vc.em: Internal boxcoxmix functions

In boxcoxmix: Box-Cox-Type Transformations for Linear and Logistic Models with Random Effects

Description

auxiliary functions are not intended to be directly called from the user.

Usage

np.estep(y, x, lambda, p, beta, z, sigma)

np.zk(y, x, w, beta, lambda)

fik(y, x, lambda, beta, z, sigma)

np.theta(y, x, lambda, beta, z)

yhat(v, lambda = 1)

ytrans(y, lambda = 1)

np.bhat(y, x, w, z, lambda)

np.mstep(y, x, beta, lambda, w)

np.em(
  y,
  x,
  K,
  lambda = 1,
  steps = 500,
  tol = 0.5,
  start = "gq",
  EMdev.change = 1e-04,
  plot.opt = 1,
  verbose = TRUE,
  ...
)

np.boxcox(
  formula,
  groups = 1,
  data,
  K = 3,
  tol = 0.5,
  lambda = 1,
  steps = 500,
  EMdev.change = 1e-04,
  plot.opt = 1,
  verbose = TRUE,
  start = "gq",
  ...
)

vc.estep(Y, X, sizes = 1, lambda, p, beta, z, sigma)

zk(Y, X, sizes, w, beta, lambda)

bhat(Y, X, sizes, w, z, lambda)

mik(Y, X, sizes, lambda, beta, z, sigma)

vc.theta(Y, X, sizes, lambda, beta, z)

vc.mstep(Y, X, sizes = 1, beta, lambda, w)

vc.em(
  y,
  x,
  sizes = 1,
  K,
  lambda,
  steps = 500,
  tol = 0.5,
  start = "gq",
  EMdev.change = 1e-04,
  plot.opt = 1,
  verbose = TRUE,
  ...
)

vc.boxcox(
  formula,
  groups = 1,
  data,
  K = 3,
  tol = 0.5,
  lambda = 1,
  steps = 500,
  EMdev.change = 1e-04,
  plot.opt = 1,
  verbose = TRUE,
  start = "gq",
  ...
)

gqz(numnodes = 20, minweight = 1e-06)

masspoint.class(object)

Arguments

y	..
x	..
lambda	a transformation parameter, setting lambda=1 means 'no transformation'.
p	..
beta	..
z	..
sigma	..
w	..
v	..
K	the number of mass points.
steps	maximum number of iterations for the EM algorithm.
tol	a positive scalar (usually, 0< tol <= 2)
start	a description of the initial values to be used in the fitted model, Quantile-based version "quantile" or Gaussian Quadrature "gq" can be set.
EMdev.change	a small scalar, with default 0.0001, used to determine when to stop EM algorithm.
plot.opt	Set plot.opt=1, to plot the disparity against iteration number. Use plot.opt=2 for tolfind.boxcox and plot.opt=3 for optim.boxcox.
verbose	If set to FALSE, no printed output on progress.
...	extra arguments will be ignored.
formula	a formula describing the transformed response and the fixed effect model (e.g. y ~ x).
groups	the random effects. To fit overdispersion models , set groups = 1.
data	a data frame containing variables used in the fixed and random effect models.
Y	..
X	..
sizes	..
numnodes	..
minweight	..
object	..

Details

Internal boxcoxmix functions

Author(s)

Amani Almohaimeed and Jochen Einbeck

boxcoxmix documentation built on Sept. 17, 2020, 9:31 a.m.