Description Usage Arguments Value Examples
gw
is used to fit Generalized Waring Regression Models (GWRM), specified by giving a symbolic description of the linear predictor.
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formula |
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. |
data |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from |
weights |
an optional vector of 'prior weights' to be used in the fitting process. Should be |
k |
optional value for the |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain |
kstart |
starting value for the |
rostart |
starting value for the |
betastart |
starting values for the vector of means. |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be |
control |
a list of parameters for controlling the fitting process. |
method |
the method to be used in fitting the model. The default method initially uses non-linear minimization ( |
hessian |
if |
model |
a logical value indicating whether model frame should be included as a component of the returned value. |
x, y |
For For |
... |
further arguments. |
gw
returns an object of class "gw"
. The function summary
can be used to obtain or print a summary of the results. An object of class "gw"
is a list containing the following components:
Y
if requested (the default), the y
vector used.
W
the weights supplied, a vector of 1
s if none were.
covars
names of the covariates in the model.
nobs
number of observations.
covoffset
a logical value specifying if an offset is present.
loglik
the maximized log-likelihood.
aic
a version of Akaike's An Information Criterion, minus twice the maximized log-likelihood plus twice the number of parameters.
bic
Bayesian Information Criterion, minus twice the maximized log-likelihood plus the number of parameters multiplied by the logarithm of the number of observations.
df.residual
the residual degrees of freedom.
residuals
the residuals in the final iteration of the fit.
coefficients
a named vector of coefficients.
betaIIpars
parameters estimates of the BetaII distribution.
betascoefs
a vector of coefficients.
fitted.values
the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.
hessian
a symmetric matrix giving an estimate of the Hessian at the solution found in the optimization of the log-likelihood function.
cov
an estimate of the covariance matrix of the model coefficients.
se
a vector of the standard errors estimates of the estimated coefficients.
corr
an estimate of the correlation matrix of the model coefficients.
code
a code that indicates successful convergence of the fitter function used (see nlm
and optim
helps).
converged
logical value that indicates if the optimization algorithms succesfull.
method
the name of the fitter function used.
k
if requested, the k
value used.
kBool
a logical value specifying whether there is a k
value or it is estimated.
call
the matched call.
formula
the formula supplied.
terms
the terms
object used.
data
the data
argument.
offset
the offset vector used.
control
the value of the control
argument used.
method
the name of the fitter function used.
contrasts
(where relevant) the contrasts used.
xlevels
(where relevant) a record of the levels of the factors used in fitting.
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