Simulation of a dataset
To show how the functions work, let’s first simulate some traits with
known parameters.
We use function sim.bd.taxa.age from package
TreeSim to simulate a phylogenetic tree.
set.seed(17920902)
ntaxa = 80
tree <- TreeSim::sim.bd.taxa.age(n = ntaxa, numbsim = 1, lambda = 0.1, mu = 0,
age = 1, mrca = TRUE)[[1]]
We then choose a set of parameters, using function
params_process:
params <- params_process("OU", ## Process
p = 2, ## Dimension
variance = diag(0.5, 2, 2) + 0.5, ## Rate matrix
selection.strength = 3, ## Selection Strength
random = TRUE, ## Root is random
stationary.root = TRUE, ## Root is stationary
edges = c(16, 81, 124), ## Positions of the shifts
values = cbind(c(5, 4), ## Values of the shifts
c(-4, -5),
c(5, -3)))
Note that here, we took the selection strength to a scalar matrix,
but we could specify a full matrix. Also, we did not specified the
ancestral values of the optimum states: these are took to be \(0\) by default.
The position of the shifts is specified through to the number of the
edge where it occurs. These can be saw using a standard
plot from package ape. Once the parameters are
constructed, we can check the position of the shifts by plotting
them:
plot(params, phylo = tree, traits = 1, value_in_box = TRUE, shifts_bg = "white")
plot(params, phylo = tree, traits = 2, value_in_box = TRUE, shifts_bg = "white")
We can then simulate a process along the tree with these
parameters:
sim <- simul_process(params, tree)
We can then extract a matrix of simulated data from this object:
data <- extract(sim, ## The simul_process object
what = "states", ## We want the actual values
where = "tips") ## Only at the tips of the tree
The column names of the matrix are set to the tip labels of the tree.
By default, the row names are left blank, but, for the sake of the
demonstration, let’s assume that the two traits are named “A” and
“B”:
rownames(data) <- c("A", "B")
Finally, we can plot the simulated process on the tree to see what it
looks like:
plot(params, phylo = tree, data = data)
Inference
In this section, we take the traits as simulated by function
simul_process, and use function PhyloEM for
the inference of the parameters.
To make things more interesting, let’s assume that we have some
missing data:
nMiss <- floor(ntaxa * 2 * 0.1) ## 10% of missing data
miss <- sample(1:(2 * ntaxa), nMiss, replace = FALSE) ## sample missing randomly
chars <- (miss - 1) %% 2 + 1 ## Trace back rows and columns
tips <- (miss - 1) %/% 2 + 1
for (i in 1:nMiss){
data[chars[i], tips[i]] <- NA ## Forget some values
}
By default, the inference is done on an automatically computed grid
of alpha values. Here, to keep the computations to a minimum, we will
only use \(2\) values for alpha,
including the true value. In a true application however, a grid with
\(10\) values of \(\alpha\) is automatically chosen.
## Grid on alpha
alpha_grid <- c(1, 3)
## Run algorithm
res <- PhyloEM(phylo = tree,
Y_data = data,
process = "scOU", ## scalar OU model
random.root = TRUE, ## Root is stationary (true model)
stationary.root = TRUE,
alpha = alpha_grid, ## On a grid of alpha
K_max = 10, ## Maximal number of shifts
parallel_alpha = TRUE, ## This can be set to TRUE for
Ncores = 2) ## parallel computations
res
## Result of the PhyloEM algorithm.
## Selected parameters by the default method:
## 2 dimensional scOU process with a random stationary root.
##
## Root expectations:
## [1] -0.03037687 -0.16473674
##
## Root variance:
## [,1] [,2]
## [1,] 0.16133808 0.08997022
## [2,] 0.08997022 0.17555872
##
## Process variance:
## [,1] [,2]
## [1,] 0.9680285 0.5398213
## [2,] 0.5398213 1.0533523
##
## Process selection strength:
## [,1] [,2]
## [1,] 3 0
## [2,] 0 3
##
## Process root optimal values:
## [1] -0.03037687 -0.16473674
##
## Shifts positions on branches: 124, 81, 16
## Shifts values:
## 124 81 16
## [1,] 4.840772 -4.044706 5.156022
## [2,] -2.837176 -4.843107 4.268610
##
##
##
## See help to see all plotting and handling functions.
The fitted object contains the inferred parameters for every values
of \(\alpha\) and \(K\), as well as the parameters selected by
the various model selection criteria.
Here, we can see that the original shifts were recovered by the model
selection procedure. We can plot the obtained solution, as shown below.
The inferred states a the tips are shown in dotted lines. Notice that
they are coherent with the positions of the shifts that we found.
The function above automatically plots the solution selected by the
model selection criterion. We can plot this criterion, to see whether
the solution selected is a clear minimum.
Here, the criterion behaves rather well, and the solution for \(K=3\) seems to be a good choice.
Above, we plotted the solution given by the LINselect method (the
default). But we might want to see the solution given by another
selection method, such as the slope heuristic. In that case, we just
need to run:
plot(res, params = params_process(res, method.selection = "DDSE"))
where we extracted the parameters with the method
params_process. Here, the results are the same.
We can also extract the solution for a given number of shifts and/or
a given value of alpha. For instance, we can see that in this simple
case, the inferred position of the shifts is robust to the value of the
selection strength:
plot(res, params = params_process(res, K = 3, alpha = 1))
The inferred shifts values however are quite different:
params_process(res, K = 3, alpha = 1)$shifts
## $edges
## [1] 124 81 16
##
## $values
## [,1] [,2] [,3]
## [1,] 8.112486 -6.319460 9.170696
## [2,] -4.904368 -7.624025 7.856662
##
## $relativeTimes
## [1] 0 0 0
params_process(res, K = 3, alpha = 3)$shifts
## $edges
## [1] 124 81 16
##
## $values
## [,1] [,2] [,3]
## [1,] 4.840772 -4.044706 5.156022
## [2,] -2.837176 -4.843107 4.268610
##
## $relativeTimes
## [1] 0 0 0
