qrcm-internal: Internal Functions

In qrcm: Quantile Regression Coefficients Modeling

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, ...)

qrcm documentation built on May 29, 2024, 12:09 p.m.