| variance.phylog {ade4} | R Documentation |
This function performs the variance analysis of a trait on eigenvectors associated to a phylogenetic tree.
variance.phylog(phylog, z, bynames = TRUE,
na.action = c("fail", "mean"))
phylog |
: an object of class phylog |
z |
: a numeric vector of the values corresponding to the variable |
bynames |
: if TRUE checks if z labels are the same as phylog leaves label, possibly in a different order. If FALSE the check is not made and z labels must be in the same order than phylog leaves label |
na.action |
: if 'fail' stops the execution of the current expression when z contains any missing value. If 'mean' replaces any missing values by mean(z) |
phylog$Amat defines a set of orthonormal vectors associated the each nodes of the phylogenetic tree.
phylog$Adim defines the dimension of the subspace A defined by
the first phylog$Adim vectors of phylog$Amat that corresponds to phylogenetic inertia.
variance.phylog performs the linear regression of z on A.
Returns a list containing
lm |
: an object of class lm that corresponds to the linear regression of z on A. |
anova |
: an object of class anova that corresponds to the anova of the precedent model. |
smry |
: an object of class table that is a summary of the precedent object. |
Sébastien Ollier ollier@biomserv.univ-lyon1.fr
Daniel Chessel
Grafen, A. (1989) The phylogenetic regression. Philosophical Transactions of the Royal Society London B, 326, 119–156.
Diniz-Filho, J. A. F., Sant'Ana, C.E.R. and Bini, L.M. (1998) An eigenvector method for estimating phylogenetic inertia. Evolution, 52, 1247–1262.
data(njplot) njplot.phy <- newick2phylog(njplot$tre) variance.phylog(njplot.phy,njplot$tauxcg) par(mfrow = c(1,2)) table.phylog(njplot.phy$Ascores, njplot.phy, clabel.row = 0, clabel.col = 0.1, clabel.nod = 0.6, csize = 1) dotchart.phylog(njplot.phy, njplot$tauxcg, clabel.nodes = 0.6) orthogram(njplot$tauxcg, njplot.phy$Ascores)
> library(ade4)
> ### Name: variance.phylog
> ### Title: The phylogenetic ANOVA
> ### Aliases: variance.phylog
> ### Keywords: models
>
> ### ** Examples
>
> data(njplot)
> njplot.phy <- newick2phylog(njplot$tre)
> variance.phylog(njplot.phy,njplot$tauxcg)
$lm
Call:
lm(formula = fmla, data = df)
Coefficients:
(Intercept) A1 A2 A3 A4 A5
-2.312e-16 2.884e-01 3.291e-01 -1.967e-02 4.661e-02 3.326e-01
A6 A7 A8 A9 A10 A11
9.722e-02 -4.594e-01 5.077e-02 -2.489e-01 3.056e-01 -2.126e-01
A12 A13
-1.691e-01 -1.293e-01
$anova
Analysis of Variance Table
Response: z
Df Sum Sq Mean Sq F value Pr(>F)
A1 1 2.9935 2.9935 9.2111 0.006080 **
A2 1 3.8986 3.8986 11.9960 0.002209 **
A3 1 0.0139 0.0139 0.0429 0.837899
A4 1 0.0782 0.0782 0.2407 0.628576
A5 1 3.9834 3.9834 12.2569 0.002019 **
A6 1 0.3402 0.3402 1.0469 0.317332
A7 1 7.5986 7.5986 23.3809 7.855e-05 ***
A8 1 0.0928 0.0928 0.2855 0.598470
A9 1 2.2304 2.2304 6.8630 0.015646 *
A10 1 3.3618 3.3618 10.3442 0.003977 **
A11 1 1.6270 1.6270 5.0064 0.035705 *
A12 1 1.0293 1.0293 3.1673 0.088946 .
A13 1 0.6023 0.6023 1.8532 0.187190
Residuals 22 7.1498 0.3250
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$sumry
Df Sum Sq Mean Sq F value Pr(>F)
Phylogenetic 13 27.85018 2.14232 6.59193 6e-05
Residuals 22 7.14982 0.32499
> par(mfrow = c(1,2))
> table.phylog(njplot.phy$Ascores, njplot.phy, clabel.row = 0,
+ clabel.col = 0.1, clabel.nod = 0.6, csize = 1)
> dotchart.phylog(njplot.phy, njplot$tauxcg, clabel.nodes = 0.6)
> orthogram(njplot$tauxcg, njplot.phy$Ascores) class: krandtest Monte-Carlo tests Call: orthogram(x = njplot$tauxcg, orthobas = njplot.phy$Ascores) Test number: 4 Permutation number: 999 Test Obs Std.Obs Alter Pvalue 1 R2Max 0.2171031 1.103395 greater 0.144 2 SkR2k 10.1182201 -3.380872 less 0.001 3 Dmax 0.4356543 4.298191 two-sided 0.001 4 SCE 2.4195191 7.270271 greater 0.002 other elements: NULL
> > > >