[R-br] pacotes do R para trabalhar com correlação

[alanarocha em sapo.pt]

Olá a todos,
eu ando as voltas para instalar pacotes para trabalhar com correlação.
install.packages("ppcor", lib="~/Rpacks") library"
library(ppcor, lib="~/Rpacks")
install.packages("ppcor", lib="~/Rpacks") library"
library(ppcor, lib="~/Rpacks")

   no library trees found in 'lib.loc'
>
>

help.search(“correlaction”)
>
> help.search(“correlation”)
Error: unexpected input in "help.search(“"

>
> help.search(“correlation”)
Error: unexpected input in "help.search(“"
>
>
> help.search(“correlaction”)
Error: unexpected input in "help.search(“"
>
> help.search(“cor”)
Error: unexpected input in "help.search(“"
>
> help(package=ppcor)
starting httpd help server ... done
> help(package=ppcor) help(package=cor) install.packages("ppcor")
Installing package(s) into ‘C:/Documents and Settings/ARua/My  
Documents/R/win-library/2.15’
(as ‘lib’ is unspecified)
Warning: unable to access index for repository  
http://cran.fiocruz.br/bin/windows/contrib/2.15
Warning: unable to access index for repository  
http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/2.15
Warning message:
package ‘ppcor’ is not available (for R version 2.15.1)
> library(ppcor)
Error in library(ppcor) : there is no package called ‘ppcor’
>
e tambem com "cor" pscor etc
se eu tenho um pdf do pacote por isso acho ainda mai estranho...
que estou eu a fazer mal? tenho R2.15
Package ‘ppcor’
February 15, 2012
Type Package
Title Partial and Semi-partial (Part) correlation
Version 1.0
Date 2011-06-14
Author Seongho Kim
Maintainer Seongho Kim <biostatistician.kim em gmail.com>
Description The R package ppcor can calculate parital and semi-partial
(part) correlations along with p-value.
License GPL-2
Repository CRAN
Date/Publication 2011-06-15 18:04:26
R topics documented:
ppcor-package . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
. . . . . . . . . . . . 1
pcor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
. . . . . . . . . . . . 3
pcor.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
. . . . . . . . . . . . . 4
spcor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
. . . . . . . . . . . . . 5
spcor.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
. . . . . . . . . . . . 7
Index 9
ppcor-package Partial and Semi-partial (Part) correlation
Description
The R package ppcor can calculate parital and semi-partial (part)  
correlations along with p value.
Details
1
2 ppcor-package
Package: ppcor
Type: Package
Version: 1.0
Date: 2011-06-14
License: GPL-2
Author(s)
Seongho Kim <biostatistician.kim em gmail.com>
References
Kim, S.H. and Yi, S. (2007) Understanding relationship between  
sequence and functional evolution
in yeast proteins . Genetica, 131: 151. Kim, S.H. and Yi, S. (2006)  
Correlated asymmetry between
sequence and functional divergence of duplicate proteins in  
Saccharomyces cerevisiae . Molecular
Biology and Evolution, 23: 1068. Johnson, Richard A. and Dean W.  
Wichern (2002) Applied
multivariate statistical analysis. Prentice Hall. Whittaker, Joe  
(1990) Graphical models in applied
multivariate statistics. John Wiley & Sons.
Examples
# data
y.data <- data.frame(
hl=c(7,15,19,15,21,22,57,15,20,18),
disp=c(0.000,0.964,0.000,0.000,0.921,0.000,0.000,1.006,0.000,1.011),
deg=c(9,2,3,4,1,3,1,3,6,1),
BC=c(1.78e-02,1.05e-06,1.37e-05,7.18e-03,0.00e+00,0.00e+00,0.00e+00,4.48e-03,2.10e-06,0.00e+00)
)
# partial correlation
pcor(y.data)
# partial correlation between "hl" and "disp" given "deg" and "BC"
pcor.test(y.data$hl,y.data$disp,y.data[,c("deg","BC")])
pcor.test(y.data[,1],y.data[,2],y.data[,c(3:4)])
pcor.test(y.data[,1],y.data[,2],y.data[,-c(1:2)])
# semi-partial (part) correlation
spcor(y.data)
# semi-partial (part) correlation between "hl" and "disp" given "deg" and "BC"
spcor.test(y.data$hl,y.data$disp,y.data[,c("deg","BC")])
spcor.test(y.data[,1],y.data[,2],y.data[,c(3:4)])
spcor.test(y.data[,1],y.data[,2],y.data[,-c(1:2)])
pcor 3
pcor Partial correlation
Description
The function pcor can calculate the pairwise partial correlations for  
each pair of variables given
others. In addition, it gives us the p value as well as statistic for  
each pair of variables.
Usage
pcor(x, method = c("pearson", "kendall", "spearman"))
Arguments
x a matrix or data fram.
method a character string indicating which partial correlation  
coefficient is to be computed.
One of "pearson" (default), "kendall", or "spearman" can be abbreviated.
Details
Partial correlation is the correlation of two variables while  
controlling for a third or more other
variables.
Value
estimate a matrix of the partial correlation coefficient between two variables
p.value a matrix of the p value of the test
statistic a matrix of the value of the test statistic
n the number of samples
gn the number of given variables
method the correlation method used
Note
Missing values are not allowed.
Author(s)
Seongho Kim <<biostatistician.kim em gmail.com>>
References
Kim, S.H. and Yi, S. (2007) Understanding relationship between  
sequence and functional evolution
in yeast proteins . Genetica, 131: 151. Kim, S.H. and Yi, S. (2006)  
Correlated asymmetry between
sequence and functional divergence of duplicate proteins in  
Saccharomyces cerevisiae . Molecular
Biology and Evolution, 23: 1068. Johnson, Richard A. and Dean W.  
Wichern (2002) Applied
multivariate statistical analysis. Prentice Hall. Whittaker, Joe  
(1990) Graphical models in applied
multivariate statistics. John Wiley & Sons.
4 pcor.test
See Also
pcor.test, spcor, spcor.test
Examples
# data
y.data <- data.frame(
hl=c(7,15,19,15,21,22,57,15,20,18),
disp=c(0.000,0.964,0.000,0.000,0.921,0.000,0.000,1.006,0.000,1.011),
deg=c(9,2,3,4,1,3,1,3,6,1),
BC=c(1.78e-02,1.05e-06,1.37e-05,7.18e-03,0.00e+00,0.00e+00,0.00e+00,4.48e-03,2.10e-06,0.00e+00)
)
# partial correlation
pcor(y.data)
pcor.test Partial correlation for two variables given a third variable.
Description
The function pcor.test can calculate the pairwise partial correlations  
between two variables. In
addition, it gives us the p value as well as statistic.
Usage
pcor.test(x, y, z, method = c("pearson", "kendall", "spearman"))
Arguments
x a numeric vector.
y a numeric vector.
z a numeric vector.
method a character string indicating which partial correlation  
coefficient is to be computed.
One of "pearson" (default), "kendall", or "spearman" can be abbreviated.
Details
Partial correlation is the correlation of two variables while  
controlling for a third variable.
Value
estimate the partial correlation coefficient between two variables
p.value the p value of the test
statistic the value of the test statistic
n the number of samples
gn the number of given variables
method the correlation method used
spcor 5
Note
Missing values are not allowed
Author(s)
Seongho Kim <<biostatistician.kim em gmail.com>>
References
Kim, S.H. and Yi, S. (2007) Understanding relationship between  
sequence and functional evolution
in yeast proteins . Genetica, 131: 151. Kim, S.H. and Yi, S. (2006)  
Correlated asymmetry between
sequence and functional divergence of duplicate proteins in  
Saccharomyces cerevisiae . Molecular
Biology and Evolution, 23: 1068. Johnson, Richard A. and Dean W.  
Wichern (2002) Applied
multivariate statistical analysis. Prentice Hall. Whittaker, Joe  
(1990) Graphical models in applied
multivariate statistics. John Wiley & Sons.
See Also
pcor, spcor, spcor.test
Examples
# data
y.data <- data.frame(
hl=c(7,15,19,15,21,22,57,15,20,18),
disp=c(0.000,0.964,0.000,0.000,0.921,0.000,0.000,1.006,0.000,1.011),
deg=c(9,2,3,4,1,3,1,3,6,1),
BC=c(1.78e-02,1.05e-06,1.37e-05,7.18e-03,0.00e+00,0.00e+00,0.00e+00,4.48e-03,2.10e-06,0.00e+00)
)
# partial correlation between "hl" and "disp" given "deg" and "BC"
pcor.test(y.data$hl,y.data$disp,y.data[,c("deg","BC")])
pcor.test(y.data[,1],y.data[,2],y.data[,c(3:4)])
pcor.test(y.data[,1],y.data[,2],y.data[,-c(1:2)])
spcor Semi-partial (part) correlation
Description
The function spcor can calculate the pairwise semi-partial (part)  
correlations for each pair of variables
given others. In addition, it gives us the p value as well as  
statistic for each pair of variables.
Usage
spcor(x, method = c("pearson", "kendall", "spearman"))
6 spcor
Arguments
x a matrix or data fram.
method a character string indicating which semi-partial (part)  
correlation coefficient is
to be computed. One of "pearson" (default), "kendall", or "spearman" can be
abbreviated.
Details
Semi-partial correlation is the correlation of two variables with  
variation from a third or more other
variables removed only from the second variable.
Value
estimate a matrix of the semi-partial (part) correlation coefficient  
between two variables
p.value a matrix of the p value of the test
statistic a matrix of the value of the test statistic
n the number of samples
gn the number of given variables
method the correlation method used
Note
Missing values are not allowed.
Author(s)
Seongho Kim <<biostatistician.kim em gmail.com>>
References
Kim, S.H. and Yi, S. (2007) Understanding relationship between  
sequence and functional evolution
in yeast proteins . Genetica, 131: 151. Kim, S.H. and Yi, S. (2006)  
Correlated asymmetry between
sequence and functional divergence of duplicate proteins in  
Saccharomyces cerevisiae . Molecular
Biology and Evolution, 23: 1068. Johnson, Richard A. and Dean W.  
Wichern (2002) Applied
multivariate statistical analysis. Prentice Hall. Whittaker, Joe  
(1990) Graphical models in applied
multivariate statistics. John Wiley & Sons.
See Also
spcor.test, pcor, pcor.test
Examples
# data
y.data <- data.frame(
hl=c(7,15,19,15,21,22,57,15,20,18),
disp=c(0.000,0.964,0.000,0.000,0.921,0.000,0.000,1.006,0.000,1.011),
deg=c(9,2,3,4,1,3,1,3,6,1),
spcor.test 7
BC=c(1.78e-02,1.05e-06,1.37e-05,7.18e-03,0.00e+00,0.00e+00,0.00e+00,4.48e-03,2.10e-06,0.00e+00)
)
# semi-partial (part) correlation
spcor(y.data)
spcor.test Semi-partial (part) correlation for two variables given a  
third variable.
Description
The function spcor.test can calculate the pairwise semi-partial (part)  
correlations between two
variables. In addition, it gives us the p value as well as statistic.
Usage
spcor.test(x, y, z, method = c("pearson", "kendall", "spearman"))
Arguments
x a numeric vector.
y a numeric vector.
z a numeric vector.
method a character string indicating which partial correlation  
coefficient is to be computed.
One of "pearson" (default), "kendall", or "spearman" can be abbreviated.
Details
Semi-partial correlation is the correlation of two variables with  
variation from a third variable removed
only from the second variable.
Value
estimate the semi-partial (part) correlation coefficient between two variables
p.value the p value of the test
statistic the value of the test statistic
n the number of samples
gn the number of given variables
method the correlation method used
Note
Missing values are not allowed
8 spcor.test
Author(s)
Seongho Kim <<biostatistician.kim em gmail.com>>
References
Kim, S.H. and Yi, S. (2007) Understanding relationship between  
sequence and functional evolution
in yeast proteins . Genetica, 131: 151. Kim, S.H. and Yi, S. (2006)  
Correlated asymmetry between
sequence and functional divergence of duplicate proteins in  
Saccharomyces cerevisiae . Molecular
Biology and Evolution, 23: 1068. Johnson, Richard A. and Dean W.  
Wichern (2002) Applied
multivariate statistical analysis. Prentice Hall. Whittaker, Joe  
(1990) Graphical models in applied
multivariate statistics. John Wiley & Sons.
See Also
spcor, pcor, pcor.test
Examples
# data
y.data <- data.frame(
hl=c(7,15,19,15,21,22,57,15,20,18),
disp=c(0.000,0.964,0.000,0.000,0.921,0.000,0.000,1.006,0.000,1.011),
deg=c(9,2,3,4,1,3,1,3,6,1),
BC=c(1.78e-02,1.05e-06,1.37e-05,7.18e-03,0.00e+00,0.00e+00,0.00e+00,4.48e-03,2.10e-06,0.00e+00)
)
# semi-partial (part) correlation between "hl" and "disp" given "deg" and "BC"
spcor.test(y.data$hl,y.data$disp,y.data[,c("deg","BC")])
spcor.test(y.data[,1],y.data[,2],y.data[,c(3:4)])
spcor.test(y.data[,1],y.data[,2],y.data[,-c(1:2)])
Index
_Topic htest
pcor, 3
pcor.test, 4
ppcor-package, 1
spcor, 5
spcor.test, 7
pcor, 3, 5, 6, 8
pcor.test, 4, 4, 6, 8
ppcor (ppcor-package), 1
ppcor-package, 1
spcor, 4, 5, 5, 8
spcor.test, 4–6, 7
u9no meu caso qero trabalhar com matrizes de celação
obrigada
Ana Rocha