Nothing

```
## Compute the probabilities of generating every state, given that we
## started with one species in state 0 or state 1. For lt = length(t)
## times, returns a n(space) x lt x 2 array.
## 'scal': a multiplier to get use of extra precision (the vector
## will be scaled so that the total probability sums to
## scal. Underflow is possible where scal=1, though).
## 'tol': tolerance, used as exit condition (when changes in the
## probability space do not exceed tol, the step size is good
## enough). Lower values _may_ be faster and _may_ have
## lower accuracy (but with large numbers of clades being
## calculated, this effect can be small). It is not clear to
## me if tol should be resacled by scal before being passed
## in.
## 'm': parameter affecting internal arguments. Small numbers often
## faster, sometimes slower. Play around.
nucexp <- function(nt, la0, la1, mu0, mu1, q01, q10, p0c, p0a,
p1c, p1a, t, scal=1, tol=1e-10, m=15) {
stop("Unresolved clade calculations no longer supported (since 0.10.0)")
}
## This is more useful; given model parameters, and vectors of times
## (t), number of species in each clade (Nc), numbers of species in
## known states (nsc) and numbers of species in state b (k), calculate
## the probabilities of generating the clade, and of extinction.
## Returns a length(t) x 4 matrix, where the first two columns are the
## probabilities of generating the clade, and the second two are the
## probabilities of extinction.
nucexpl <- function(nt, la0, la1, mu0, mu1, q01, q10, p0c, p0a,
p1c, p1a, t, Nc, nsc, k, scal=1, tol=1e-10, m=15) {
stop("Unresolved clade calculations no longer supported (since 0.10.0)")
}
## Construct the transition matrix. Again, non-R style, as this was
## used as a template for constructing the same in Fortran.
make.matrix.ness <- function(nt, mu.a, mu.b, lambda.a, lambda.b, q.ba,
q.ab, p.ac, p.aa, p.bc, p.ba) {
## Diagonals (are linear sums of these: -(na*pa + nb*pb))
## The chance of no change is unaffected by BiSSEness, which only alters what
## happens at speciation.
pa <- lambda.a + mu.a + q.ba
pb <- lambda.b + mu.b + q.ab
## A few useful numbers:
## n1 represents the state space size, excluding the absorbing state and the state with nt-1 species
## 2*n1 also represents the total number of non-zero elements in each of the transition and extinction matrices
## nm represents the state space size: {0,0}, {1,0}, {0,1}, ...{absorbing with nt species}
n1 <- nt*(nt-1)/2
nm <- n1 + nt + 1
## Create some useful vectors
## j1 contains the positions in the vector space that have some species in state a (na>0)
## j1+1 contains the positions in the vector space that have some species in state b (nb>0)
## na contains just the vector of numbers of species in state a (dropping those with na=0)
## nb contains just the vector of numbers of species in state b (dropping those with nb=0)
j1 <- integer(n1)
na <- integer(n1)
nb <- integer(n1)
k <- 1
for ( i in 1:(nt-1) ) {
for ( j in 1:i ) {
j1[k] <- i + k
na[k] <- i - j + 1
nb[k] <- j
k <- k + 1
}
}
## Character state transitions and extinctions
m <- matrix(0, nm, nm)
for ( i in 1:n1 ) {
m[j1[i], i] <- na[i]*mu.a
m[j1[i]+1, i] <- nb[i]*mu.b
m[j1[i], j1[i]+1] <- na[i]*q.ba
m[j1[i]+1, j1[i]] <- nb[i]*q.ab
}
## Speciation and diagonals (BiSSE):
## for ( i in 2:n1 ) {
## if ( na[i] > 1 )
## m[i, j1[i]] <- (na[i]-1)*lambda.a
## if ( nb[i] > 1 )
## m[i, j1[i]+1] <- (nb[i]-1)*lambda.b
## m[i,i] <- - (na[i]-1)*pa - (nb[i]-1)*pb
## }
## Speciation and diagonals (BiSSEness):
## (Calculations are performed where na[i]-1 represents number of state a species in source.)
for ( i in 2:n1 ) {
if ( na[i] > 1 ) {
m[i, j1[i]] <- (na[i]-1)*lambda.a*(1-p.ac)
m[i, j1[i]+1] <- (na[i]-1)*lambda.a*p.ac*p.aa
m[i, j1[i]+2] <- (na[i]-1)*lambda.a*p.ac*(1-p.aa)
}
if ( nb[i] > 1 ) {
m[i, j1[i]+1] <- m[i, j1[i]+1] + (nb[i]-1)*lambda.b*(1-p.bc)
m[i, j1[i]] <- m[i, j1[i]] + (nb[i]-1)*lambda.b*p.bc*p.ba
## While the above may be contributed by speciation in state a, the following cannot be.
m[i, j1[i]-1] <- (nb[i]-1)*lambda.b*p.bc*(1-p.ba)
}
m[i,i] <- - (na[i]-1)*pa - (nb[i]-1)*pb
}
## Speciation in the special final column, diagonals for the last
## class (unaffected by bisseness, because all movements to last
## class are collapsed, regardless of trait combination).
k <- nt*(nt-1)/2
for ( i in 1:nt ) {
m[k+i, nm] <- (nt-i)*lambda.a + (i-1)*lambda.b
m[k+i, k+i] <- -(nt-i)*pa - (i-1)*pb
}
m
}
```

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