R help - May 2010 - Get output values in a table

Hi,

I use two similar scripts to put p-values of shapiro.test and, respectively,
of wilcox.test in a table:

a)
d <- data.frame(dataset$GroupFactor, dataset[2:11])
# p-values for the shapiro test (by levels of GroupFactor)
with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
shapiro.test(x)$p.value))

b)
d <- data.frame(dataset$GroupFactor, dataset[2:11])
with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
wilcox.test(x)$p.value))

How to replace the p-values with values of "W" (for Shapiro-Wilk test
and,
respectively, for Wilcoxon-Mann-Whitney U test)?

Example of output:
---------------------------
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
shapiro.test(x)$p.value))
  Group.1            1          2        3           4            5
6         7         8      IA        IV
1   town 0.0030465882 0.002068448 0.01129771 7.606061e-06 1.189380e-03
0.001396341 0.009097276 6.824918e-05 0.4877525 0.08005584
2   country 0.0002436141 0.005135004 0.01540923 2.469611e-06 1.372854e-05
0.001517259 0.052174738 1.057227e-06 0.3172002 0.15745973


Regards,
Iurie Malai
Department of Psychology
Ion Creanga Moldova Pedagogical State University - www.upsm.md
http://en.wikipedia.org/wiki/Ion_Creang%C4%83_Pedagogical_State_University
-------------- next part --------------
"GroupFactor",1,2,3,4,5,6,7,8,"IA","IV"
"town",7,5,4,5,5,8,6,8,5.30,7.00
"town",3,5,5,3,2,6,9,5,4.30,7.50
"town",7,5,8,0,3,10,9,5,6.60,9.50
"town",5,6,7,3,6,6,8,5,6.00,7.00
"town",6,7,7,5,5,7,8,8,6.60,7.50
"town",4,7,3,5,3,8,8,5,4.60,8.00
"town",5,5,7,4,5,10,8,8,5.60,9.00
"town",8,5,8,4,5,8,9,5,7.00,8.50
"town",9,7,8,4,5,5,8,3,8.00,6.50
"town",6,4,3,2,4,7,4,8,4.30,5.50
"town",3,5,4,2,6,5,9,7,4.00,7.00
"town",6,6,4,2,4,5,3,8,5.30,4.00
"town",6,6,8,4,6,8,9,7,6.60,8.50
"town",7,8,6,2,4,5,5,6,7.00,5.00
"town",2,6,5,4,6,10,7,9,4.30,8.50
"town",4,3,3,0,3,9,4,7,3.30,6.50
"town",7,6,5,3,3,6,8,4,6.00,7.00
"town",7,6,5,3,6,9,8,7,6.00,8.50
"town",7,5,9,4,4,5,3,4,7.00,4.00
"town",4,4,5,4,4,2,6,7,4.30,4.00
"town",5,3,8,2,4,10,11,7,5.30,10.50
"town",4,5,2,1,3,7,5,5,3.60,6.00
"town",5,6,7,2,5,7,5,7,6.00,6.00
"town",8,3,7,4,8,8,6,7,6.00,7.00
"town",4,2,2,4,2,4,6,6,2.60,5.00
"town",6,6,6,2,5,5,9,8,6.00,7.00
"town",6,6,8,3,7,8,10,6,6.60,9.00
"town",6,4,8,4,5,6,8,8,6.00,7.00
"town",2,2,4,3,3,5,6,2,2.60,5.50
"town",2,4,6,2,3,5,7,7,4.00,6.00
"town",2,4,2,2,2,5,5,7,2.60,5.00
"town",8,5,9,3,2,8,10,2,7.30,9.00
"town",7,6,5,3,3,8,9,4,6.00,8.50
"town",8,8,8,4,7,8,6,8,8.00,7.00
"town",4,3,8,5,7,5,8,3,5.00,6.50
"town",8,8,9,5,6,10,11,8,8.30,10.50
"town",3,3,4,0,1,5,5,4,3.30,5.00
"town",3,4,4,2,4,8,7,5,3.60,7.50
"town",5,5,5,5,4,7,5,2,5.00,6.00
"town",6,7,6,3,5,8,8,8,6.30,8.00
"town",5,3,5,4,5,7,4,8,4.30,5.50
"town",2,2,1,3,2,2,1,3,1.60,1.50
"town",5,6,7,1,5,6,3,4,6.00,4.50
"town",7,3,3,2,4,1,1,4,4.30,1.00
"town",6,1,8,1,6,6,7,5,5.00,6.50
"town",7,5,6,2,7,5,7,4,6.00,6.00
"town",4,6,9,2,4,4,6,5,6.30,5.00
"town",7,5,2,4,4,6,6,7,4.60,6.00
"town",5,3,8,3,5,5,4,2,5.30,4.50
"town",5,4,7,5,4,3,6,1,5.30,4.50
"town",5,6,4,1,6,8,9,7,5.00,8.50
"town",8,7,3,4,7,6,7,8,6.00,6.50
"town",7,4,4,5,4,6,7,4,5.00,6.50
"town",5,5,2,3,4,6,5,6,4.00,5.50
"town",4,1,3,2,2,6,4,8,2.60,5.00
"town",10,6,7,3,7,6,7,8,7.60,6.50
"town",6,3,3,3,4,8,8,4,4.00,8.00
"town",2,6,9,5,6,3,4,8,5.60,3.50
"town",8,4,5,2,4,6,9,4,5.60,7.50
"town",7,3,6,2,4,5,8,5,5.30,6.50
"town",7,5,6,4,4,5,6,1,6.00,5.50
"town",5,2,8,3,6,7,10,8,5.00,8.50
"town",4,5,4,3,5,8,8,7,4.30,8.00
"town",2,2,4,1,6,7,8,8,2.60,7.50
"town",4,4,7,5,2,6,6,6,5.00,6.00
"town",9,8,10,2,7,7,7,8,9.00,7.00
"town",5,6,4,4,2,8,5,5,5.00,6.50
"town",5,5,5,3,1,7,7,6,5.00,7.00
"town",7,3,5,4,6,9,8,7,5.00,8.50
"town",6,5,1,3,5,1,7,9,4.00,4.00
"town",5,5,5,2,1,7,6,6,5.00,6.50
"town",7,2,5,4,5,6,8,6,4.60,7.00
"town",8,8,7,5,4,7,11,6,7.60,9.00
"town",6,5,5,3,1,6,9,5,5.30,7.50
"town",5,6,6,5,4,4,8,2,5.60,6.00
"town",7,5,9,5,7,5,6,9,7.00,5.50
"town",5,7,3,2,3,7,8,8,5.00,7.50
"town",8,8,7,4,7,10,9,8,7.60,9.50
"town",9,6,7,5,7,7,9,6,7.30,8.00
"town",5,4,5,4,4,7,8,8,4.60,7.50
"town",6,3,5,5,7,5,5,5,4.60,5.00
"town",7,6,5,4,3,7,7,4,6.00,7.00
"town",8,6,6,5,1,3,7,7,6.60,5.00
"town",3,3,6,1,6,7,10,8,4.00,8.50
"town",6,4,8,2,5,7,7,6,6.00,7.00
"town",7,8,4,2,7,5,8,4,6.30,6.50
"town",8,7,7,5,7,8,7,9,7.30,7.50
"town",5,4,3,1,4,8,10,6,4.00,9.00
"town",4,4,2,4,4,5,6,5,3.30,5.50
"town",8,5,10,3,7,8,9,8,7.60,8.50
"town",6,1,2,4,3,3,4,5,3.00,3.50
"town",7,5,7,5,2,6,10,6,6.30,8.00
"town",3,5,4,1,3,6,6,5,4.00,6.00
"town",6,5,7,2,6,8,9,5,6.00,8.50
"town",6,5,7,2,4,6,7,7,6.00,6.50
"town",7,4,7,2,6,2,5,5,6.00,3.50
"town",8,3,7,4,4,5,7,7,6.00,6.00
"town",4,8,5,5,4,6,6,5,5.60,6.00
"town",9,8,10,4,8,9,10,7,9.00,9.50
"town",7,6,7,5,6,5,7,5,6.60,6.00
"country",6,8,6,4,7,8,8,9,6.60,8.00
"country",4,5,7,3,4,7,8,8,5.30,7.50
"country",7,5,7,3,6,7,9,4,6.30,8.00
"country",8,6,5,4,5,7,12,8,6.30,9.50
"country",2,4,3,3,4,6,6,9,3.00,6.00
"country",5,8,6,4,8,9,8,9,6.30,8.50
"country",7,6,9,4,7,9,10,7,7.30,9.50
"country",6,4,4,1,6,7,4,7,4.60,5.50
"country",5,3,1,2,8,4,5,7,3.00,4.50
"country",6,6,9,1,3,6,9,4,7.00,7.50
"country",6,3,7,4,7,10,10,9,5.30,10.00
"country",3,4,7,3,6,8,10,7,4.60,9.00
"country",6,3,3,4,6,7,11,8,4.00,9.00
"country",7,5,5,4,6,6,9,6,5.60,7.50
"country",7,4,4,1,6,8,7,6,5.00,7.50
"country",8,4,6,3,5,7,7,7,6.00,7.00
"country",7,4,4,1,4,9,9,6,5.00,9.00
"country",7,4,7,4,5,6,5,7,6.00,5.50
"country",6,3,7,5,7,6,5,9,5.30,5.50
"country",7,6,6,5,5,7,8,6,6.30,7.50
"country",5,1,6,1,6,7,2,7,4.00,4.50
"country",4,5,6,4,6,6,5,7,5.00,5.50
"country",3,3,5,2,4,6,6,5,3.60,6.00
"country",8,5,5,1,6,9,8,5,6.00,8.50
"country",4,3,4,1,1,4,7,6,3.60,5.50
"country",5,5,6,1,7,5,8,9,5.30,6.50
"country",6,5,3,3,7,7,8,7,4.60,7.50
"country",5,6,8,1,5,7,5,9,6.30,6.00
"country",3,8,5,4,7,4,6,7,5.30,5.00
"country",8,7,8,4,7,10,11,9,7.60,10.50
"country",5,3,3,4,4,7,9,5,3.60,8.00
"country",3,5,4,1,4,7,7,8,4.00,7.00
"country",7,1,6,2,3,6,4,6,4.60,5.00
"country",7,5,7,3,3,7,8,6,4.60,7.50
"country",3,2,6,1,4,11,4,5,3.60,7.50
"country",6,2,6,1,5,7,6,7,4.60,6.50
"country",4,4,4,0,5,10,6,5,4.00,8.00
"country",6,6,9,2,3,6,9,4,7.00,7.50
"country",3,2,4,3,7,9,9,5,3.00,9.00
"country",4,7,5,4,7,5,9,8,5.30,7.00
"country",6,5,6,4,5,8,9,7,5.60,8.50
"country",7,7,4,3,4,7,6,6,4.60,6.50
"country",6,4,5,2,4,6,6,9,5.00,6.00
"country",5,5,6,4,5,8,7,9,5.30,7.50
"country",9,3,4,2,6,9,7,8,5.30,8.00
"country",6,7,4,4,3,7,7,6,5.60,7.00
"country",7,4,7,2,8,7,7,9,6.00,7.00
"country",7,3,3,1,5,8,8,8,4.30,8.00
"country",2,6,5,4,7,10,8,9,4.30,9.00
"country",6,5,6,3,7,8,5,8,5.60,6.50
"country",3,4,1,0,5,6,5,9,2.60,5.50
"country",6,7,7,3,7,7,10,7,6.60,8.50
"country",2,2,3,3,3,6,4,5,2.30,5.00
"country",5,4,4,2,5,7,7,7,4.30,7.00
"country",7,4,4,2,5,6,8,7,5.00,7.00
"country",3,6,5,1,7,5,7,7,4.60,7.00
"country",6,3,3,4,6,6,7,8,4.00,7.50
"country",6,3,6,4,6,7,6,9,5.00,6.50
"country",6,7,5,2,5,7,9,7,6.00,8.00
"country",7,6,8,3,7,9,9,7,7.00,9.00
"country",7,4,5,2,7,6,5,7,5.30,5.50
"country",8,6,6,4,3,9,8,7,6.60,8.50
"country",6,4,4,4,3,4,6,6,4.60,5.00
"country",6,4,6,4,4,8,6,9,5.30,7.00
"country",5,9,7,5,7,7,10,9,7.00,8.50
"country",7,7,5,4,1,3,5,4,6.30,4.00
"country",6,2,5,3,1,6,7,5,4.30,6.50
"country",4,3,7,2,6,7,3,8,4.60,5.00
"country",6,6,7,1,7,8,8,6,6.30,8.00
"country",8,7,7,4,7,10,10,9,7.30,10.00
"country",5,3,5,1,6,7,10,5,4.30,8.50
"country",5,4,6,0,7,5,7,7,5.00,6.00
"country",5,2,4,2,4,10,5,8,3.60,7.50
"country",5,6,6,4,7,9,11,9,4.30,6.50
"country",4,5,7,4,6,8,7,9,5.60,10.00
"country",6,6,5,3,7,7,8,8,5.30,7.50
"country",3,3,1,2,5,9,5,9,5.60,8.00
"country",3,3,7,2,7,6,7,9,2.30,7.00
"country",4,7,6,2,4,5,6,8,5.60,5.50
"country",6,4,5,4,5,7,6,6,5.00,6.50
"country",6,7,5,2,7,6,5,8,6.00,5.50
"country",5,4,3,3,5,6,5,7,4.00,5.50
"country",6,6,8,3,6,8,8,9,6.60,8.00
"country",2,4,6,3,7,9,9,9,4.00,9.00
"country",9,4,10,3,5,7,8,9,7.60,7.50
"country",8,5,6,4,5,9,10,9,6.30,9.50
"country",3,5,8,2,7,6,10,8,5.30,8.00
"country",4,3,4,2,8,7,6,5,3.60,6.50
"country",6,5,6,3,3,7,8,7,5.60,7.50
"country",2,5,6,3,6,10,9,9,4.30,9.50
"country",3,3,5,1,6,6,8,8,3.60,7.00
"country",6,3,3,1,5,4,4,4,4.00,4.00
"country",6,5,2,4,3,7,6,8,4.30,6.50
"country",3,2,5,3,3,4,4,5,3.30,4.00
"country",6,2,3,1,5,6,5,6,3.60,5.50
"country",6,4,4,1,6,8,6,8,4.60,7.00
"country",6,2,9,5,6,7,7,7,5.60,7.00
"country",4,4,5,1,6,5,6,9,4.30,5.50
"country",8,6,7,4,6,7,6,5,7.00,6.50
"country",8,3,5,3,3,8,8,7,5.30,8.00

Iurie -
    The help file for both functions has a "Values" section
that describes in detail exactly what the function returns.
In both cases, you'll see there is a component named
"statistic", which is what you want.

 					- Phil Spector
 					 Statistical Computing Facility
 					 Department of Statistics
 					 UC Berkeley
 					 spector at stat.berkeley.edu



On Tue, 25 May 2010, Iurie Malai wrote:
> Hi,
>
> I use two similar scripts to put p-values of shapiro.test and,
respectively,
> of wilcox.test in a table:
>
> a)
> d <- data.frame(dataset$GroupFactor, dataset[2:11])
> # p-values for the shapiro test (by levels of GroupFactor)
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
> shapiro.test(x)$p.value))
>
> b)
> d <- data.frame(dataset$GroupFactor, dataset[2:11])
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
> wilcox.test(x)$p.value))
>
> How to replace the p-values with values of "W" (for Shapiro-Wilk
test and,
> respectively, for Wilcoxon-Mann-Whitney U test)?
>
> Example of output:
> ---------------------------
>> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
> shapiro.test(x)$p.value))
>  Group.1            1          2        3           4            5
> 6         7         8      IA        IV
> 1   town 0.0030465882 0.002068448 0.01129771 7.606061e-06 1.189380e-03
> 0.001396341 0.009097276 6.824918e-05 0.4877525 0.08005584
> 2   country 0.0002436141 0.005135004 0.01540923 2.469611e-06 1.372854e-05
> 0.001517259 0.052174738 1.057227e-06 0.3172002 0.15745973
>
>
> Regards,
> Iurie Malai
> Department of Psychology
> Ion Creanga Moldova Pedagogical State University - www.upsm.md
> http://en.wikipedia.org/wiki/Ion_Creang%C4%83_Pedagogical_State_University
>

At a closer look I realized that this method is good for Shapiro-Wilk test,
not for Two-samples Wilcoxon test.
Can somebody to sugest a solution? I want to compare town with country.

2010/5/25 Iurie Malai <iurie.malai at gmail.com>
>
> I use two similar scripts to put calculated values (many thanks for Phil
> Spector) of shapiro.test and, respectively, of wilcox.test in a table:
>
> a)
> d <- data.frame(dataset$GroupFactor, dataset[2:11])
>
    with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
shapiro.test(x)$statistic))
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
> shapiro.test(x)$p.value))
>
> b)
> d <- data.frame(dataset$GroupFactor, dataset[2:11])
>
    with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
wilcox.test(x)$statistic))
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
> wilcox.test(x)$p.value))
>
> Example of output:
> ---------------------------
>
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
shapiro.test(x)$statistic))
  Group.1        1          2        3           4            5          6
        7         8      IA        IV
1   town 0.9582980 0.9558846 0.9661461 0.9148788 0.9523534 0.9533873
0.9648804 0.9323970 0.9877194 0.9771856
2   country 0.9416557 0.9614808 0.9679403 0.9050779 0.9197961 0.9539193
0.9748136 0.8972952 0.9849926 0.9809476
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
shapiro.test(x)$p.value))
  Group.1            1          2        3           4            5
6         7         8      IA        IV
1   town 0.0030465882 0.002068448 0.01129771 7.606061e-06 1.189380e-03
0.001396341 0.009097276 6.824918e-05 0.4877525 0.08005584
2   country 0.0002436141 0.005135004 0.01540923 2.469611e-06 1.372854e-05
0.001517259 0.052174738 1.057227e-06 0.3172002 0.15745973

> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
wilcox.test(x)$statistic))
  Group.1    1          2        3           4            5          6
        7         8      IA        IV
1  town 5050 5050 5050 4753 5050 5050 5050 5050 5050 5050
2  country 5050 5050 5050 4753 5050 5050 5050 5050 5050 5050
> with(d, aggregate(d[,-1], list(d[,1]), FUN = function(x)
wilcox.test(x)$p.value))
  Group.1            1          2        3           4            5
6         7         8      IA        IV
1   town 3.108746e-18 2.915348e-18 3.296582e-18 7.780612e-18 2.949441e-18
2.970452e-18 3.203640e-18 2.976408e-18 3.616515e-18 3.635618e-18
2   country 2.690976e-18 2.980669e-18 2.956880e-18 7.065626e-18 2.682166e-18
2.505664e-18 3.223056e-18 2.577218e-18 3.689949e-18 3.555541e-18
>
>
> Regards,
> Iurie Malai
> Department of Psychology
> Ion Creanga Moldova Pedagogical State University - www.upsm.md
> http://en.wikipedia.org/wiki/Ion_Creang%C4%83_Pedagogical_State_University
>
-------------- next part --------------
"GroupFactor",1,2,3,4,5,6,7,8,"IA","IV"
"town",7,5,4,5,5,8,6,8,5.30,7.00
"town",3,5,5,3,2,6,9,5,4.30,7.50
"town",7,5,8,0,3,10,9,5,6.60,9.50
"town",5,6,7,3,6,6,8,5,6.00,7.00
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