kopperud/slouch

Stochastic Linear Ornstein Uhlenbeck Comparative Hypotheses

slouch's Issues

bug when calculating weight matrix for SIMMAP trees

In slouch version 2.1.4 there is a bug in calculating the weight matrix for a model with discrete predictors mapped as regimes on the tree. The reason we discovered this, is that we were working on an alternative (faster) implementation of the likelihood. I'm copy-pasting a minimum-working example below

## minimum working example
library(ape)
library(phytools)
library(slouch)

############################################################
#                                                          #
#            State-dependent pruning                       #
# !! σ2, α are scalars, θ is a vector of length # states   #   
#                                                          #
############################################################

sd_postorder <- function(node_index, edge, tree, continuousChar,
                         μ, V, log_norm_factor, subedges_lengths, σ2, α, θ){
  ntip = length(tree$tip.label)
  
  # if is internal node
  if (node_index > ntip){
    
    left_edge  = which(edge[,1] == node_index)[1] # index of left child edge
    right_edge = which(edge[,1] == node_index)[2] # index of right child edge
    left = edge[left_edge,2] # index of left child node
    right = edge[right_edge,2] # index of right child node
    
    output_left <- sd_postorder(left, edge, tree, continuousChar,
                                μ, V, log_norm_factor, subedges_lengths, σ2, α, θ)
    μ <- output_left[[1]]
    V <- output_left[[2]]
    log_norm_factor <- output_left[[3]]
    
    output_right <- sd_postorder(right, edge, tree, continuousChar,
                                 μ, V, log_norm_factor, subedges_lengths, σ2, α, θ)
    μ <- output_right[[1]]
    V <- output_right[[2]]
    log_norm_factor <- output_right[[3]]
    
    sub_bl_left = subedges_lengths[left_edge][[1]] # all subedges of left child edge
    sub_bl_right = subedges_lengths[right_edge][[1]] # all subedges of right child edge
    
    # for the sake of readability, computation of variance, mean, and log_nf are done in separate loops
    # 1) variance of the normal variable: this branch (v_left) and the subtree (V[left])
    ## Is 'delta_left* exp(2.0 * α * bl_left)' added in each sub-edge?
    
    delta_left = V[left]
    v_left = 0 # initialise v_left
    for (i in rev(1:length(sub_bl_left))){
      state <- names(sub_bl_left)[i]
      delta_t <- sub_bl_left[[i]]
      v_left = σ2/(2*α) * expm1(2.0*α * delta_t) 
      delta_left = v_left + delta_left * exp(2.0 * α * delta_t)
    }
    
    delta_right = V[right]
    v_right = 0 # initialise v_right
    for (i in rev(1:length(sub_bl_right))){
      state <- names(sub_bl_right)[i]
      v_right = σ2/(2*α) *expm1(2.0*α*sub_bl_right[[i]])
      delta_right = v_right + delta_right * exp(2.0 * α * sub_bl_right[[i]])
    }
    
    var_left = delta_left
    var_right = delta_right
    
    # 2) mean of the normal variable
    mean_left = μ[left]
    for (i in rev(1:length(sub_bl_left))){
      state <- names(sub_bl_left)[i]
      mean_left = exp(α*sub_bl_left[[i]])*(mean_left - θ[[state]]) + θ[[state]]
    }
    
    mean_right = μ[right]
    for (i in rev(1:length(sub_bl_right))){
      state <- names(sub_bl_right)[i]
      mean_right = exp(α*sub_bl_right[[i]])*(mean_right - θ[[state]]) + θ[[state]]
    }
    
    ## compute the mean and variance of the node
    mean_ancestor = (mean_left * var_right + mean_right * var_left) / (var_left + var_right)
    μ[node_index] = mean_ancestor
    var_node = (var_left * var_right) / (var_left + var_right)
    V[node_index] = var_node
    
    ## compute the normalizing factor, the left-hand side of the pdf of the normal variable
    log_nf_left = 0
    for (i in rev(1:length(sub_bl_left))){
      state <- names(sub_bl_left)[i]
      delta_t <- sub_bl_left[[i]]
      log_nf_left = log_nf_left + delta_t * α
    }
    
    log_nf_right = 0
    for (i in rev(1:length(sub_bl_right))){
      state <- names(sub_bl_right)[i]
      log_nf_right = log_nf_right + sub_bl_right[[i]] * α
    }
    
    contrast = mean_left - mean_right
    a = -(contrast*contrast / (2*(var_left+var_right)))
    b = log(2*pi*(var_left+var_right))/2.0
    log_nf = log_nf_left + log_nf_right + a - b
    log_norm_factor[node_index] = log_nf
    
    return(list(μ, V, log_norm_factor))
  }
  # if is tip
  else{
    species = tree$tip.label[node_index]
    
    μ[node_index] = continuousChar[[which(names(continuousChar) == species)]]
    V[node_index] = 0.0 ## if there is no observation error
    
    return(list(μ, V, log_norm_factor))
  }
}

sd_logL_pruning <- function(tree, continuousChar, σ2, α, θ){
  ntip = length(tree$tip.label) # number of tips
  edge = tree$edge # equals tree[:edge] in Julia
  n_edges = length(edge[,1]) # number of edges
  max_node_index = max(tree$edge) # total number of nodes
  
  V = numeric(max_node_index)
  μ = numeric(max_node_index)
  log_norm_factor = numeric(max_node_index)
  
  subedges_lengths = tree$maps
  
  root_index = ntip + 1
  
  output <- sd_postorder(root_index, edge, tree, continuousChar,
                         μ, V, log_norm_factor, subedges_lengths, σ2, α, θ)
  μ <- output[[1]]
  V <- output[[2]]
  log_norm_factor <- output[[3]]
  
  ## assume root value equal to theta
  μ_root = μ[root_index]
  v_root = V[root_index]
  left_edge_from_root <- which(edge[,1] == ntip+1)[1] # obtain left child edge index of root node
  left_subedges_from_root <- subedges_lengths[[left_edge_from_root]] # obtain sub-edge lengths
  root_state = names(tail(left_subedges_from_root))[[1]] # obtain root state, assuming it equals last state at left child edge
  lnl = dnorm(θ[[root_state]], mean = μ_root, sd = sqrt(v_root), log = TRUE)
  
  ## add norm factor
  for (log_nf in log_norm_factor){
    lnl = lnl + log_nf
  }
  return(lnl)
}

And some code to test the two versions of computing the likelihood

###################################################
#                                                 #
#                     Testing...                  #   
#                                                 #
###################################################

# test with slouch data set
data("artiodactyla")
data("neocortex")

# convert continuous data to read.nexus.data() format
brain <- list()
for (i in 1:length(neocortex$brain_mass_g_log_mean)){
  sp <- neocortex$species[i]
  brain[sp] <- list(neocortex$brain_mass_g_log_mean[i])
}

neocortex <- neocortex[match(artiodactyla$tip.label, neocortex$species), ]
diet <- as.character(neocortex$diet)
names(diet) <- neocortex$species
set.seed(123)
smaptree <- phytools::make.simmap(artiodactyla, diet)

This is what the SIMMAP tree looks like (it's just one random SIMMAP, but it does not matter for illustrating that the likelihoods are equivalent)

m0 <- slouch.fit(
        smaptree,
        species = neocortex$species,
        response = neocortex$brain_mass_g_log_mean,
        fixed.fact = neocortex$diet,
        a_values = 0.01,
        sigma2_y_values = 1.0,
        anc_maps = "simmap",
        hillclimb = FALSE
    )


theta <- dput(m0$beta_primary$coefficients[,1])
lnl_brain_pruning <- sd_logL_pruning(smaptree, brain,
                                     σ2 = 1.0,
                                     α = 0.01, θ = theta)

As of version 2.1.4, these two methods gave different results (i.e. different probability densities). There is a fix in 88c6181. When printing the likelihoods after the bug fix, in version 2.1.5 (I will upload a new version to CRAN), the likelihoods are identical up to floating point errors

print(m0$modfit$Support, digits = 20)
print(lnl_brain_pruning, digits = 20)

[1] -89.861374999422423571
[1] -89.86137499942240936