The phylogenetic ANOVA

This function performs the variance analysis of a trait on eigenvectors associated to a phylogenetic tree.

Usage

variance.phylog(phylog, z, bynames = TRUE,
 na.action = c("fail", "mean"))

Arguments

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)

Details

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.

Value

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 anova that is a summary of the precedent object.

References

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.

Author

Sébastien Ollier sebastien.ollier@u-psud.fr
Daniel Chessel

See also

phylog, lm

Examples

data(njplot)
njplot.phy <- newick2phylog(njplot$tre)
variance.phylog(njplot.phy,njplot$tauxcg)
#> $lm
#> 
#> Call:
#> stats::lm(formula = fmla, data = df)
#> 
#> Coefficients:
#> (Intercept)           A1           A2           A3           A4           A5  
#>   1.137e-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
#> 
#> $sumry
#>              Df  Sum Sq Mean Sq F value    Pr(>F)    
#> Phylogenetic 13 27.8502 2.14232  6.5919 6.188e-05 ***
#> Residuals    22  7.1498 0.32499                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
par(mfrow = c(1,2))
table.phylog(njplot.phy$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)

if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) {
  tre <- ape::read.tree(text = njplot$tre)
  adephylo::orthogram(njplot$tauxcg, tre = tre)
}
#> Warning: Labels are not unique. 
#> Warning: Labels are not unique. 
#> Warning: Labels are not unique. 
#> Warning: Labels are not unique. 
#> Warning: Labels are not unique. 

#> class: krandtest lightkrandtest 
#> Monte-Carlo tests
#> Call: adephylo::orthogram(x = njplot$tauxcg, tre = tre)
#> 
#> Number of tests:   4 
#> 
#> Adjustment method for multiple comparisons:   none 
#> Permutation number:   999 
#>    Test        Obs    Std.Obs   Alter Pvalue
#> 1 R2Max  0.1801004  0.3075748 greater  0.332
#> 2 SkR2k 11.3482467 -2.7799548 greater  1.000
#> 3  Dmax  0.3987362  3.6931639 greater  0.001
#> 4   SCE  1.6017356  4.6676211 greater  0.006
#>