ksnormTest Kolmogorov-Smirnov normality test,
shapiroTest Shapiro-Wilk's test for normality,
jarqueberaTest Jarque--Bera 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 Anderson--Darling normality test,
cvmTest Cramer--von Mises normality test,
lillieTest Lilliefors (Kolmogorov-Smirnov) normality test,
pchiTest Pearson chi--square normality test,
sfTest Shapiro--Francia normality test. }
For SPlus/Finmetrics Compatibility:
normalTest test suite for some normality tests. }
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)
"ks" for the Kolmogorov-Smirnov one--sample test,
"sw" for the Shapiro-Wilk test,
"jb" for the Jarque-Bera Test, and
FALSE.timeSeries."htest"
a different output report is produced. The tests here return an S4
object of class "fHTEST". The object contains the following slots:"htest".@test returns an object of class "list"
containing the following (otionally empty) elements:@test slot is the following:
ksnormTest
returns the values for the 'D' statistic and p-values for the three
alternatives 'two-sided, 'less' and 'greater'.
shapiroTest
returns the values for the 'W' statistic and the p-value.
jarqueberaTest
jbTest
returns the values for the 'Chi-squared' statistic with 2 degrees of
freedom, and the asymptotic p-value. 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 'Chi-squared', the 'Z3' (Skewness) and 'Z4'
(Kurtosis) statistic together with the corresponding p values.
adTest
returns the value for the 'A' statistic and the p-value.
cvmTest
returns the value for the 'W' statistic and the p-value.
lillieTest
returns the value for the 'D' statistic and the p-value.
pchiTest
returns the value for the 'P' statistic and the p-values 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 p-value.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 S-Plus'
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 Anderson-Darling test is used to test if a sample of data came
from a population with a specific distribution, here the normal
distribution. The adTest goodness-of-fit test can be
considered as a modification of the Kolmogorov--Smirnov test which
gives more weight to the tails than does the ksnormTest.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 chi-squared type, In: D'Agostino, R.B. and Stephens, M.A., eds., Goodness-of-Fit 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 Kolmogorov-Smirnov Statistic for Use with Correlated Data, JASA 73, 872--75. Wuertz D., Katzgraber H.G. (2005); Precise finite-sample quantiles of the Jarque-Bera adjusted Lagrange multiplier test, ETHZ Preprint.
## Series:
x = rnorm(100)
## ksnormTests -
# Kolmogorov - Smirnov One-Sampel Test
ksnormTest(x)
## shapiroTest - Shapiro-Wilk Test
shapiroTest(x)
## jarqueberaTest -
# Jarque - Bera Test
# jarqueberaTest(x)
# jbTest(x)Run the code above in your browser using DataLab