Description Usage Arguments Details Value Note Author(s) References Examples
A collection and description of functions of one
sample tests for testing normality of financial
return series.
The functions for testing normality are:
ksnormTest  KolmogorovSmirnov normality test, 
shapiroTest  ShapiroWilk's test for normality, 
jarqueberaTest  JarqueBera test for normality, 
dagoTest  D'Agostino normality test. 
Functions for high precision Jarque Bera LM and ALM tests:
jbTest  Performs finite sample adjusted JB LM and ALM test. 
Additional functions for testing normality from the 'nortest' package:
adTest  AndersonDarling normality test, 
cvmTest  Cramervon Mises normality test, 
lillieTest  Lilliefors (KolmogorovSmirnov) normality test, 
pchiTest  Pearson chisquare normality test, 
sfTest  ShapiroFrancia normality test. 
For SPlus/Finmetrics Compatibility:
normalTest  test suite for some normality tests. 
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ksnormTest(x, title = NULL, description = NULL)
jbTest(x, title = NULL, description = NULL)
shapiroTest(x, title = NULL, description = NULL)
normalTest(x, method = c("sw", "jb"), na.rm = FALSE)
jarqueberaTest(x, title = NULL, description = NULL)
dagoTest(x, title = NULL, description = NULL)
adTest(x, title = NULL, description = NULL)
cvmTest(x, title = NULL, description = NULL)
lillieTest(x, title = NULL, description = NULL)
pchiTest(x, title = NULL, description = NULL)
sfTest(x, title = NULL, description = NULL)

description 
optional description string, or a vector of character strings. 
method 
[normalTest]  
na.rm 
[normalTest]  
title 
an optional title string, if not specified the inputs data name is deparsed. 
x 
a numeric vector of data values or a S4 object of class

The hypothesis tests may be of interest for many financial
and economic applications, especially for the investigation
of univariate time series returns.
Normal Tests:
Several tests for testing if the records from a data set are normally
distributed are available. The input to all these functions may be
just a vector x
or a univariate time series object x
of class timeSeries
.
First there exists a wrapper function which allows to call one from two normal tests either the Shapiro–Wilks test or the Jarque–Bera test. This wrapper was introduced for compatibility with SPlus' FinMetrics package.
Also available are the Kolmogorov–Smirnov one sample test and the D'Agostino normality test.
The remaining five normal tests are the Anderson–Darling test,
the Cramer–von Mises test, the Lilliefors (Kolmogorov–Smirnov)
test, the Pearson chi–square test, and the Shapiro–Francia test.
They are calling functions from R's contributed package nortest
.
The difference to the original test functions implemented in R and
from contributed R packages is that the Rmetrics functions accept
time series objects as input and give a more detailed output report.
The AndersonDarling test is used to test if a sample of data came
from a population with a specific distribution, here the normal
distribution. The adTest
goodnessoffit test can be
considered as a modification of the Kolmogorov–Smirnov test which
gives more weight to the tails than does the ksnormTest
.
In contrast to R's output report from S3 objects of class "htest"
a different output report is produced. The tests here return an S4
object of class "fHTEST"
. The object contains the following slots:
@call 
the function call. 
@data 
the data as specified by the input argument(s). 
@test 
a list whose elements contain the results from the statistical
test. The information provided is similar to a list object of
class 
@title 
a character string with the name of the test. This can be overwritten specifying a user defined input argument. 
@description 
a character string with an optional user defined description. By default just the current date when the test was applied will be returned. 
The slot @test
returns an object of class "list"
containing the following (otionally empty) elements:
statistic 
the value(s) of the test statistic. 
p.value 
the pvalue(s) of the test. 
parameters 
a numeric value or vector of parameters. 
estimate 
a numeric value or vector of sample estimates. 
conf.int 
a numeric two row vector or matrix of 95 
method 
a character string indicating what type of test was performed. 
data.name 
a character string giving the name(s) of the data. 
The meaning of the elements of the @test
slot is the following:
ksnormTest
returns the values for the 'D' statistic and pvalues for the three
alternatives 'twosided, 'less' and 'greater'.
shapiroTest
returns the values for the 'W' statistic and the pvalue.
jarqueberaTest
jbTest
returns the values for the 'Chisquared' statistic with 2 degrees of
freedom, and the asymptotic pvalue. jbTest
is the finite sample
version of the Jarque Bera Lagrange multiplier, LM, and adjusted
Lagrange multiplier test, ALM.
dagoTest
returns the values for the 'Chisquared', the 'Z3' (Skewness) and 'Z4'
(Kurtosis) statistic together with the corresponding p values.
adTest
returns the value for the 'A' statistic and the pvalue.
cvmTest
returns the value for the 'W' statistic and the pvalue.
lillieTest
returns the value for the 'D' statistic and the pvalue.
pchiTest
returns the value for the 'P' statistic and the pvalues for the
adjusted and not adjusted test cases. In addition the number of
classes is printed, taking the default value due to Moore (1986)
computed from the expression n.classes = ceiling(2 * (n^(2/5)))
,
where n
is the number of observations.
sfTest
returns the value for the 'W' statistic and the pvalue.
Some of the test implementations are selected from R's ctest
and nortest
packages.
Rcore team for the tests from R's ctest package,
Adrian Trapletti for the runs test from R's tseries package,
Juergen Gross for the normal tests from R's nortest package,
James Filliben for the Fortran program producing the runs report,
Diethelm Wuertz and Helmut Katzgraber for the finite sample JB tests,
Diethelm Wuertz for the Rmetrics Rport.
Earlier versions of theses functions were based on Fortran code of Paul Johnson.
Anderson T.W., Darling D.A. (1954); A Test of Goodness of Fit, JASA 49:765–69.
Conover, W. J. (1971); Practical nonparametric statistics, New York: John Wiley & Sons.
D'Agostino R.B., Pearson E.S. (1973); Tests for Departure from Normality, Biometrika 60, 613–22.
D'Agostino R.B., Rosman B. (1974); The Power of Geary's Test of Normality, Biometrika 61, 181–84.
Durbin J. (1961); Some Methods of Constructing Exact Tests, Biometrika 48, 41–55.
Durbin,J. (1973); Distribution Theory Based on the Sample Distribution Function, SIAM, Philadelphia.
Geary R.C. (1947); Testing for Normality; Biometrika 36, 68–97.
Lehmann E.L. (1986); Testing Statistical Hypotheses, John Wiley and Sons, New York.
Linnet K. (1988); Testing Normality of Transformed Data, Applied Statistics 32, 180–186.
Moore, D.S. (1986); Tests of the chisquared type, In: D'Agostino, R.B. and Stephens, M.A., eds., GoodnessofFit Techniques, Marcel Dekker, New York.
Shapiro S.S., Francia R.S. (1972); An Approximate Analysis of Variance Test for Normality, JASA 67, 215–216.
Shapiro S.S., Wilk M.B., Chen V. (1968); A Comparative Study of Various Tests for Normality, JASA 63, 1343–72.
Thode H.C. (2002); Testing for Normality, Marcel Dekker, New York.
Weiss M.S. (1978); Modification of the KolmogorovSmirnov Statistic for Use with Correlated Data, JASA 73, 872–75.
Wuertz D., Katzgraber H.G. (2005); Precise finitesample quantiles of the JarqueBera adjusted Lagrange multiplier test, ETHZ Preprint.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Series:
x = rnorm(100)
## ksnormTests 
# Kolmogorov  Smirnov OneSampel Test
ksnormTest(x)
## shapiroTest  ShapiroWilk Test
shapiroTest(x)
## jarqueberaTest 
# Jarque  Bera Test
# jarqueberaTest(x)
# jbTest(x)

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