lssVarReg | R Documentation |
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.
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(...),
...
)
y |
Vector containing outcome data. Must be no missing data. |
x |
Vector containing the covariate data, same length as |
locationmodel |
Text to specify the location model to be fit. Options: |
scale2model |
Text to specify the scale^2 model to be fit. Options: |
shapemodel |
Text to specify the shape model to be fit. Options: |
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 |
para.space |
Text to indicate the parameter space to search for scale2 parameter estimates. |
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 |
control |
List of control parameters for the algorithm. See |
... |
arguments to be used to form the default control argument if it is not supplied directly |
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.
VarReg.control
plotlssVarReg
## 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)
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