np.estep: Internal boxcoxmix functions

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

lambda

a transformation parameter, setting lambda=1 means 'no transformation'.

beta

sigma

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.

sizes

numnodes

minweight

object

Details

Internal boxcoxmix functions