Package 'VarReg' reference manual

Title:	Semi-Parametric Variance Regression
Description:	Methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.
Authors:	Kristy Robledo [aut, cre], Ian Marschner [ths]
Maintainer:	Kristy Robledo <[email protected]>
License:	GPL-3
Version:	2.0.1
Built:	2024-11-08 04:12:46 UTC
Source:	https://github.com/kristyrobledo/varreg

Censored Linear mean and variance regression

Description

censlinVarReg performs censored multivariate mean and multivariate variance regression. This function is designed to be used by the semiVarReg function.

Usage

censlinVarReg(
  dat,
  mean.ind = c(2),
  var.ind = c(2),
  cens.ind = c(3),
  mean.intercept = TRUE,
  para.space = c("all", "positive", "negative"),
  mean.init = NULL,
  var.init = NULL,
  control = list(...),
  ...
)
censlinVarReg(
  dat,
  mean.ind = c(2),
  var.ind = c(2),
  cens.ind = c(3),
  mean.intercept = TRUE,
  para.space = c("all", "positive", "negative"),
  mean.init = NULL,
  var.init = NULL,
  control = list(...),
  ...
)

Arguments

`dat`	Dataframe containing outcome and covariate data. Outcome data must be in the first column, with censored values set to the limits. Covariates for mean and variance model in next columns.
`mean.ind`	Vector containing the column numbers of the data in 'dat' to be fit as covariates in the mean model. 0 indicates constant mean option. NULL indicates zero mean option.
`var.ind`	Vector containing the column numbers of the data in 'dat' to be fit as covariates in the variance model. FALSE indicates constant variance option.
`cens.ind`	Vector containing the column number of the data in 'dat' to indicate the censored data. 0 indicates no censoring, -1 indicates left (lower) censoring and 1 indicates right (upper) censoring.
`mean.intercept`	Logical to indicate if an intercept is to be included in the mean model. Default is TRUE.
`para.space`	Parameter space to search for variance parameter estimates. "positive" means only search positive parameter space, "negative" means search only negative parameter space and "all" means search all. Default is all.
`mean.init`	Vector of initial estimates to be used for the mean model.
`var.init`	Vector of initial estimates to be used for the variance model.
`control`	List of control parameters. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

censlinVarReg returns a list of output including:

converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed of the EM algorithm.
reldiff: the positive convergence tolerance that occured at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
boundary: Logical argument indicating if estimates are on the boundary.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
mean.ind: Vector of integer(s) indicating the column number(s) in the dataframe data that were fit in the mean model.
mean: Vector of the maximum likelihood estimates of the mean parameters.
var.ind: Vector of integer(s) indicating the column(s) in the dataframe data that were fit in the variance model.
variance: Vector of the maximum likelihood estimates of the variance parameters.
cens.ind: Integer indicating the column in the dataframe data that corresponds to the censoring indicator.
data: Dataframe containing the variables included in the model.

The Censored data EM loop

Description

censloop_em is an EM loop function for censored data to be utilised by various other higher level functions.

Usage

censloop_em(
  meanmodel,
  theta.old,
  beta.old,
  p.old,
  x.0,
  X,
  censor.ind,
  mean.intercept,
  maxit,
  eps
)
censloop_em(
  meanmodel,
  theta.old,
  beta.old,
  p.old,
  x.0,
  X,
  censor.ind,
  mean.intercept,
  maxit,
  eps
)

Arguments

`meanmodel`	Dataframe containing only the covariates to be fit in the mean model. NULL for zero mean model and FALSE for constant mean model.
`theta.old`	Vector containing the initial variance parameter estimates to be fit in the variance model.
`beta.old`	Vector containing the initial mean parameter estimates to be fit in the mean model.
`p.old`	Vector of length n containing the initial variance estimate.
`x.0`	Matrix of covariates (length n) to be fit in the variance model. All have been rescaled so zero is the minimum. If NULL, then its a constant variance model.
`X`	Vector of length n of the outcome variable.
`censor.ind`	Vector of length n of the censoring indicator. 0=uncensored, -1=left censored and 1 is right censored.
`mean.intercept`	Logical to indicate if mean intercept is to be included in the model.
`maxit`	Number of maximum iterations for the EM algorithm.
`eps`	Very small number for the convergence criteria.

Value

A list of the results from the EM algorithm, including:

conv: Logical argument indicating if convergence occurred
it: Total iterations performed of the EM algorithm
reldiff: the positive convergence tolerance that occured at the final iteration.
theta.new: Vector of variance parameter estimates. Note that these are not yet transformed back to the appropriate scale
mean: Vector of mean parameter estimates
fittedmean: Vector of fitted mean estimates
p.old: Vector of fitted variance estimates

Calculation of information criterion

Description

criterion calculates various information criterion for the algorithms in this package

Usage

criterion(n, loglik, param)
criterion(n, loglik, param)

Arguments

`n`	Number of observations
`loglik`	Loglikelihood from model
`param`	Number of parameters fit in model

Value

A list of the four IC

aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion

lidar dataset.

Description

A dataset containing 221 observations from a light detection and ranging (LIDAR) experiment.

Usage

lidar
lidar

Format

A data frame with 221 rows and 2 variables:

range: distance travelled before the light is reflected back to its source.
logratio: logarithm of the ratio of received light from two laser sources.

...

Source

Sigrist, M. (Ed.) (1994). Air Monitoring by Spectroscopic Techniques (Chemical Analysis Series, vol. 197). New York: Wiley.

References

Ruppert, D., Wand, M.P. and Carroll, R.J. (2003) Semiparametric Regression Cambridge University Press. http://stat.tamu.edu/~carroll/semiregbook/

Examples

library(VarReg)
data(lidar)
attach(lidar)
plot(range,logratio)
library(VarReg)
data(lidar)
attach(lidar)
plot(range,logratio)

Linear mean and variance regression function

Description

linVarReg performs multivariate mean and multivariate variance regression. This function is designed to be used by the semiVarReg function.

Usage

linVarReg(
  dat,
  var.ind = c(2),
  mean.ind = c(2),
  para.space = c("all", "positive", "negative"),
  control = list(...),
  ...
)
linVarReg(
  dat,
  var.ind = c(2),
  mean.ind = c(2),
  para.space = c("all", "positive", "negative"),
  control = list(...),
  ...
)

Arguments

`dat`	Dataframe containing outcome and covariate data. Outcome data must be in the first column. Covariates for mean and variance model in next columns.
`var.ind`	Vector containing the column numbers of the data in 'dat' to be fit as covariates in the variance model. FALSE indicates constant variance option.
`mean.ind`	Vector containing the column numbers of the data in 'dat' to be fit as covariates in the mean model. 0 indicates constant mean option. NULL indicates zero mean option.
`para.space`	Parameter space to search for variance parameter estimates. "positive" means only search positive parameter space, "negative" means search only negative parameter space and "all" means search all.
`control`	List of control parameters. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

linVarReg returns a list of output including:

converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed of the EM algorithm.
reldiff: the positive convergence tolerance that occured at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
boundary: Logical argument indicating if estimates are on the boundary.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
mean.ind: Vector of integer(s) indicating the column number(s) in the dataframe data that were fit in the mean model.
mean: Vector of the maximum likelihood estimates of the mean parameters.
var.ind: Vector of integer(s) indicating the column(s) in the dataframe data that were fit in the variance model.
variance: Vector of the maximum likelihood estimates of the variance parameters.
cens.ind: Integer indicating the column in the dataframe data that corresponds to the censoring indicator. Always NULL.
data: Dataframe containing the variables included in the model.

The EM loop for the main mean and variance function

Description

loop_em is a basic EM loop function to be utilised by various other higher level functions.

Usage

loop_em(meanmodel, theta.old, p.old, x.0, X, maxit, eps)
loop_em(meanmodel, theta.old, p.old, x.0, X, maxit, eps)

Arguments

`meanmodel`	Dataframe containing only the covariates to be fit in the mean model. NULL for zero mean model and FALSE for constant mean model.
`theta.old`	Vector containing the initial variance parameter estimates to be fit in the variance model.
`p.old`	Vector of length n containing the containing the initial variance estimate.
`x.0`	Matrix of covariates (length n) to be fit in the variance model. All have been rescaled so zero is the minimum. If NULL, then its a constant variance model.
`X`	Vector of length n of the outcome variable.
`maxit`	Number of maximum iterations for the EM algorithm.
`eps`	Very small number for the convergence criteria.

Value

A list of the results from the EM algorithm, including

conv: Logical argument indicating if convergence occurred
it: Total iterations performed of the EM algorithm
reldiff: the positive convergence tolerance that occured at the final iteration.
theta.new: Vector of variance parameter estimates. Note that these are not yet transformed back to the appropriate scale
mean: Vector of mean parameter estimates
fittedmean: Vector of fitted mean estimates
p.old: Vector of fitted variance estimates

The EM loop for the LSS model

Description

loop_lss is the EM loop function for the LSS model to be utilised by various other higher level functions

Usage

loop_lss(
  alldat,
  xiold,
  omega2old,
  nuold,
  mean.ind,
  var.ind,
  nu.ind,
  para.space,
  maxit,
  eps,
  int.maxit,
  print.it
)
loop_lss(
  alldat,
  xiold,
  omega2old,
  nuold,
  mean.ind,
  var.ind,
  nu.ind,
  para.space,
  maxit,
  eps,
  int.maxit,
  print.it
)

Arguments

`alldat`	Dataframe containing all the data for the models. Outcome in the first column.
`xiold`	Vector of initial location parameter estimates to be fit in the location model.
`omega2old`	Vector of initial scale2 parameter estimates to be fit in the scale2 model.
`nuold`	Vector of initial nu parameter estimates to be fit in the nu model.
`mean.ind`	Vector containing the column numbers of the data in 'alldat' to be fit as covariates in the location model.
`var.ind`	Vector containing the column numbers of the data in 'alldat' to be fit as covariates in the scale2 model. FALSE indicates a constant variance model.
`nu.ind`	Vector containing the column numbers of the data in 'alldat' to be fit as covariates in the nu model. NULL indicates constant model.
`para.space`	Parameter space to search for variance parameter estimates. "positive" means only search positive parameter space, "negative" means search only negative parameter space and "all" means search all.
`maxit`	Number of maximum iterations for the main EM algorithm.
`eps`	Very small number for the convergence criteria.
`int.maxit`	Number of maximum iterations for the internal EM algorithm for the location and scale.
`print.it`	Logical to indicate if the estimates for each iteration should be printed.

Value

A list of the results from the algorithm, including conv, reldiff, it, mean, xi.new, omega2.new, nu.new, fitted.xi

conv: Logical argument indicating if convergence occurred
it: Total iterations performed of the EM algorithm
reldiff: the positive convergence tolerance that occured at the final iteration
xinew: Vector of location parameter estimates
omega2new: Vector of scale squared parameter estimates
nunew: Vector of shape parameter estimates
fitted.xi: Vector of fitted location estimates

Calculations for SN

Description

lss_calc performs calculations for transforming SN data (location, scale and shape) to mean, variance and skew. This function is utilised by other, higher level functions.

Usage

lss_calc(x)
lss_calc(x)

Arguments

`x`	Object of class lssVarReg (output from `lssVarReg`).

Value

dataframe containing:

y: y variable
x: x variable
eta: $\eta$ or fitted location estimates
omega: $\omega$ or fitted scale estimates
shape: $\alpha$ or fitted shape estimates
predicted mean: fitted mean estimates
predicted variance: fitted variance estimates
Predicted skewness: fitted skewness estimates
stand.res2: Squared standardised residuals

Semi parametric location, shape and scale regression

Description

lssVarReg performs a semiparametric location ( $\xi$ or xi), shape ( $\nu$ or nu) and scale ( $\omega$ or omega) regression model. Currently, this is only designed for a single covariate that is fit in the location, scale and shape models.

Usage

lssVarReg(
  y,
  x,
  locationmodel = c("constant", "linear", "semi"),
  scale2model = c("constant", "linear", "semi"),
  shapemodel = c("constant", "linear"),
  knots.l = 2,
  knots.sc = 2,
  knots.sh = 2,
  degree = 2,
  mono.scale = c("none", "inc", "dec"),
  para.space = c("all", "positive", "negative"),
  location.init = NULL,
  scale2.init = NULL,
  shape.init = NULL,
  int.maxit = 1000,
  print.it = FALSE,
  control = list(...),
  ...
)
lssVarReg(
  y,
  x,
  locationmodel = c("constant", "linear", "semi"),
  scale2model = c("constant", "linear", "semi"),
  shapemodel = c("constant", "linear"),
  knots.l = 2,
  knots.sc = 2,
  knots.sh = 2,
  degree = 2,
  mono.scale = c("none", "inc", "dec"),
  para.space = c("all", "positive", "negative"),
  location.init = NULL,
  scale2.init = NULL,
  shape.init = NULL,
  int.maxit = 1000,
  print.it = FALSE,
  control = list(...),
  ...
)

Arguments

`y`	Vector containing outcome data. Must be no missing data.
`x`	Vector containing the covariate data, same length as `y`. Must be no missing data.
`locationmodel`	Text to specify the location model to be fit. Options: `"constant"` = constant model (intercept only), `"linear"` = linear term with x covariate, `"semi"` = semiparametric spline (specify with `knots.l`).
`scale2model`	Text to specify the scale^2 model to be fit. Options: `"constant"` = constant term only, `"linear"` = linear term with `x` covariate, `"semi"` = semiparametric spline (specify with `knots.sc`)
`shapemodel`	Text to specify the shape model to be fit. Options: `"constant"` = constant shape model, `"linear"` = linear term with x covariate, `"semi"` = semiparametric spline (specify with `knots.sh`).
`knots.l`	Integer indicating the number of internal knots to be fit in the location model. Default is '2'. (Note that the knots are placed equidistantly over x.)
`knots.sc`	Integer indicating the number of internal knots to be fit in the scale^2 model. Default is '2'. (Note that the knots are placed equidistantly over x.)
`knots.sh`	Integer indicating the number of internal knots to be fit in the shape model. Default is '2'. (Note that the knots are placed equidistantly over x.)
`degree`	Integer to indicate the degree of the splines fit in the location and scale. Default is '2'.
`mono.scale`	Text to indicate whether the scale2 model is monotonic. Default is `"none"` (no monotonic constraints). Options are `"inc"` for increasing or `"dec"` for decreasing. If this is chosen, the appropriate `para.space` is set autopmatically (`"positive"` for `inc`, `"negative"` for `dec`).
`para.space`	Text to indicate the parameter space to search for scale2 parameter estimates. `"positive"` means only search positive parameter space, `"negative"` means search only negative parameter space and `"all"` means search all parameter spaces. Default is `all`.
`location.init`	Vector of initial parameter estimates for the location model. Defaults to vector of 1's of appropriate length.
`scale2.init`	Vector of initial parameter estimates for the scale^2 model. Defaults to vector of 1's of appropriate length.
`shape.init`	Vector of initial parameter estimates for the shape model. Defaults to vector of 1's of appropriate length.
`int.maxit`	Integer of maximum iterations for the internal location and scale EM algorithm. Default is 1000 iterations.
`print.it`	Logical for printing progress of estimates through each iteration. Default is `FALSE`.
`control`	List of control parameters for the algorithm. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

lssVarReg returns an object of class "lssVarReg", which inherits most from class "VarReg". This object of class lssVarReg is a list of the following components:

modeltype: Text indicating the model that was fit, always "LSS model".
locationmodel, scale2model, shapemodel, knots.l, knots.sc, knots.sh, degree,mono.scale : Returning the input variables as described above
converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed of the main algorithm (not including the internal EM algorithm).
reldiff: the positive convergence tolerance that occured at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
location: Vector of the maximum likelihood estimates of the location parameters.
scale2: Vector of the maximum likelihood estimates of the scale (squared) parameters.
shape: Vector of the maximum likelihood estimates of the shape parameters.
data: Dataframe containing the variables included in the model.

Examples

## run a model with linear mean, linear variance and constant shape (not run):
## lssmodel<-lssVarReg(mcycle$accel, mcycle$times,  locationmodel="linear", scale2model="linear",
## shapemodel="constant",  maxit=10000)
## run a model with linear mean, linear variance and constant shape (not run):
## lssmodel<-lssVarReg(mcycle$accel, mcycle$times,  locationmodel="linear", scale2model="linear",
## shapemodel="constant",  maxit=10000)

Semi parametric location, shape and scale regression

Description

lssVarReg.multi performs a semiparametric location ( $\xi$ or xi), shape ( $\nu$ or nu) and scale ( $\omega$ or omega) regression model. This is designed for multiple covariates that are fit in the location, scale and shape models.

Usage

lssVarReg.multi(
  y,
  x,
  locationmodel = c("constant", "linear", "semi"),
  location.vars = c(1),
  scale2model = c("constant", "linear", "semi"),
  scale2.vars = c(1),
  shapemodel = c("constant", "linear", "semi"),
  shape.vars = c(1),
  knots.l = NULL,
  knots.sc = NULL,
  knots.sh = NULL,
  degree = 2,
  location.init = NULL,
  scale2.init = NULL,
  shape.init = NULL,
  int.maxit = 1000,
  print.it = FALSE,
  control = list(...),
  ...
)
lssVarReg.multi(
  y,
  x,
  locationmodel = c("constant", "linear", "semi"),
  location.vars = c(1),
  scale2model = c("constant", "linear", "semi"),
  scale2.vars = c(1),
  shapemodel = c("constant", "linear", "semi"),
  shape.vars = c(1),
  knots.l = NULL,
  knots.sc = NULL,
  knots.sh = NULL,
  degree = 2,
  location.init = NULL,
  scale2.init = NULL,
  shape.init = NULL,
  int.maxit = 1000,
  print.it = FALSE,
  control = list(...),
  ...
)

Arguments

`y`	Vector containing outcome data. Must be no missing data.
`x`	Matrix containing the covariate data, same length as `y`. Must be no missing data.
`locationmodel`	Vector to specify the location model to be fit for each covariate. Options: `"constant"` = constant model (intercept only), `"linear"` = linear term with x covariate, `"semi"` = semiparametric spline (specify with `knots.l`).
`location.vars`	Vector to specify the column(s) in `x` referring to covariates to be fit in the location model, eg c(1,2) indicates columns 1 and 2 in `x`. Must be the same length as `locationmodel` which specifies if they are fit as linear/semi. If semi, use `knots.l` to specify knots.
`scale2model`	Vector to specify the scale^2 model to be fit for each covariate. Options: `"constant"` = constant term only, `"linear"` = linear term with `x` covariate, `"semi"` = semiparametric spline (specify with `knots.sc`)
`scale2.vars`	Vector to specify the column(s) in `x` referring to covariates to be fit in the scale^2 model, eg c(1,2) indicates columns 1 and 2 in `x`. Must be the same length as `scale2model` which specifies if they are fit as linear/semi. If semi, use `knots.sc` to specify knots.
`shapemodel`	Vector to specify the shape model to be fit for each covariate. Options: `"constant"` = constant shape model, `"linear"` = linear term with x covariate, `"semi"` = semiparametric spline (specify with `knots.sh`).
`shape.vars`	Vector to specify the column(s) in `x` referring to covariates to be fit in the shape model, eg c(1,2) indicates columns 1 and 2 in `x`. Must be the same length as `shapemodel` which specifies if they are fit as linear/semi. If semi, use `knots.sh` to specify knots.
`knots.l`	Vector indicating the number of internal knots to be fit in the location model for each covariate. Default is '2'. (Note that the knots are placed equidistantly over x.)
`knots.sc`	Vector indicating the number of internal knots to be fit in the scale^2 model for each covariate. Default is '2'. (Note that the knots are placed equidistantly over x.)
`knots.sh`	Vector indicating the number of internal knots to be fit in the shape model for each covariate. Default is '2'. (Note that the knots are placed equidistantly over x.)
`degree`	Integer to indicate the degree of the splines fit in the location, scale and shape. Default is '2'.
`location.init`	Vector of initial parameter estimates for the location model. Defaults to vector of 1's of appropriate length.
`scale2.init`	Vector of initial parameter estimates for the scale^2 model. Defaults to vector of 1's of appropriate length.
`shape.init`	Vector of initial parameter estimates for the shape model. Defaults to vector of 1's of appropriate length.
`int.maxit`	Integer of maximum iterations for the internal location and scale EM algorithm. Default is 1000 iterations.
`print.it`	Logical for printing progress of estimates through each iteration. Default is `FALSE`.
`control`	List of control parameters for the algorithm. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

lssVarReg returns an object of class "lssVarReg", which inherits most from class "VarReg". This object of class lssVarReg is a list of the following components:

modeltype: Text indicating the model that was fit, always "LSS model" for this model.
locationmodel, scale2model, shapemodel, knots.l, knots.sc, knots.sh, degree,mono.scale : Returning the input variables as described above
converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed of the main algorithm (not including the internal EM algorithm).
reldiff: the positive convergence tolerance that occured at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
location: Vector of the maximum likelihood estimates of the location parameters.
scale2: Vector of the maximum likelihood estimates of the scale (squared) parameters.
shape: Vector of the maximum likelihood estimates of the shape parameters.
data: Dataframe containing the variables included in the model.

Examples

## not run
## library(palmerpenguins)
## cc<-na.omit(penguins)
## y<-cc$body_mass_g
## x<-as.data.frame(cbind(cc$bill_length_mm, cc$flipper_length_mm,cc$bill_depth_mm))
## colnames(x) <-c("bill length mm", "flipper length mm","bill depth mm")
## model1<-lssVarReg.multi(y, x,
##                         locationmodel="linear", location.vars = 2,
##                         scale2model="constant",
##                         shapemodel=c("linear", "semi"), shape.vars = c(2,3),
##                         knots.sh = 1, int.maxit=10 )
## model1[-21] ## print model
## not run
## library(palmerpenguins)
## cc<-na.omit(penguins)
## y<-cc$body_mass_g
## x<-as.data.frame(cbind(cc$bill_length_mm, cc$flipper_length_mm,cc$bill_depth_mm))
## colnames(x) <-c("bill length mm", "flipper length mm","bill depth mm")
## model1<-lssVarReg.multi(y, x,
##                         locationmodel="linear", location.vars = 2,
##                         scale2model="constant",
##                         shapemodel=c("linear", "semi"), shape.vars = c(2,3),
##                         knots.sh = 1, int.maxit=10 )
## model1[-21] ## print model

mcycle dataset.

Description

A dataset containing 133 observations from a simulated motorcycle accident, used to test crash helmets.

Usage

mcycle
mcycle

Format

A data frame with 133 rows and 2 variables:

times: in milliseconds from time of impact
accel: in g, acceleration of the head

...

Source

Silverman, B. W. (1985) Some aspects of the spline smoothing approach to non-parametric curve fitting. Journal of the Royal Statistical Society series B 47, 1-52.

References

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer.

Examples

library(VarReg)
data(mcycle)
attach(mcycle)
plot(times,accel)
library(VarReg)
data(mcycle)
attach(mcycle)
plot(times,accel)

Plots graphics for a location, scale and shape regression model

Description

plotlssVarReg is used to produce graphics for models fit in the VarReg package with the lssVarReg function. As the skew-normal distribution is used to fit this type of model, the data needs to be transformed from the SN parameters (location, scale and shape) to the typical mean, variance and skew parameters.

Usage

plotlssVarReg(x, knot.lines = FALSE, xlab = "x", ylab = "y")
plotlssVarReg(x, knot.lines = FALSE, xlab = "x", ylab = "y")

Arguments

`x`	Object of class lssVarReg (output from `lssVarReg`).
`knot.lines`	Logical to show the knot lines on the graphics (if model is type "semi"). Default is `TRUE`
`xlab`	Label to be placed on the x axis of graphics (covariate)
`ylab`	Label to be placed on the y axis of graphics (outcome)

Value

A graphic is returned, as well as a dataframe. The graphic returned is a 2 by 2 plot of:

the mean function over the x-variable, with or without the knot lines indicated
the variance function over the x-variable, with or without the knot lines indicated
the skew function over the x-variable, with or without the knot lines indicated
a Q-Q plot of the squared residuals from the model, plotted against the Chi-squared (df=1) distribution. For data from a skew-normal distribution, these residuals should follow a Chi-squared (df=1) distribution, regardless of skew.

The dataframe returned contains the following columns:

x: x variable
y: y variable
eta: ( $\eta$ ), the location parameter
omega: ( $\omega$ ), the scale parameter
shape: ( $\nu$ ), the shape parameter
predicted~mean: ( $\mu$ ), the mean
predicted~variance: ( $\sigma^2$ ), the variance
predicted~skewness: ( $\gamma$ ), the skew
stand.res2: the standardised residuals squared.

Examples

data(mcycle)
## not run. LSS model followed by the basic plot command
##lssmodel<-lssVarReg(mcycle$accel, mcycle$times,  locationmodel="linear", scale2model="linear",
##shapemodel="constant", maxit=10000)
##lssplot_out<-plotlssVarReg(lssmodel, xlab="Time in seconds", ylab="Acceleration")
data(mcycle)
## not run. LSS model followed by the basic plot command
##lssmodel<-lssVarReg(mcycle$accel, mcycle$times,  locationmodel="linear", scale2model="linear",
##shapemodel="constant", maxit=10000)
##lssplot_out<-plotlssVarReg(lssmodel, xlab="Time in seconds", ylab="Acceleration")

Plots graphics for a mean and variance regression model

Description

plotVarReg to produce graphics for models fit in this package.

Usage

plotVarReg(
  x,
  knot.lines = FALSE,
  ci = FALSE,
  ci.type = c("im", "boot"),
  bootreps = 1000,
  xlab = "x",
  ylab = "y",
  control = list(...),
  ...
)
plotVarReg(
  x,
  knot.lines = FALSE,
  ci = FALSE,
  ci.type = c("im", "boot"),
  bootreps = 1000,
  xlab = "x",
  ylab = "y",
  control = list(...),
  ...
)

Arguments

`x`	Object of class `VarReg` (see `semiVarReg`).
`knot.lines`	Logical to indicate if knot lines should be shown on graphics (if model is type "semi"). Default is `FALSE`
`ci`	Logical indicate if 95% CI should be shown on the plots. Default is `FALSE` and `ci.type="im"`.
`ci.type`	Text to indicate the type of CI to plot. Either `"im"` (information matrix) or `"boot"` (bootstrapped). Default is `"im"`.
`bootreps`	Integer to indicate the number of bootstrap replications to be performed if `ci.type="boot"`. Default is `1000`.
`xlab`	Text for the label to be placed on the `x` axis of graphics (covariate)
`ylab`	Text for the label to be placed on the `y` axis of graphics (outcome)
`control`	list of control parameters to be used in bootstrapping. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

This function returns a 2x2 plot, with slightly different plots given, depending on the outcome data. For uncensored data, the plots are:

the mean function over the x-variable, with or without 95% CI, and with or without the knot lines indicated
the variance function over the x-variable, with or without 95% CI and with or without the knot lines indicated
a Q-Q plot of the residuals from the model
a histogram of the residuals from the model

If the outcome data is censored, the last two plots are no longer appropriate. Given the censored residuals from the model, we can compare the squared standardised residuals (given in black) with their censoring indicator to the chi-squared distribution with one degree of freedom (given in red). This is one of the plots given for censored data, and the other is a plot of the data, coloured by the censoring status. The triangles with the point at the top are bottom censored and the triangles with the point at the bottom are top censored.

Examples

data(mcycle)
linmodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="linear", varmodel="linear",
maxit=10000)
plotVarReg(linmodel)
plotVarReg(linmodel, ci=TRUE, ci.type="im", ylab="Range", xlab="Time in seconds")
##not run
##plotVarReg(linmodel, ci=TRUE, ci.type="boot", bootreps=10,ylab="Acceleration",
##xlab="Time in seconds")

##not run
##semimodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, maxit=10000)
##plotVarReg(semimodel, ci=TRUE, ci.type="boot",bootreps=10,ylab="Acceleration",
##xlab="Time in seconds", maxit=10000)
data(mcycle)
linmodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="linear", varmodel="linear",
maxit=10000)
plotVarReg(linmodel)
plotVarReg(linmodel, ci=TRUE, ci.type="im", ylab="Range", xlab="Time in seconds")
##not run
##plotVarReg(linmodel, ci=TRUE, ci.type="boot", bootreps=10,ylab="Acceleration",
##xlab="Time in seconds")

##not run
##semimodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, maxit=10000)
##plotVarReg(semimodel, ci=TRUE, ci.type="boot",bootreps=10,ylab="Acceleration",
##xlab="Time in seconds", maxit=10000)

Searches for best semi parametric mean and variance regression model

Description

searchVarReg performs multiple semi-parametric mean and variance regression models for a covariate of interest, in order to search for the optimal number of knots. The best model is chosen based on the information criterion of preference ("selection"). At the moment, this is only designed for a single covariate that is fit in both the mean and variance models.

Usage

searchVarReg(
  y,
  x,
  cens.ind = NULL,
  maxknots.m = 3,
  maxknots.v = 3,
  degree = 2,
  mono.var = c("none", "inc", "dec"),
  selection = c("AIC", "AICc", "HQC", "BIC"),
  print.it = FALSE,
  control = list(...),
  ...
)
searchVarReg(
  y,
  x,
  cens.ind = NULL,
  maxknots.m = 3,
  maxknots.v = 3,
  degree = 2,
  mono.var = c("none", "inc", "dec"),
  selection = c("AIC", "AICc", "HQC", "BIC"),
  print.it = FALSE,
  control = list(...),
  ...
)

Arguments

`y`	Vector containing outcome data. Must be no missing data and any censored values must be set to the limits of detection.
`x`	Vector containing the covariate data. Must be no missing data and same length as `y`.
`cens.ind`	Vector containing the censoring indicator, if applicable. There must be no missing data contained in the vector and this vector should be the same length as `y`. `"0"` values indicate uncensored data, `"1"` indicates right, or upper, censoring and `"-1"` indicates left, or lower, censoring. The default is `NULL` which indicates there is no censored data.
`maxknots.m`	Integer indicating the maximum number of internal knots to be fit in the mean model. Default is `3`. (Note that the knots are placed equidistantly over x.)
`maxknots.v`	Integer indicating the maximum number of internal knots to be fit in the variance model. Default is `3`. (Note that the knots are placed equidistantly over x.)
`degree`	The degree of the splines fit in the mean and variance. Default is `2`.
`mono.var`	Text to indicate whether the variance model is monotonic (only applied to 'linear' or semi-parametric variance models). Default is "`none`" (no monotonic constraints). Options are "`inc`" for increasing or "`dec`" for decreasing. If the variance model is linear, the parameter space is constrained (positive for increasing and negative for decreasing). For semi-parametric variance models, the appropriate monotonic B splines are fit in the semi-parametric variance model.
`selection`	Text to indicate which information criteria is to be used for the selection of the best model. Choices are "`AIC`", "`AICc`", "`BIC`" and "`HQC`". Default is "`AIC`".
`print.it`	Logical to indicate whether to print progress from each model as the models are performed. Default is `FALSE`.
`control`	list of control parameters. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Details

A matrix of models are performed, of increasing complexity. Mean models start at a zero mean model, then constant mean, linear, 0 internal knots, etc, up to a maximum internal knots as specified in maxknots.m. Variance models start at constant variance, linear variance, 0 internal knots, etc, up to max internal knots as specified in maxknots.v.

Note that this function can take some time to run, due to the number of models to be fit. A window will appear on windows based systems to show a progress bar for the function.

Value

searchVarReg returns an list, with the following components:

ll: a dataframe of the log-likelihoods from each of the models that have been fit.
AIC: a dataframe of the AIC from each of the models that have been fit. The parameters fit in the mean model are given in the columns, and the parameters in the variance are given in the rows.
AICc: a dataframe of the AIC-c from each of the models that have been fit.
BIC: a dataframe of the BIC from each of the models that have been fit.
HQC: a dataframe of the HQC from each of the models that have been fit.
best.model: an object of class VarReg (see semiVarReg) containing the output from the optimal model (that model within the specified models in the mean and variance with the lowest information criterion according to the criterion selected).

Examples

data(mcycle)
### not run
### find<-searchVarReg(mcycle$accel, mcycle$times, maxknots.v=3, maxknots.m=3,
### selection="HQC", maxit=10000)
data(mcycle)
### not run
### find<-searchVarReg(mcycle$accel, mcycle$times, maxknots.v=3, maxknots.m=3,
### selection="HQC", maxit=10000)

Semi parametric mean and variance regression

Description

semiVarReg performs semi-parametric mean and variance regression models. Currently, this is only designed for a single covariate that is fit in the mean and variance models.

Usage

semiVarReg(
  y,
  x,
  cens.ind = NULL,
  meanmodel = c("zero", "constant", "linear", "semi"),
  mean.intercept = TRUE,
  varmodel = c("constant", "linear", "semi"),
  knots.m = 2,
  knots.v = 2,
  degree = 2,
  mono.var = c("none", "inc", "dec"),
  para.space = c("all", "positive", "negative"),
  control = list(...),
  ...
)
semiVarReg(
  y,
  x,
  cens.ind = NULL,
  meanmodel = c("zero", "constant", "linear", "semi"),
  mean.intercept = TRUE,
  varmodel = c("constant", "linear", "semi"),
  knots.m = 2,
  knots.v = 2,
  degree = 2,
  mono.var = c("none", "inc", "dec"),
  para.space = c("all", "positive", "negative"),
  control = list(...),
  ...
)

Arguments

`y`	Vector containing outcome data. Must be no missing data and any censored values must be set to the limits of detection.
`x`	Vector containing the covariate data. Must be no missing data and same length as `y`.
`cens.ind`	Vector containing the censoring indicator, if applicable. There must be no missing data contained in the vector and this vector should be the same length as `y`. `"0"` values indicate uncensored data, `"1"` indicates right, or upper, censoring and `"-1"` indicates left, or lower, censoring. The default is `NULL` which indicates there is no censored data.
`meanmodel`	Text to specify the mean model to be fit to the data. The possible inputs are `"zero"`, `"constant"`, `"linear"` or `"semi"`. `"semi"` indicates a semi-parametric spline model, with the number of internal knots specified in `knots.m`.
`mean.intercept`	Logical argument to indicate if the mean model is to include an intercept term. This option is only available in the censored mean model, and the default=`TRUE`.
`varmodel`	Text to specify the variance model to be fit to the data. The possible inputs are `"constant"`, `"linear"` or `"semi"`. `"semi"` indicates a semi-parametric B-spline model, with the number of internal knots specified in `knots.v`.
`knots.m`	Integer indicating the number of internal knots to be fit in the semi-parametric mean model. Knots are placed equidistantly over the covariate. The default value is `2`.
`knots.v`	Integer indicating the number of internal knots to be fit in the semi-parametric variance model. Knots are placed equidistantly over the covariate. The default value is `2`.
`degree`	Integer indicating the degree of the splines fit in the mean and the variance models. The default value is `2`.
`mono.var`	Text to indicate whether the variance model is monotonic. Note that this is not available for the `"constant"` variance model. Options are `"none"`, `"inc"` or `"dec"`, with the default=`"none"`. `"Inc"` indicates increasing monotonic and `"dec"` indicates decreasing monotonic. If the variance model is linear, the parameter space is constrained (positive for increasing and negative for decreasing). For semi-parametric variance models, the appropriate monotonic B-splines are fit in the semi-parametric variance model.
`para.space`	Text to indicate the parameter space to search for scale2 parameter estimates. `"positive"` means only search positive parameter space, `"negative"` means search only negative parameter space and `"all"` means search all parameter spaces. Default is `all`.
`control`	list of control parameters. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

semiVarReg returns an object of class "VarReg" which inherits some components from the class "glm". This object of class "VarReg" is a list containing the following components:

modeltype: Text indicating the model that was fit, indicating if a censored approach or an uncensored approach was performed.
knots.m, knots.v, degree, meanmodel, varmodel: Returning the input variables as described above
converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed.
reldiff: the positive convergence tolerance that occurred at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
boundary: Logical argument indicating if the MLE is on the boundary of the parameter space.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
mean.ind: Vector of integer(s) indicating the column number(s) in the dataframe data that were fit in the mean model.
mean: Vector of the maximum likelihood estimates of the mean parameters.
var.ind: Vector of integer(s) indicating the column(s) in the dataframe data that were fit in the variance model.
variance: Vector of the maximum likelihood estimates of the variance parameters.
cens.ind: Integer indicating the column in the dataframe data that corresponds to the censoring indicator.
data: Dataframe containing the variables included in the model.

Examples

data(mcycle)
## run a model with linear mean and linear variance:
linmodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="linear", varmodel="linear",
 maxit=10000)
## run a model with semi-parametric mean (4 internal knots) and semi-parametric variance (2 knots):
##not run
##semimodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, maxit=10000)
## run a model with semi-parametric mean (4 internal knots) and semi-parametric monotonic
## variance (2 knots):
## not run
##semimodel_inc<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, mono.var="inc")
data(mcycle)
## run a model with linear mean and linear variance:
linmodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="linear", varmodel="linear",
 maxit=10000)
## run a model with semi-parametric mean (4 internal knots) and semi-parametric variance (2 knots):
##not run
##semimodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, maxit=10000)
## run a model with semi-parametric mean (4 internal knots) and semi-parametric monotonic
## variance (2 knots):
## not run
##semimodel_inc<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, mono.var="inc")

Semi parametric mean and variance regression (multivariate)

Description

semiVarReg.multi performs semi-parametric mean and variance regression models. This is designed for multiple covariates fit in the mean and variance models.

Usage

semiVarReg.multi(
  y,
  x,
  mean.model = c("zero", "constant", "linear", "semi"),
  mean.vars = c(1),
  knots.m = NULL,
  var.model = c("constant", "linear", "semi"),
  var.vars = c(1),
  knots.v = NULL,
  degree = 2,
  control = list(...),
  ...
)
semiVarReg.multi(
  y,
  x,
  mean.model = c("zero", "constant", "linear", "semi"),
  mean.vars = c(1),
  knots.m = NULL,
  var.model = c("constant", "linear", "semi"),
  var.vars = c(1),
  knots.v = NULL,
  degree = 2,
  control = list(...),
  ...
)

Arguments

`y`	Vector containing outcome data. Must be no missing data and any censored values must be set to the limits of detection.
`x`	Matrix containing the covariate data. Must be no missing data and same length as `y`.
`mean.model`	Vector to specify the mean model to be fit to the data. The possible inputs are `"zero"`, `"constant"`, or a vector to indicate if covariates are to be `"linear"` or `"semi"`. `"semi"` indicates a semi-parametric spline model, with the number of internal knots specified in `knots.m`. If covariates are fit, each covariate needs an indicator of `"linear"` or `"semi"`, where `mean.vars` specifies each covariate.
`mean.vars`	Vector to specify column(s) in `x` referring to covariates to be fit in the mean model, eg c(1,2) indicates columns 1 and 2 in `x`. Must be the same length as `mean.model` which specifies if they are fit as linear/semi. If semi, use `knots.m` to specify knots.
`knots.m`	Vector indicating the number of internal knots to be fit in each of covariate(s) fit in the semi-parametric mean model. Must be one entry per `"semi"` covariate in `mean.model`. Knots are placed equidistantly over each covariate.
`var.model`	Vector to specify the variance model to be fit to the data. The possible inputs are `"constant"`, or a vector to indicate if each covariate is to be `"linear"` or `"semi"`. `"semi"` indicates a semi-parametric B-spline model, with the number of internal knots specified in `knots.v`.
`var.vars`	Vector to specify column(s) in `x` referring to covariates to be fit in the variance model, eg c(1,2) indicates columns 1 and 2 in `x`. Must be the same length as `var.model` which specifies if they are fit as linear/semi. If semi, use `knots.v` to specify knots.
`knots.v`	Vector indicating the number of internal knots to be fit in the semi-parametric variance model. Knots are placed equidistantly over the covariate.
`degree`	Integer indicating the degree of the splines fit in the mean and the variance models. The default value is `2`.
`control`	list of control parameters. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

semiVarReg.multi returns an object of class "VarReg" which inherits some components from the class "glm". This object of class "VarReg" is a list containing the following components:

modeltype: Text indicating the model that was fit, indicating an uncensored approach was performed.
knots.m, knots.v, degree, meanmodel, varmodel: Returning the input variables as described above
converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed.
reldiff: the positive convergence tolerance that occurred at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
boundary: Logical argument indicating if the MLE is on the boundary of the parameter space.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
mean.ind: Vector of integer(s) indicating the column number(s) in the dataframe data that were fit in the mean model.
mean: Vector of the maximum likelihood estimates of the mean parameters.
var.ind: Vector of integer(s) indicating the column(s) in the dataframe data that were fit in the variance model.
variance: Vector of the maximum likelihood estimates of the variance parameters.
data: Dataframe containing the variables included in the model.

Examples

data(mcycle)
## run a model with linear mean and linear variance:
linmodel<-semiVarReg.multi(mcycle$accel, x=mcycle, mean.model="linear",mean.vars=2,
var.model="linear", var.vars=2,  maxit=10000)
## run a model with semi-parametric mean (4 internal knots) and semi-parametric variance (2 knots):
##not run
##semimodel<-semiVarReg.multi(mcycle$accel, x=mcycle, meanmodel="semi",mean.vars=2, varmodel="semi",
##var.vars=2,knots.m=4, knots.v=2, maxit=10000)
data(mcycle)
## run a model with linear mean and linear variance:
linmodel<-semiVarReg.multi(mcycle$accel, x=mcycle, mean.model="linear",mean.vars=2,
var.model="linear", var.vars=2,  maxit=10000)
## run a model with semi-parametric mean (4 internal knots) and semi-parametric variance (2 knots):
##not run
##semimodel<-semiVarReg.multi(mcycle$accel, x=mcycle, meanmodel="semi",mean.vars=2, varmodel="semi",
##var.vars=2,knots.m=4, knots.v=2, maxit=10000)

SE calculations for mean and variance regression models

Description

seVarReg calculates SE for an object of class VarReg. If the result is not on a boundary, the Fishers Information matrix SE are given. The bootstrapped 95% CI can also be calculated. Designed to be called by the plot function plotVarReg, rather than run by a user.

Usage

seVarReg(
  x,
  boot = FALSE,
  bootreps = 1000,
  vector.mean = x$data[, 2],
  vector.variance = x$data[, 2],
  control = list(...),
  ...
)
seVarReg(
  x,
  boot = FALSE,
  bootreps = 1000,
  vector.mean = x$data[, 2],
  vector.variance = x$data[, 2],
  control = list(...),
  ...
)

Arguments

`x`	Object of class `VarReg` to determin the SE (eg. result from `semiVarReg`).
`boot`	Logical to indicate if bootstrapped CI should be calculated. Default is `FALSE`.
`bootreps`	Number of bootstraps to be performed if `boot=TRUE`. Default is `1000`.
`vector.mean`	Vector of `x` values for which the SE of the mean is to be calculated. Default is the `x` covariate from the model.
`vector.variance`	Vector of `x` values for which the SE of the variance is to be calculated. Default is the actual `x` covariate from the model.
`control`	List of control parameters for the bootstrapped models. See `VarReg.control`.
`...`	arguments to be used to form the default control argument if it is not supplied directly

Value

The result is a list of results. This includes:

mean.est: dataframe of overall results from the mean model, including parameter estimates from the model, SEs from information matrix (if boundary=FALSE) and if specified, the SE from bootstrapping with the bootstrapped 95% CI.
variance.est: dataframe of overall results from the variance model, including parameter estimates from the model, SEs from information matrix (if boundary=FALSE) and if specified, the SE from bootstrapping with the bootstrapped 95% CI.
mean.im: dataframe of the expected information matrices for the mean (as appropriate)
variance.im: dataframe of the expected information matrices for the variance (as appropriate)
mean.outputs: dataframe with complete output for mean graphics. Includes the vector.mean as input, and the mean vector (mean.mean) and the SE vector mean.se.im, and bootstrapping outputs as appropriate.
variance.outputs: dataframe with complete output for variance graphics. Includes the vector.variance as input, and the mean vector (var.mean) and the SE vector var.se.im, and bootstrapping outputs as appropriate.

Examples

data(mcycle)
##Fit model with range as a covariate in the mean and the variance model
semimodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="linear",
knots.m=4, maxit=10000)
##Calculate SE
se1<-seVarReg(semimodel, boot=FALSE)
##not run: with bootstrapping
##se2<-seVarReg(semimodel, boot=TRUE, bootreps=10)
##not run: calculate mean and SE for a given sequence
##test.seq<-seq(min(mcycle$times), max(mcycle$times),
##by=((max(mcycle$times)-min(mcycle$times))/999))
##se2<-seVarReg(semimodel, boot=TRUE, bootreps=10, vector.mean=test.seq)
data(mcycle)
##Fit model with range as a covariate in the mean and the variance model
semimodel<-semiVarReg(mcycle$accel, mcycle$times, meanmodel="semi", varmodel="linear",
knots.m=4, maxit=10000)
##Calculate SE
se1<-seVarReg(semimodel, boot=FALSE)
##not run: with bootstrapping
##se2<-seVarReg(semimodel, boot=TRUE, bootreps=10)
##not run: calculate mean and SE for a given sequence
##test.seq<-seq(min(mcycle$times), max(mcycle$times),
##by=((max(mcycle$times)-min(mcycle$times))/999))
##se2<-seVarReg(semimodel, boot=TRUE, bootreps=10, vector.mean=test.seq)

VarReg: Semi-parametric mean and variance regression

Description

Methods for fitting semi-parametric mean and variance models, with normal or censored data. Also extended to allow a regression in the location, scale and shape parameters.

Details

This package provides functions to fit semi-parametric mean and variance regression models. These models are based upon EM-type algorithms, which can have more stable convergence properties than other algorithms for additive variance regression models.

The primary function to use for linear and semi-parametric mean and variance models is semiVarReg. This function also is able to fit models to censored outcome data. There is also a plot function for these models called plotVarReg. A search function has also been produced in order to assist users to find the optimal number of knots in the model (searchVarReg).

The other functions that are of particular use are lssVarReg and its plot function plotlssVarReg. This uses the skew-normal distribution and combines the EM algorithm with a coordinate-ascent type algorithm in order to fit a regression model in the location, scale and shape, therefore extending the semi-parametric models to non-normal data.

Multivariate models can be fit with semiVarReg.multi and lssVarReg.multi

Author(s)

Kristy Robledo [email protected]

Auxillary for controlling VarReg fitting

Description

Use VarReg.control to determine parameters for the fitting of semiVarReg. Typically only used internally within functions.

Usage

VarReg.control(bound.tol = 1e-05, epsilon = 1e-06, maxit = 1000)
VarReg.control(bound.tol = 1e-05, epsilon = 1e-06, maxit = 1000)

Arguments

`bound.tol`	Positive tolerance for specifying the interior of the parameter space. This allows the algorithm to terminate early if an interior maximum is found. If set to `bound.tol=Inf`, no early termination is attempted.
`epsilon`	Positive convergence tolerance. If $\theta$ is a vector of estimates, convergence is declared when $\sqrt{(\sum (\theta_{old} - \theta_{new})^2)}/ \sqrt{\sum (\theta_{old})^2}$ . This should be smaller than `bound.tol`.
`maxit`	integer giving the maximum number of EM algorithm iterations for a given parameterisation.

Details

This is used similarly to glm.control. If required, it may be internally passed to another function.

Value

A list of the three components: bound.tol, epsilon and maxit .

vcf dataset.

Description

A dataset containing 100 observations of mean velocity of circumferential fibre shortening (vcf), made by long axis and short axis echocardiography.

Usage

vcf
vcf

Format

A data frame with 133 rows and 3 variables:

pid: patient identifier
vcflong: vcf measurement from long axis
vcfshort: vcf measurement from short axis

...

Source

Data from Bland JM, Altman DG. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307-310. (Supplied by Paul D'Arbela)

Examples

library(VarReg)
data(vcf)
attach(vcf)
plot(rowMeans(vcf[-1]),vcf$vcflong-vcf$vcfshort)
library(VarReg)
data(vcf)
attach(vcf)
plot(rowMeans(vcf[-1]),vcf$vcflong-vcf$vcfshort)

Package 'VarReg'

Help Index

Censored Linear mean and variance regression

Description

Usage

Arguments

Value

The Censored data EM loop

Description

Usage

Arguments

Value

Calculation of information criterion

Description

Usage

Arguments

Value

lidar dataset.

Description

Usage

Format

Source

References

Examples

Linear mean and variance regression function

Description

Usage

Arguments

Value

The EM loop for the main mean and variance function

Description

Usage

Arguments

Value

The EM loop for the LSS model

Description

Usage

Arguments

Value

Calculations for SN

Description

Usage

Arguments

Value

Semi parametric location, shape and scale regression

Description

Usage

Arguments

Value

See Also

Examples

Semi parametric location, shape and scale regression

Description

Usage

Arguments

Value

See Also

Examples

mcycle dataset.

Description

Usage

Format

Source

References

Examples

Plots graphics for a location, scale and shape regression model

Description

Usage

Arguments

Value

See Also

Examples

Plots graphics for a mean and variance regression model

Description

Usage

Arguments

Value

See Also

Examples

Searches for best semi parametric mean and variance regression model