internals {qrcm}R Documentation

Internal Functions

Description

Functions for internal use only, or not yet documented.

Usage


check.in.iqr(mf, formula.p, s)
check.in0.iqrL(mf1, mf2)
check.in.iqrL(mf, y,w,formula.p, s)
check.out(theta, S, covar)
start.iqr(y,z,d, x, weights, bfun, df, yy, zz, s, type = "ctiqr")
start.iqrL(y,x,z,id, w1,w2, bfun1,bfun2, s.theta,s.phi, S1,S2)

ctiqr.internal(mf, cl, formula.p, tol = 1e-06, maxit, s, remove.qc)
iqrL.internal(mf1, mf2, cl, fu,fv, s.theta, s.phi, tol = 1e-5, maxit)
iqrL.fit(theta,phi, y,alpha, x,xw,z,zw, id, w1,w2, bfun1,bfun2, s.theta, s.phi, 
	maxit.theta, safeit.theta, maxit.phi, safeit.phi, eps, tol, maxit)

iqr.ee(theta, y, z, d, X, Xw, bfun, p.star.y, p.star.z, 
  J = TRUE, G, i = FALSE, lambda = 0)
ciqr.ee(theta, y, z, d, X, Xw, bfun, p.star.y, p.star.z, 
  J = TRUE, G, i = FALSE, lambda = 0)
ctiqr.ee(theta, y, z, d, X, Xw, bfun, p.star.y, p.star.z, 
  J = TRUE, G, i = FALSE, lambda = 0)
iciqr.ee(theta, y, z, d, X, Xw, bfun, p.star.y, p.star.z, 
  J = TRUE, G, i = FALSE, lambda = 0)
  
iobjfun(theta, y, X, weights, bfun, p.star)
iobjfun.ct(theta, z,y,d,X,weights, bfun, py, pz, type)
iobjfun.ic(fit, V, bfun)

cov.fun.iqr(theta, y, z, d, X, Xw, weights, bfun, p.star.y, p.star.z, type, s)

iqrL.ee(par, x,xw, bfun, p, g = TRUE, H = TRUE, i = FALSE)
cov.fun.iqrL(fit, x,xw,z,zw, id, w1,w2, bfun1,bfun2, s.theta, s.phi)

iqr.newton(theta, y,z,d,X,Xw, bfun, s, type, tol, maxit, safeit, eps0, lambda = 0)
divide.et.impera(fit, V, bfun, s, type, tol, maxit, safeit, eps0, lambda = 0)
iqrL.newton(par, y,x,xw, bfun, s, tol, maxit, safeit, eps)

pmax0(x)
maxind(A)
num.fun(dx,fx, op = c("int", "der"))
make.bfun(p, x)
apply_bfun(bfun, p, fun = c("bfun", "b1fun"))
p.bisec(theta, y, X, bfun, n.it = 20)
p.bisec.internal(theta, y,X,bp)
slp.basis(k, intercept)
is.slp(f)
safesolve(A,B,lambda)
middlepoint(y)

iqr.waldtest(obj)
extract.p(model, p, cov = FALSE)
pred.beta(model, p, se = FALSE)


km(z,y,d,w, type, exclude = NULL)
alpha(obj, mz, mc, k = 98, zcmodel, Tc, Tz)
test.unif.ct(z,y,d,w, type, exclude = 0.05)
findagoodestimator(dat, w, type = "ctiqr")
quickpred(obj, y, type = c("PDF", "SF"))
trans(z,y,d,w,type)
fitgamma(y,X,w)

alpha.bisec(theta,phi,y,x,z,id,w1,w2,bfun1,bfun2, long = FALSE)
alpha.bisec.out(A, theta,phi,y,x,z,id,w1,w2,bfun1,bfun2, long = FALSE)
ks(u,v,id,w1,w2, K = 25)

## S3 method for class 'iqr'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'summary.iqr'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'iqr'
terms(x, ...)
## S3 method for class 'iqr'
model.matrix(object, ...)
## S3 method for class 'iqr'
vcov(object, ...)
## S3 method for class 'iqr'
nobs(object, ...)


predict_iqrL.internal(object, level, type = c("coef", "CDF", "QF", "sim"), 
  newdata, p, se = FALSE, ...)
## S3 method for class 'iqrL'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'summary.iqrL'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'iqrL'
terms(x, ...)
## S3 method for class 'iqrL'
model.matrix(object, ...)
## S3 method for class 'iqrL'
vcov(object, ...)
## S3 method for class 'iqrL'
nobs(object, ...)

qc.penalty(theta, X, bfun, lambda, pen, H)
fixqc(fit, V, bfun, s, type, tol, maxit, safeit, eps0, 
  lambda, r, maxTry, trace, count, pcross = NULL)
## S3 method for class 'qc.iqr'
print(x, ...)

[Package qrcm version 3.2 Index]