View source: R/gets-base-source.R
coef.arx | R Documentation |
Extraction functions for objects of class 'arx'
## S3 method for class 'arx' coef(object, spec=NULL, ...) ## S3 method for class 'arx' fitted(object, spec=NULL, ...) ## S3 method for class 'arx' logLik(object, ...) ## S3 method for class 'arx' model.matrix(object, spec=c("mean","variance"), response=FALSE, as.zoo=TRUE, ...) ## S3 method for class 'arx' plot(x, spec=NULL, col=c("red","blue"), lty=c("solid","solid"), lwd=c(1,1), ...) ## S3 method for class 'arx' print(x, signif.stars=TRUE, ...) ## S3 method for class 'arx' residuals(object, std=FALSE, ...) ## S3 method for class 'arx' sigma(object, ...) ## S3 method for class 'arx' summary(object, ...) ## S3 method for class 'arx' vcov(object, spec=NULL, ...)
object |
an object of class 'arx' |
x |
an object of class 'arx' |
spec |
|
response |
|
as.zoo |
|
signif.stars |
|
std |
|
col |
colours of actual (default=blue) and fitted (default=red) lines |
lty |
types of actual (default=solid) and fitted (default=solid) lines |
lwd |
widths of actual (default=1) and fitted (default=1) lines |
... |
additional arguments |
coef: |
a numeric vector containing parameter estimates |
fitted: |
a |
logLik: |
log-likelihood (normal density) |
model.matrix: |
a matrix with the regressors and, optionally, the response |
plot: |
a plot of the fitted values and the residuals |
print: |
a print of the estimation results |
residuals: |
a |
sigma: |
the regression standard error ('SE of regression') |
summary: |
a print of the items in the |
vcov: |
variance-covariance matrix |
Felix Pretis, http://www.felixpretis.org/
James Reade, https://sites.google.com/site/jjamesreade/
Moritz Schwarz, https://www.inet.ox.ac.uk/people/moritz-schwarz/
Genaro Sucarrat, http://www.sucarrat.net/
arx
##simulate from an AR(1): set.seed(123) y <- arima.sim(list(ar=0.4), 40) ##simulate four independent Gaussian regressors: xregs <- matrix(rnorm(4*40), 40, 4) ##estimate an 'arx' model: An AR(2) with intercept and four conditioning ##regressors in the mean, and log-ARCH(3) in the variance: mymod <- arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:3) ##print results: print(mymod) ##plot the fitted vs. actual values, and the residuals: plot(mymod) ##print the entries of object 'mymod': summary(mymod) ##extract coefficient estimates (automatically determined): coef(mymod) ##extract mean coefficients only: coef(mymod, spec="mean") ##extract log-variance coefficients only: coef(mymod, spec="variance") ##extract all coefficient estimates: coef(mymod, spec="both") ##extract regression standard error: sigma(mymod) ##extract log-likelihood: logLik(mymod) ##extract variance-covariance matrix of mean equation: vcov(mymod) ##extract variance-covariance matrix of log-variance equation: vcov(mymod, spec="variance") ##extract and plot the fitted mean values (automatically determined): mfit <- fitted(mymod) plot(mfit) ##extract and plot the fitted variance values: vfit <- fitted(mymod, spec="variance") plot(vfit) ##extract and plot both the fitted mean and variance values: vfit <- fitted(mymod, spec="both") plot(vfit) ##extract and plot the fitted mean values: vfit <- fitted(mymod, spec="mean") plot(vfit) ##extract and plot residuals: epshat <- residuals(mymod) plot(epshat) ##extract and plot standardised residuals: zhat <- residuals(mymod, std=TRUE) plot(zhat)
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