pcor.test | R Documentation |

Calculate the partial correlation coefficient of both of parametric ("Pearson") and non-parametric ("Spearman" and "Kendall") statistics.

pcor.test(x, y, z, use = c("mat","rec"), method = c("pearson","spearman","kendall"), na.rm = T)

`x` |
a numeric vector. Missing values are allowed. |

`y` |
a numeric vector. Missing values are allowed. |

`z` |
a numeric vector, matrix or data frame. Missing values are allowed. |

`use` |
an optional character string giving a method for computing the partial correlation coefficients. This must be one of the strings "mat" (default) or "rec". |

`method` |
a character string indicating which partial correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated. |

`na.rm` |
logical. Should missing values be removed? Default is `True` |

`estimate`

gives the partial correlation coefficieint between `x`

and `y`

given `z`

,
`p.value`

gives the p-value of the test,
`statistic`

gives the value of the test statistic,
`n`

gives the number of samples after deleting all the missing samples,
`gn`

gives the number of given variables,
`Method`

gives the correlation method used, and
`Use`

gives the computation method used.
`pcor.test`

.
One is by using variance-covariance matrix ("`mat`

") and the other recursive formula ("`rec`

").
Both of "`mat`

" and "`rec`

" give the same result in case of `na.rm = T`

.
Otherwise, the `estimate`

may be slightly different from each other due to the way dealing with the missing samples,
if there are the missing samples.
Kim, S-H. and Yi, S. (2007)
Understanding relationship between sequence and functional evolution in yeast proteins
.
* Genetica, * In press.

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:** 1068–1075.

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.

`cor`

, `cov2cor`

, `eigen`

,
`cor.test`

# load the R code "pcor.test" source("pcor.R") # 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)])

[R code *pcor.test*]