Phylogeny and quantitative traits of flowers

Description

This data set describes the phylogeny of 11 flowers as reported by Morales (2000). It also gives morphologic and demographic traits corresponding to these 11 species.

Usage

data(tithonia)

Format

tithonia is a list containing the 2 following objects :

tre

is a character string giving the phylogenetic tree in Newick format.

tab

is a data frame with 11 species and 14 traits (6 morphologic traits and 8 demographic).

Details

Variables of tithonia$tab are the following ones :
morho1: is a numeric vector that describes the seed size (mm)
morho2: is a numeric vector that describes the flower size (mm)
morho3: is a numeric vector that describes the female leaf size (cm)
morho4: is a numeric vector that describes the head size (mm)
morho5: is a integer vector that describes the number of flowers per head
morho6: is a integer vector that describes the number of seeds per head
demo7: is a numeric vector that describes the seedling height (cm)
demo8: is a numeric vector that describes the growth rate (cm/day)
demo9: is a numeric vector that describes the germination time
demo10: is a numeric vector that describes the establishment (per cent)
demo11: is a numeric vector that describes the viability (per cent)
demo12: is a numeric vector that describes the germination (per cent)
demo13: is a integer vector that describes the resource allocation
demo14: is a numeric vector that describes the adult height (m)

Source

Data were obtained from Morales, E. (2000) Estimating phylogenetic inertia in Tithonia (Asteraceae) : a comparative approach. Evolution, 54, 2, 475–484.

Examples

data(tithonia)
phy <- newick2phylog(tithonia$tre)
tab <- log(tithonia$tab + 1)
table.phylog(scalewt(tab), phy)
gearymoran(phy$Wmat, tab)
gearymoran(phy$Amat, tab)

Worked out examples

> library(ade4)
> ### Name: tithonia
> ### Title: Phylogeny and quantitative traits of flowers
> ### Aliases: tithonia
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(tithonia)
> phy <- newick2phylog(tithonia$tre)
> tab <- log(tithonia$tab + 1)
> table.phylog(scalewt(tab), phy)

> gearymoran(phy$Wmat, tab)
class: krandtest 
Monte-Carlo tests
Call: as.krandtest(sim = matrix(res$result, ncol = nvar, byr = TRUE), 
    obs = res$obs, alter = alter, names = test.names)

Test number:   14 
Permutation number:   999 
     Test       Obs    Std.Obs   Alter Pvalue
1  morho1 0.7321356  4.9798473 greater  0.003
2  morho2 0.3822949  0.4099018 greater  0.306
3  morho3 0.3712126  0.3818651 greater  0.265
4  morho4 0.2572795 -0.9160990 greater  0.786
5  morho5 0.4457180  1.0830945 greater  0.127
6  morho6 0.4089212  0.8093959 greater  0.173
7   demo7 0.4416215  1.0750672 greater  0.114
8   demo8 0.4822195  1.6753639 greater  0.065
9   demo9 0.3043863 -0.4400796 greater  0.616
10 demo10 0.2744296 -0.8073416 greater  0.805
11 demo11 0.4458932  1.3889707 greater  0.096
12 demo12 0.2640213 -0.9185727 greater  0.858
13 demo13 0.6092138  3.3125114 greater  0.006
14 demo14 0.3903768  0.5407252 greater  0.239

other elements: NULL
> gearymoran(phy$Amat, tab)
class: krandtest 
Monte-Carlo tests
Call: as.krandtest(sim = matrix(res$result, ncol = nvar, byr = TRUE), 
    obs = res$obs, alter = alter, names = test.names)

Test number:   14 
Permutation number:   999 
     Test         Obs     Std.Obs   Alter Pvalue
1  morho1  0.53784586  2.97648592 greater  0.004
2  morho2  0.10046720  0.73531858 greater  0.236
3  morho3  0.07014773  0.42867221 greater  0.312
4  morho4 -0.09223746 -0.62446197 greater  0.783
5  morho5  0.35119692  2.16630569 greater  0.025
6  morho6  0.17659490  1.01639094 greater  0.166
7   demo7  0.44981278  2.69566720 greater  0.010
8   demo8  0.25528857  1.44331572 greater  0.090
9   demo9 -0.01050264 -0.09530229 greater  0.496
10 demo10 -0.09660405 -0.57577067 greater  0.669
11 demo11  0.26985310  1.52828703 greater  0.069
12 demo12 -0.19861554 -1.15667781 greater  0.885
13 demo13  0.63572312  3.50352045 greater  0.001
14 demo14  0.05085783  0.33862758 greater  0.357

other elements: NULL
> 
> 
> 
>