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:
jbTable Table of finite sample p values for the JB test,
pjb Computes probabilities for the Jarque Bera Test,
qjb Computes quantiles for the Jarque Bera Test,
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. }
More tests ...
runsTest Runs test for detecting non-randomness.}
ksnormTest(x, title = NULL, description = NULL)
shapiroTest(x, title = NULL, description = NULL)
jarqueberaTest(x, title = NULL, description = NULL)
dagoTest(x, title = NULL, description = NULL)jbTable(type = c("LM", "ALM"), size = c("all", "small"))
pjb(q, N = Inf, type = c("LM", "ALM"))
qjb(p, N = Inf, type = c("LM", "ALM"))
jbTest(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)
normalTest(x, method = c("sw", "jb"), na.rm = FALSE)
runsTest(x)
"ks" for the Kolmogorov-Smirnov one--sample test,
"sw" for the Shapiro-Wilk test,
"jb" for the Jarque-Bera Test, and
FALSE."all" then all data are used from the table, if
set to "small" then only a small part of the data
will be returned."LM"
denotes the Lagrange multiplier test, and "ALM" the
adjusted Lagrange multiplier test.timeSeries."htest"
a different output report is produced. The tests here return an S4
object of class "fHTEST". The object contains the following slots:@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.
Runs Test:
The runs test can be used to decide if a data set is from a random
process. A run is defined as a series of increasing values or a
series of decreasing values. The number of increasing, or decreasing,
values is the length of the run. In a random data set, the probability
that the (i+1)-th value is larger or smaller than the i-th
value follows a binomial distribution, which forms the basis of the
runs test.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.
## SOURCE("fBasics.5B-OneSampleTests")
## Series:
x = rnorm(100)
## ksnormTests -
# Kolmogorov - Smirnov One-Sampel Test
ksnormTest(x)
## shapiroTest - Shapiro-Wilk Test
shapiroTest(x)
## jarqueberaTest -
## jbTest - Jarque-Bera Test
jarqueberaTest(x)
jbTest(x)
## runsTest -
runsTest(x)Run the code above in your browser using DataLab