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Package nortest

Package 'nortest'. July 30 2015. Title Tests for Normality. Version 1.0-4 Date/Publication 2015-07-30 00:14:57. R topics documented: ad.test .



Package nortest

30 mai 2012 Package 'nortest'. February 15 2013. Title Tests for Normality. Version 1.0-2. Date 2012-05-30 ... R topics documented: ad.test .



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3 sept. 2020 Les conditions d'activation de la zone LF-R 213 NORD-EST A seront conformes à l'AIP France ENR 5.1.5.1 – « Zones provisoirement inactives » ...



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14 mai 2008 R apports mensuels. Sensibilisation en milieu scolaire ?. Lycée Jeressar district de Soroti. 14/05/08. Soroti



ASIE DU NORD-EST

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les populations (SOREP). Ces travaux portent sur le Nord-Est de la province de. Qu?bec (Canada) et plus particuli?rement sur la r?gion du Saguenay.



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Stat 427/527: Advanced Data Analysis I - University of New Mexico

R has several tests of normality I Shapiro-Wilk test shapiro test() is a base function I The R package nortest has some others: -the Anderson-Darling test ad test() is useful related to the Kolmogorov-Smirnov -the Cramer-von Mises test cvm test() Extreme outliers and skewness have the biggest e ects on standard methods based on normality



matrixTests: Fast Statistical Hypothesis Tests on Rows and

Results should be the same as running nortest::ad test(x) on every row (or column) of x Value a data frame where each row contains the results of Anderson-Darling test performed on the corre-sponding row/column of x Each row contains the following information (in order): 1 obs - number of observations 2 statistic - test statistic 3 pvalue



Testing experimental data for univariate normality - New Mexico

(Insightful Corporation) and R version 2 1 1 [9] with their associated libraries including the car library [10] in S-Plus and the fBasics and nortest packages in R The majority of the programs used in this review are available in these R packages; all others are listed later It should be noted that R



Searches related to nortest r filetype:pdf

the nortest R package [26] and give optimum Quantile-Quantile (Q-Q) plots of the meta-analysis z-score re-sults This was done to reduce how arbitrary the selected filtering parameters are After

What does nor-test stand for?

    Research Council of Norway. Research Council of Norway. Tenecteplase versus alteplase for management of acute ischaemic stroke (NOR-TEST): a phase 3, randomised, open-label, blinded endpoint trial Lancet Neurol. 2017 Oct;16(10):781-788.doi: 10.1016/S1474-4422(17)30253-3. Epub 2017 Aug 2. Authors

What is nor-test-2?

    Study design: NOR-TEST-2 is a multi-centre PROBE (prospective randomised, open-label, blinded endpoint) trial, designed to establish non-inferiority of tenecteplase as compared with alteplase for consecutively admitted patients with acute ischaemic stroke treated within 4½ hours after symptom onset. Randomisation tenecteplase:alteplase is 1:1.

What is Nortec?

    Nortec originated in 1999 by experimenting with samples of old banda sinaloense and norteño albums and altering them on computer or filtering them with analog synthesizers.

What is Norte?

    Norte is an invite-only tech investment club founded and backed exclusively by entrepreneurs.

Package 'nortest"

October 13, 2022

TitleTests for Normality

Version1.0-4

Date2015-07-29

DescriptionFive omnibus tests for testing the composite hypothesis of normality.

LicenseGPL (>= 2)

Importsstats

AuthorJuergen Gross [aut],

Uwe Ligges [aut, cre]

MaintainerUwe Ligges

NeedsCompilationno

RepositoryCRAN

Date/Publication2015-07-30 00:14:57

Rtopics documented:

ad.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 cvm.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 lillie.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 pearson.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 sf.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Index9ad.testAnderson-Darling test for normalityDescription Performs the Anderson-Darling test for the composite hypothesis of normality, see e.g. Thode (2002, Sec. 5.1.4). 1

2ad.test

Usage ad.test(x)

Arguments

xa numeric vector of data values, the number of which must be greater than 7.

Missing values are allowed.

Details

The Anderson-Darling test is an EDF omnibus test for the composite hypothesis of normality. The test statistic is A=n1n n X i=1[2i1][ln(p(i)) + ln(1p(ni+1))]; wherep(i)= ([x(i)x]=s). Here,is the cumulative distribution function of the standard normal distribution, andxandsare mean and standard deviation of the data values. The p-value is computed from the modified statisticZ=A(1:0 + 0:75=n+ 2:25=n2)\ according to Table 4.9 in

Stephens (1986).

Value A list with class "htest" containing the following components: statisticthe value of the Anderson-Darling statistic. p.valuethe p-value for the test. methodthe character string "Anderson-Darling normality test". data.namea character string giving the name(s) of the data. Note The Anderson-Darling test is the recommended EDF test by Stephens (1986). Compared to the Cramer-von Mises test (as second choice) it gives more weight to the tails of the distribution.

Author(s)

Juergen Gross

References

Stephens, M.A. (1986): Tests based on EDF statistics. In: D"Agostino, R.B. and Stephens, M.A., eds.: Goodness-of-Fit Techniques. Marcel Dekker, New York. Thode Jr., H.C. (2002): Testing for Normality. Marcel Dekker, New York.

See Also

sf.testfor performing further tests for normality.qqnormfor producing a normal quantile- quantile plot. cvm.test3

Examples

ad.test(rnorm(100, mean = 5, sd = 3)) ad.test(runif(100, min = 2, max = 4))cvm.testCramer-von Mises test for normalityDescription Performs the Cramer-von Mises test for the composite hypothesis of normality, see e.g. Thode (2002, Sec. 5.1.3). Usage cvm.test(x)

Arguments

xa numeric vector of data values, the number of which must be greater than 7.

Missing values are allowed.

Details

The Cramer-von Mises test is an EDF omnibus test for the composite hypothesis of normality. The test statistic is

W=112n+nX

i=1 p (i)2i12n 2 wherep(i)= ([x(i)x]=s). Here,is the cumulative distribution function of the standard normal distribution, andxandsare mean and standard deviation of the data values. The p-value is computed from the modified statisticZ=W(1:0 + 0:5=n)according to Table 4.9 in Stephens (1986). Value A list with class "htest" containing the following components: statisticthe value of the Cramer-von Mises statistic. p.valuethe p-value for the test. methodthe character string "Cramer-von Mises normality test". data.namea character string giving the name(s) of the data.

Author(s)

Juergen Gross

4lillie.test

References

Stephens, M.A. (1986): Tests based on EDF statistics. In: D"Agostino, R.B. and Stephens, M.A., eds.: Goodness-of-Fit Techniques. Marcel Dekker, New York. Thode Jr., H.C. (2002): Testing for Normality. Marcel Dekker, New York.

See Also

sf.testfor performing further tests for normality.qqnormfor producing a normal quantile- quantile plot.

Examples

cvm.test(rnorm(100, mean = 5, sd = 3))

cvm.test(runif(100, min = 2, max = 4))lillie.testLilliefors (Kolmogorov-Smirnov) test for normalityDescription

Performs the Lilliefors (Kolmogorov-Smirnov) test for the composite hypothesis of normality, see e.g. Thode (2002, Sec. 5.1.1). Usage lillie.test(x)

Arguments

xa numeric vector of data values, the number of which must be greater than 4.

Missing values are allowed.

Details

The Lilliefors (Kolmogorov-Smirnov) test is an EDF omnibus test for the composite hypothesis of normality. The test statistic is the maximal absolute difference between empirical and hypothetical cumulative distribution function. It may be computed asD= maxfD+;Dgwith D += maxi=1;:::;nfi=np(i)g;D= maxi=1;:::;nfp(i)(i1)=ng; wherep(i)= ([x(i)x]=s). Here,is the cumulative distribution function of the standard normal distribution, andxandsare mean and standard deviation of the data values. The p-value is computed from the Dallal-Wilkinson (1986) formula, which is claimed to be only reliable when the

p-value is smaller than 0.1. If the Dallal-Wilkinson p-value turns out to be greater than 0.1, then the

p-value is computed from the distribution of the modified statisticZ=D(pn0:01+0:85=pn), see Stephens (1974), the actual p-value formula being obtained by a simulation and approximation process. lillie.test5 Value A list with class "htest" containing the following components: statisticthe value of the Lilliefors (Kolomogorv-Smirnov) statistic. p.valuethe p-value for the test. methodthe character string "Lilliefors (Kolmogorov-Smirnov) normality test". data.namea character string giving the name(s) of the data. Note The Lilliefors (Kolomorov-Smirnov) test is the most famous EDF omnibus test for normality. Compared to the Anderson-Darling test and the Cramer-von Mises test it is known to perform worse. Although the test statistic obtained fromlillie.test(x)is the same as that obtained from ks.test(x, "pnorm", mean(x), sd(x)), it is not correct to use the p-value from the latter for the composite hypothesis of normality (mean and variance unknown), since the distribution of the test statistic is different when the parameters are estimated. The function calllillie.test(x)essentially produces the same result as the S-PLUS function callks.gof(x)with the distinction that the p-value is not set to 0.5 when the Dallal-Wilkinson approximation yields a p-value greater than 0.1. (Actually, the alternative p-value approximation is provided for the complete range of test statistic values, but is only used when the Dallal-Wilkinson approximation fails.)

Author(s)

Juergen Gross

References

Dallal, G.E. and Wilkinson, L. (1986): An analytic approximation to the distribution of Lilliefors" test for normality. The American Statistician, 40, 294-296. Stephens, M.A. (1974): EDF statistics for goodness of fit and some comparisons. Journal of the

American Statistical Association, 69, 730-737.

Thode Jr., H.C. (2002): Testing for Normality. Marcel Dekker, New York.

See Also

sf.testfor performing further tests for normality.qqnormfor producing a normal quantile- quantile plot.

Examples

lillie.test(rnorm(100, mean = 5, sd = 3)) lillie.test(runif(100, min = 2, max = 4))

6pearson.testpearson.testPearson chi-square test for normalityDescription

Performs the Pearson chi-square test for the composite hypothesis of normality, see e.g. Thode (2002, Sec. 5.2). Usage pearson.test(x, n.classes = ceiling(2 * (n^(2/5))), adjust = TRUE)

Arguments

xa numeric vector of data values. Missing values are allowed. n.classesThe number of classes. The default is due to Moore (1986). adjustlogical; ifTRUE(default), the p-value is computed from a chi-square distribution withn.classes-3 degrees of freedom, otherwise from a chi-square distribution withn.classes-1 degrees of freedom.

Details

The Pearson test statistic isP=P(CiEi)2=Ei, whereCiis the number of counted andEi is the number of expected observations (under the hypothesis) in classi. The classes are build is such a way that they are equiprobable under the hypothesis of normality. The p-value is computed from a chi-square distribution withn.classes-3 degrees of freedom ifadjustisTRUEand from a chi-square distribution withn.classes-1 degrees of freedom otherwise. In both cases this is not (!) the correct p-value, lying somewhere between the two, see also Moore (1986). Value A list with class "htest" containing the following components: statisticthe value of the Pearson chi-square statistic. p.valuethe p-value for the test. methodthe character string "Pearson chi-square normality test". data.namea character string giving the name(s) of the data. n.classesthe number of classes used for the test. dfthe degress of freedom of the chi-square distribution used to compute the p- value. sf.test7 Note The Pearson chi-square test is usually not recommended for testing the composite hypothesis of normality due to its inferior power properties compared to other tests. It is common practice to compute the p-value from the chi-square distribution withn.classes- 3 degrees of freedom, in order to adjust for the additional estimation of two parameters. (For the simple hypothesis of nor- mality (mean and variance known) the test statistic is asymptotically chi-square distributed with n.classes- 1 degrees of freedom.) This is, however, not correct as long as the parameters are estimated bymean(x)andvar(x)(orsd(x)), as it is usually done, see Moore (1986) for details. Since the true p-value is somewhere between the two, it is suggested to runpearson.testtwice, withadjust = TRUE(default) and withadjust = FALSE. It is also suggested to slightly change the

default number of classes, in order to see the effect on the p-value. Eventually, it is suggested not

to rely upon the result of the test. The function callpearson.test(x)essentially produces the same result as the S-PLUS function callchisq.gof((x-mean(x))/sqrt(var(x)), n.param.est=2).

Author(s)

Juergen Gross

References

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. Thode Jr., H.C. (2002): Testing for Normality. Marcel Dekker, New York.

See Also

sf.testfor performing further tests for normality.qqnormfor producing a normal quantile- quantile plot.

Examples

pearson.test(rnorm(100, mean = 5, sd = 3)) pearson.test(runif(100, min = 2, max = 4))sf.testShapiro-Francia test for normalityDescription Performs the Shapiro-Francia test for the composite hypothesis of normality, see e.g. Thode (2002,

Sec. 2.3.2).

Usage sf.test(x)

8sf.test

Arguments

xa numeric vector of data values, the number of which must be between 5 and

5000. Missing values are allowed.

Details

The test statistic of the Shapiro-Francia test is simply the squared correlation between the ordered sample values and the (approximated) expected ordered quantiles from the standard normal distri- bution. The p-value is computed from the formula given by Royston (1993). Value A list with class "htest" containing the following components: statisticthe value of the Shapiro-Francia statistic. p.valuethe p-value for the test. methodthe character string "Shapiro-Francia normality test". data.namea character string giving the name(s) of the data. Note The Shapiro-Francia test is known to perform well, see also the comments by Royston (1993). The a = 3/8)), being slightly different from the approximationqnorm(ppoints(x, a = 1/2))used for the normal quantile-quantile plot byqqnormfor sample sizes greater than 10.

Author(s)

Juergen Gross

References

Royston, P. (1993): A pocket-calculator algorithm for the Shapiro-Francia test for non-normality: an application to medicine. Statistics in Medicine, 12, 181-184. Thode Jr., H.C. (2002): Testing for Normality. Marcel Dekker, New York.

See Also

pearson.testfor performing further tests for normality.qqnormfor producing a normal quantile- quantile plot.

Examples

sf.test(rnorm(100, mean = 5, sd = 3)) sf.test(runif(100, min = 2, max = 4)) Index htest ad.test,1 cvm.test,3 lillie.test,4 pearson.test,6 sf.test,7 ad.test,1 ,4,5 ,7,8 cvm.test,2,3 ,5,7,8 lillie.test,2,4,4 ,7,8 pearson.test,2,4,5 ,6 ,8 qqnorm,2,4,5 ,7,8 sf.test,2,4,5 ,7,7 shapiro.test,2,4,5 ,7,8 9quotesdbs_dbs17.pdfusesText_23
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