tPoly.newton: Fits hierarchical global polynomial regression model to...
In hisemi: Hierarchical Semiparametric Regression of Test Statistics

Description Usage Arguments Value Author(s) References See Also Examples

Fits hierarchical global polynomial regression model to t-statistics through Newtonian algorithms.

tPoly.newton(tstat, x, df, starts, 
        pen.order=1,
        optim.method = c("nlminb", "BFGS", "CG", 
        "L-BFGS-B", "Nelder-Mead", "SANN", "NR"), 
        newton.iter.max = 1500, 
        scale.conv = 0.001, lfdr.conv = 0.001, NPLL.conv = 0.001, 
        debugging = FALSE, plotit = TRUE, ...)

`tstat`	A numeric vector t-statistics
`x`	A numeric matrix of covariates, with `nrow(x)` being `length(tstat)`
`df`	A numeric scalar or vector of degrees of freedom

starts

An optional numeric vector of starting values. The first element is the r, i.e. log(scale-1). The second parameter is the intercept. The remaining elements are the starting values for the B-spline coefficients (removing the first basis) for each x. When thist argument is not provided, the code starts with a global constant model that is easiest to fit, and then increase the order gradually using the warm starts from lower order fits.

pen.order

A numeric scalar of the order of derivatives of which squared integration will be used as roughness penalty. Note: The final order of the global polynomial is always pen.order-1.

optim.method

A character scalar specifying the method of optimization.

`newton.iter.max`	A numeric scalar specifying the maximum number of iterations in Newton method.
`scale.conv`	A small numeric scalar specifying the convergence criterion for the scale parameter.
`lfdr.conv`	A small numeric scalar specifying the convergence criterion for the local false discovery rates.
`NPLL.conv`	A small numeric scalar specifying the convergence criretion for the negative penalized log likelhood.
`debugging`	A logical scalar. If `TRUE`, then `dump.frame` will be called whenever error occurs.
`plotit`	A locgical scalr specifying whether a plot should be generated.
`...`	Currently not used.

An list of class hisemit:

`lfdr:`	A numeric vector of local false discovery rates.
`model`	A list of `tstat`, `df` and `x`, which are the same as arguments
`scale.fact:`	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^(-2df/(df+1))))` where `s` is the `scale.fact`
`pi0:`	A numeric vector of mixing proportions for the central t component
`tuning:`	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
`spar:`	A list with all: All smoothing parameters searched final: The smoothing parameter used final.idx: The index of the final `spar`
`enp:`	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
`fit:`	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
`NPLL:`	A list with NPLL: Negative penalized log likelihood logLik: Log likelihood penalty: Penalty term saturated.ll: Saturated log likelihood

Long Qu rtistician@gmail.com

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

penLik.EMNewton, plot.hisemit, fitted.hisemit, coef.hisemit, vcov.hisemit, residuals.hisemit, logLik.hisemit, confint.hisemit, plot.hisemit , hisemi-package, pi0-package