Description Usage Arguments Details Author(s) See Also Examples
Ten plots (selectable by which
) are currently available: a
time series plot with observed values of the dependent variable, fixed
effects fit, and GLARMA fit; an ACF plot of residuals; a plot
of residuals against time; a normal QQ plot; the PIT histogram;
a uniform QQ plot for the PIT; a histogram of the normal randomized
residuals; a QQ plot of the normal randomized residuals; a plot of
the autocorrelation of the normal randomized residuals; and a plot of
the partial autocorrelation of the normal randomized residuals. By
default, six plots are provided, numbers 1, 3, 5, 7, 8 and 9 from this
list of plots.
1 2 3 4 5 6 7 8 9 10 11  ## S3 method for class 'glarma'
plot(x, which = c(1L,3L,5L,7L,8L,9L), fits = 1L:3L,
ask = prod(par("mfcol")) < length(which) && dev.interactive(),
lwdObs = 1, lwdFixed = 1, lwdGLARMA = 1,
colObs = "black", colFixed = "blue", colGLARMA = "red",
ltyObs = 2, ltyFixed = 1, ltyGLARMA = 1,
pchObs = 1, legend = TRUE, residPlotType = "h", bins = 10,
line = TRUE, colLine = "red", colHist = "royal blue",
lwdLine = 2, colPIT1 = "red", colPIT2 = "black",
ltyPIT1 = 1, ltyPIT2 = 2, typePIT = "l",
ltyQQ = 2, colQQ = "black", titles, ...)

x 
An object of class 
which 
Numeric; if a subset of the plots is required, specify a subset of the numbers 1:10. 1 is the time series plot with observed values of the dependent variable, fixed effects fit, and GLARMA fit. 2 is the ACF plot of residuals. 3 is a plot of residuals against time. 4 is the normal QQ plot. 5 is the PIT histogram. 6 is the uniform QQ plot for the PIT. 7 is the histogram of the normal randomized residuals. 8 is the QQ plot of the normal randomized residuals. 9 is the autocorrelation of the normal randomized residuals. 10 is the partial autocorrelation of the normal randomized residuals. By default, plots 1, 3, 5, 7, 8 and 9 are provided. 
fits 
Numeric; if a subset of fits on the time series plot is required, specify a subset of the numbers 1:3. 1 is the observed values of the dependent variable, 2 is the fixed effects fit, and 3 is GLARMA fit. By default, all fits are provided. 
ask 
Logical; if 
lwdObs 
Numeric; the line widths for lines of the observed values of the dependent variable appearing in the time series plot. 
lwdFixed 
Numeric; the line widths for lines of the fixed effects fit appearing in the time series plot. 
lwdGLARMA 
Numeric; the line widths for lines of GLARMA fit appearing in the time series plot. 
ltyObs 
An integer or character string; the line types for the
line of the observed data of the dependent variable appearing in the
time series plot, see 
ltyFixed 
An integer or character string; the line types for the
line of the fixed effects fit appearing in the time series plot, see

ltyGLARMA 
An integer or character string; the line types for the
line of GLARMA fit appearing in the time series plot, see

pchObs 
Numeric; the point type for the point of the observed data of the dependent variable appearing in the time series plot. 
colObs 
Numeric or character; the colour of lines or points of the observed data of the dependent variable appearing in the time series plot. 
colFixed 
Numeric or character; the colour of lines of the fixed effects fit appearing in the time series plot. 
colGLARMA 
Numeric or character; the colour of lines of GLARMA fit appearing in the time series plot. 
legend 
Logical; if 
residPlotType 
A 1character string giving the type of plot
desired. The following values are possible, for details, see

bins 
Numeric; the number of bins shown in the PIT histogram and of the number of breaks in the histogram of the normal randomized residuals. By default, it is 10. 
line 
Logical; if 
colLine 
Numeric or character; the colour of the line for comparison in the PIT histogram. 
lwdLine 
Numeric; the line widths for the comparison line in the PIT histogram. 
colHist 
Numeric or character; the colour of the histogram for the PIT, and of the histogram of the normal randomized residuals. 
colPIT1 
Numeric or character; the colour of the sample uniform QQ plot in the PIT. 
colPIT2 
Numeric or character; the colour of the theoretical uniform QQ plot in the PIT. 
ltyPIT1 
An integer or character string; the line types for the
sample uniform QQ plot in the PIT, see 
ltyPIT2 
An integer or character string; the line types for the
theoretical uniform QQ plot in the PIT, see 
typePIT 
A 1character string; the type of plot for the sample uniform QQ plot in the PIT. 
ltyQQ 
An integer or character string; the line type for the
normal QQ plot of the normal randomized residuals, see

colQQ 
Numeric or character; the colour of the line in the normal QQ plot of the normal randomized residuals. 
titles 
A list of the same length as 
... 
Further arguments passed to 
plot.glarma
is an S3 generic function for objects of class
glarma
.
The plots in this method display the fixed effects fit, GLARMA fit and
various types of residuals for the GLARMA fit under the Poisson
distribution, the binomial distribution or the negative binomial
distribution, plus a number of plots of the randomized residuals (see
normRandPIT
for details of the randomized residuals). In
all, ten plots can be produced. The observed values of the dependent
variable shown in the time series plot are mainly used to compare with
the two fits.
The fixed effects fit is calculated from eta, the
multiplication of the data matrix X
and beta
coefficients in GLARMA model. In contrast, the GLARMA fit is
calculated from W, the product of the data matrix X
and delta in the GLARMA model, which is the combination
of both the beta and ARMA coefficients, and is also
called the state variable of the series.
There are some major differences for computing the fixed effects fit from eta and the GLARMA fit from W under different distributions.
Under the Poisson distribution and negative binomial distribution,
fixed effects fit = exp(eta)
and
glarma fit = exp(W).
Under the binomial distribution,
fixed effects fit = 1 / (1 + exp(eta))
and
glarma fit = 1 / (1 + exp(W)).
The residuals are calculated from the observed data and GLARMA
fit. The exact computation for the residuals depends on the type
of residuals used. The details are given in
glarma
. The ACF plot, the residuals against time and
the normal QQ plot are all based on these residuals. Further details
about those three plots are passed to acf
and
qqnorm
.
There are four plots based on the randomized residuals calculated
using normRandPIT
. These are a histogram, a QQ plot,
an autocorrelation plot and a partial autocorrelation plot.
The number of plots to be shown in the window depends on the value of
the graphical parameter mfrow
(or mfcol
). If the
displayed window is set to be large enough to show all ten plots,
they will be shown at one time. Otherwise, the required number of
plots will appear each time in the displayed window, and the user
will need to enter return
to see subsequent plots. By default,
six plots are produced.
For the time series plot in the function, the fit displayed is
specified by the argument fits
. The legend will be shown if
legend
is TRUE
. It will appear under the title
of the time series plot. Also the legend and the title will alter
automatically according to the fits shown in the plot.
"Cenanning Li" <cli113@aucklanduni.ac.nz>
plot.ts
, qqnorm
, acf
,
plot.default
, normRandPIT
.
1 2 3 4 5 6 7 8 9 10 11 12 13  ### A example from Davis, Dunsmuir Wang (1999)
## MA(1,2,5), Pearson Residuals, Fisher Scoring
data(Polio)
y < Polio[, 2]
X < as.matrix(Polio[, 3:8])
glarmamod < glarma(y, X, thetaLags = c(1, 2, 5), type = "Poi",method = "FS",
residuals = "Pearson", maxit = 100 , grad = 1e6)
## The default plots are shown
plot(glarmamod)
## The plots used only to compared GLARMA fit and the observed data
plot(glarmamod, which = 1L, fits = c(1, 3))

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