Description Usage Arguments Details Value Author(s) References Examples
The Akaike Information Criterion is evaluated for each submodel.
1 2 3 |
formula |
a symbolic description of the model to be fit. The details of model specification are given below. |
data |
an optional data frame containing the variables
in the model. By default the variables are taken from
the environment which |
model, x, y |
logicals. If |
var.full |
the value of variance to be used, if 0 the variance estimated from the full model is used. |
alpha |
the penalized constant. |
contrasts |
an optional list. See the |
se |
logical. if |
verbose |
if |
Models for mle.aic
are specified symbolically. A typical model has the form response ~ terms
where response
is the (numeric) response vector and terms
is a series of terms which specifies a linear predictor for response
. A terms specification of the form first+second
indicates all the terms in first
together with all the terms in second
with duplicates removed. A specification of the form first:second
indicates the the set of terms obtained by taking the interactions of all terms in first
with all terms in second
. The specification first*second
indicates the cross of first
and second
. This is the same as first+second+first:second
.
mle.aic
returns an object of class
"mle.aic"
.
The function summary
is used to obtain and print a summary of the results.
The generic accessor functions coefficients
and residuals
extract coefficients and residuals returned by mle.aic
.
The object returned by mle.aic
are:
aic |
the AIC for each submodels |
coefficients |
the parameters estimator, one row vector for each submodel. |
scale |
an estimation of the error scale, one value for each submodel. |
residuals |
the residuals from the estimated model, one column vector for each submodel. |
call |
the match.call(). |
contrasts |
|
xlevels |
|
terms |
the model frame. |
model |
if |
x |
if |
y |
if |
info |
not well working yet, if 0 no error occurred. |
se |
standard errors of the parameters, one row vector for each
submodel. Available only if |
Claudio Agostinelli
Akaike, H., (1973) Information theory and an extension of the maximum likelihood principle, in: B.N. Petrov and F. Cs\'aki, eds., Proc. 2nd International Symposium of Information Theory, Akad\'emiai Kiad\'o, Budapest, 267-281.
1 2 3 4 5 6 7 8 9 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.