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.fact
A 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
# NOT RUN {
<!-- %\dontrun{ -->
# }
# NOT RUN {
# See the examples for the hisemi-package.
# }
# NOT RUN {
<!-- %} -->
# }
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