# Package overview

#### Francois Keck

#### 2023-10-12

### Data

First, we load the package `phylosignal` and the dataset `carnivora` from `adephylo`.

    library(phylosignal)
    library(adephylo)
    library(ape)
    library(phylobase)
    data(carni19)

Here is a phylogenetic tree of 19 carnivora species.

    tre <- read.tree(text=carni19$tre)

And we create a dataframe of 3 traits for the 19 carnivora species.

- Body mass
- Random values
- Simulated values under a Brownian Motion model along the tree

    dat <- list()
    dat$mass <- carni19$bm
    dat$random <- rnorm(19, sd = 10)
    dat$bm <- rTraitCont(tre)
    dat <- as.data.frame(dat)

We can combine phylogeny and traits into a `phylo4d` object.

    p4d <- phylo4d(tre, dat)

### Visualizing the data

    barplot.phylo4d(p4d, tree.type = "phylo", tree.ladderize = TRUE)

### Measuring and testing the signal for each trait and different methods

    phyloSignal(p4d = p4d, method = "all")

    ## $stat
    ##               Cmean           I         K    K.star       Lambda
    ## mass    0.549388707  0.39210678 0.7127747 0.7154914 9.640762e-01
    ## random -0.008591412 -0.01054837 0.1710380 0.1656598 6.846792e-05
    ## bm      0.631997788  0.54900008 1.2066485 1.2041770 1.027115e+00
    ##
    ## $pvalue
    ##        Cmean     I     K K.star Lambda
    ## mass   0.002 0.001 0.001  0.001  0.001
    ## random 0.373 0.311 0.167  0.208  1.000
    ## bm     0.001 0.001 0.001  0.001  0.001

### Assessing the behavior of these methods with this phylogeny along a Brownian-Motion influence gradient

    phylosim <- phyloSim(tree = tre, method = "all", nsim = 100, reps = 99)

    plot(phylosim, stacked.methods = FALSE, quantiles = c(0.05, 0.95))

    plot.phylosim(phylosim, what = "pval", stacked.methods = TRUE)

### Assessing the signal depth with correlograms

    mass.crlg <- phyloCorrelogram(p4d, trait = "mass")
    random.crlg <- phyloCorrelogram(p4d, trait = "random")
    bm.crlg <- phyloCorrelogram(p4d, trait = "bm")

    plot(mass.crlg)

    plot(random.crlg)

    plot(bm.crlg)

### Locating the signal with LIPA

    carni.lipa <- lipaMoran(p4d)
    carni.lipa.p4d <- lipaMoran(p4d, as.p4d = TRUE)

    barplot.phylo4d(p4d, bar.col=(carni.lipa$p.value < 0.05) + 1, center = FALSE , scale = FALSE)

    barplot.phylo4d(carni.lipa.p4d, bar.col = (carni.lipa$p.value < 0.05) + 1, center = FALSE, scale = FALSE)