Description Usage Arguments Details Value Author(s) References See Also Examples
The function computes (and by default plots) different types of serial dependence diagrams.
1 2 3 4 5 6 7 8 | ADF(x, dtype = c("ADF", "CADF", "RPADF", "DeltaADF", "ACF"),
lag.max = floor(10 * log10(length(x))), alpha = 0.05,
num.clas, B = 99, bandwidth, delta = "Delta_1", fres = ".Perm",
fdenest = ".denest", fdiv, argacf, R = 1:lag.max,
p.adjust.method = p.adjust.methods, plot = TRUE,
...)
## S3 method for class 'SDD'
print(x, digits=3, ...)
|
x |
an |
dtype |
an optional character string. It specifies the type of autodependence function and must be:
|
lag.max |
maximum lag at which to calculate the ADF. Default is |
alpha |
significance level of the tests of lag-independence (related to each bar). Default value is 0.05. |
num.clas |
when |
B |
when |
bandwidth |
when |
delta |
a character vector; when
|
fres |
an optional character string which specifies, when |
fdenest |
an optional character string which specifies when
If |
fdiv |
an optional character string which specifies, when |
plot |
if |
argacf |
when |
R |
a vector. It specifies the lags on which to test for simultaneous independence (see Bagnato, Punzo, 2010, 2012 and Bagnato, De Capitani, Punzo, 2013b). Default value is |
p.adjust.method |
a character string. It specifies the method to be used in the simultaneous independence test. It must be one of |
... |
optional arguments to be passed to the |
digits |
minimal number of significant digits. |
There are print and data.frame methods for objects of class "ADF".
Returned from this function is a SDD
object which is a list with the following components:
res |
a data frame. According to
|
dtype |
a character string. It specifies the type of serial dependence diagram generated. |
delta |
a character string. It specifies, when |
num.clas |
a scalar. It is the number of classes in each contingency table. |
alpha |
a scalar. It specifies the significance level of the tests of lag independence (related to each bar). |
df |
a scalar. It contains the degrees of freedom of the reference chi-square distribution used when |
bandwidth |
a scalar. It is the bandwidth used in kernel density estimation. |
series |
the name of the series |
R |
a vector. It specifies the lags to test in the simultaneous independence tests. |
p.adjust.method |
a character string. It specifies the method to be used in the simultaneous independence tests. It must be one of |
p.adjust |
a vector. It contains the adjusted probabilities for the simultaneous independence tests. |
Luca Bagnato, Lucio De Capitani, Angelo Mazza and Antonio Punzo
Bagnato L, De Capitani L, Mazza A, Punzo A (2015). SDD: An R Package for Serial Dependence Diagrams. Journal of Statistical Software, 64(2), 1-19. URL: http://www.jstatsoft.org/v64/c02/
Bagnato L, De Capitani L, Punzo A (2013a). Improving the autodependogram using the Kulback-Leibler divergence. arXiv:1306.5006 [stat.ME], URL: http://arxiv.org/pdf/1306.5006v1.pdf
Bagnato L, De Capitani L, Punzo A (2013b). Testing Serial Independence via Density-Based Measures of Divergence. Methodology and Computing in Applied Probability, 16(3), 627-641.
Bagnato L, De Capitani L, Punzo A (2014). Detecting Serial Dependencies with the Reproducibility Probability Autodependogram. Advances in Statistical Analysis, 98(1), 35-61.
Bagnato L, Punzo A (2010). On the Use of χ^2 Test to Check Serial Independence. Statistica & Applicazioni, VIII(1), 57-74.
Bagnato L, Punzo A (2012). Checking Serial Independence of Residuals from a Nonlinear Model. In W Gaul, A Geyer-Shulz, L Schmidt-Thieme, J Kunze (eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization, volume XIV of Studies in Classification, Data Analysis and Knowledge Organization, pp. 203-211. Springer-Verlag, Berlin Heidelberg.
Bagnato L, Punzo A, Nicolis O (2012). The autodependogram: a graphical device to investigate serial dependencies. Journal of Time Series Analysis, 33(2), 233-254.
Bagnato L, Punzo A (2013). Using the Autodependogram in Model Diagnostic Checking. In N Torelli, F Pesarin, A Bar-Hen (eds.), Advances in Theoretical and Applied Statistics, volume XIX of Studies in Theoretical and Applied Statistics, pp. 129-139. Springer-Verlag, Berlin Heidelberg.
SDD-package
, plot.SDD
, SMI
, acf
1 2 3 4 5 6 7 8 9 10 11 12 13 | # Dependence Diagrams on raw data
data("SMI")
ADF(SMI^2, dtype="ACF", main="")
ADF(SMI, main="")
ADF(SMI, dtype="RPADF", main="")
# Dependence Diagrams on residuals from a fitted model
library("tseries")
residuals <- garch(SMI, order=c(1,1))$residuals[-1]
ADF(residuals^2, dtype="ACF", main="")
ADF(residuals, dtype="RPADF", main="")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.