...)A numeric vector t-statistics
A numeric matrix of covariates, with nrow(x) being length(tstat)
A numeric scalar or vector of degrees of freedom
A numeric vector of smoothing parameter lambda
A numeric scalar of number of knots
An optional numeric vector of starting values
Either 'NIC' or 'CV', specifying the method to choose the tuning parameter spar
A numeric scalar of the fold for cross-validation. Ignored if tuning.method='NIC'.
A numeric scalar of the order of derivatives of which squared integration will be used as roughness penalty.
A numeric scalar of the degree of B-splines.
A character scalar specifying the method of optimization.
A logical scalar specifying whether or not the effective number of parameters should be corrected using a logistic curve
A numeric scalar specifying the maximum number of EM iterations. If being Inf, then EM algorithm is used. If being 0, then Newton method is used. Otherwise, EM algorithm is used initially, followed by Newton method.
A numeric scalar specifying the maximum number of iterations in the maximization step for the beta parameters in the EM algorithm. If being Inf, the original EM is used. If being 1 or other numbers, the generalized EM algorithm is used.
A numeric scalar specifying the maximum number of iterations in Newton method.
A small numeric scalar specifying the convergence criterion for the scale parameter.
A small numeric scalar specifying the convergence criterion for the local false discovery rates.
A small numeric scalar specifying the convergence criretion for the negative penalized log likelhood.
A logical scalar. If TRUE, then dump.frame will be called whenever error occurs.
A locgical scalr specifying whether a plot should be generated.
Currently not used.
An list of class hisemit:
A numeric vector of local false discovery rates.
A list of tstat, df and x, which are the same as arguments
A list with
scale.fact: Scale factor
sd.ncp: Equivalent standard deviation of noncentrality parameters
r: A reparameterization of scale.fact
t.cross: sqrt(df*(s^(2/(df+1))-1)/(1-s^(-2*df/(df+1))))
s is the scale.factA numeric vector of mixing proportions for the central t component
A list with
mean: Mean criterion
var: Variance of criterion across observations
grp: Cross-validation group membership
method: The tuning.method used.
final: The minimum mean criterion
A list with
all: All smoothing parameters searched
final: The smoothing parameter used
final.idx: The index of the final spar
A list with
raw: Raw effective number of parameters
logistic: Effective number of parameters after fitting logistic curve as a correction
final: The effective nubmer of parameters in the final model
good.idx: The index of the selected effective number of parameters
A list with
intercept: The fitted intercept
covariate.idx: The index of covariates
f.covariate: Each additive smooth function evaluated at the covariates
f: Fitted smoothing funciton
beta: Estimated regression coefficients
H: Expanded design matrix
asym.vcov: Asymptotic variance-covariance matrix for estimated parameters
A list with
NPLL: Negative penalized log likelihood
logLik: Log likelihood
penalty: Penalty term
saturated.ll: Saturated log likelihood
Long Qu, Dan Nettleton, Jack Dekkers (2012) A hierarchical semiparametric model for incorporating inter-gene relationship information for analysis of genomic data. Biometrics, 68(4):1168-1177
plot.hisemit, fitted.hisemit, coef.hisemit,
vcov.hisemit, residuals.hisemit, logLik.hisemit,
confint.hisemit, plot.hisemit,
hisemi-package, pi0-package
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