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

Daniel C Bezerra danielcbezerra em gmail.com
Segunda Novembro 19 10:53:40 BRST 2012


Como comentamos anteriormente, o pacote ppcor não deve estar disponível
para a sua versão do R (2.15.1). Atualize o R para a versão mais recente
(2.15.2) e tente novamente.

Abs,

D

On Mon, Nov 19, 2012 at 10:40 AM, <alanarocha em sapo.pt> wrote:

> 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 <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<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<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<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<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<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
>
>
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