,
Sergei Tarasov [aut] | Title: | Ontology-Informed Phylogenetic Comparative Analyses |
|---|---|
| Description: | This package provides new phylogenetic tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019). The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using PNG vector images of the organisms of interest. |
| Authors: | Diego S. Porto [aut, cre] ,
Sergei Tarasov [aut] |
| Maintainer: | Diego S. Porto <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.0 |
| Built: | 2024-11-22 06:10:10 UTC |
| Source: | https://github.com/diegosasso/ontophylo |
Adds noise to the points in the 2D coordinates in the MDS plot. # The noise is calculated as var(V)*add.noise.
add_noise_MD(MD, add.noise)add_noise_MD(MD, add.noise)
MD |
tibble. The output of a MD_morpho function. |
add.noise |
numeric. A vector of length 2 indicating the amount of noise to be added to the x and y coordinates. |
A list of tibbles as in the output of MD_morpho functions, with noise added.
Sergei Tarasov
data("hym_stm_mds") # Get a sample of 5 amalgamated stochastic maps. tree_list <- hym_stm_mds tree_list <- merge_tree_cat_list(tree_list) ## Not run: # Multidimensional scaling for an arbitrary tree. MD <- suppressWarnings(MultiScale.simmap(tree_list[[1]], add.noise = NULL)) # Add noise. add_noise_MD(MD, c(0.3, 0.3)) ## End(Not run)data("hym_stm_mds") # Get a sample of 5 amalgamated stochastic maps. tree_list <- hym_stm_mds tree_list <- merge_tree_cat_list(tree_list) ## Not run: # Multidimensional scaling for an arbitrary tree. MD <- suppressWarnings(MultiScale.simmap(tree_list[[1]], add.noise = NULL)) # Add noise. add_noise_MD(MD, c(0.3, 0.3)) ## End(Not run)
Adds a vector of pseudodata to the path data obtained from the 'make_data_NHPP_KDE_Markov_kernel' function.
add_pseudodata(Edge.groups, Pseudo.data, Path.data)add_pseudodata(Edge.groups, Pseudo.data, Path.data)
Edge.groups |
list. A list with groups of edge IDs. |
Pseudo.data |
numeric. A vector with values of pdeusodata. |
Path.data |
numeric. A list of path data obtained from the 'make_data_NHPP_KDE_Markov_kernel' function. |
A list of path data with the pseudodata added.
Sergei Tarasov
data("hym_hm", "hym_tree") # Get hamming data from the head characters. hm <- hym_hm$head # Make NHPP path data. nhpp <- make_data_NHPP_KDE_Markov_kernel(hm, add.psd = FALSE) # Add pseudo data to path data. psd <- lapply(nhpp, function(x) -x[x < 100] ) edge_groups <- as.list(1:length(hym_tree$edge.length)) nhpp_psd <- add_pseudodata(Edge.groups = edge_groups, Pseudo.data = psd, Path.data = nhpp) # Check NHPP path data plus pseudodata for an arbitrary branch. nhpp_psd[[5]]data("hym_hm", "hym_tree") # Get hamming data from the head characters. hm <- hym_hm$head # Make NHPP path data. nhpp <- make_data_NHPP_KDE_Markov_kernel(hm, add.psd = FALSE) # Add pseudo data to path data. psd <- lapply(nhpp, function(x) -x[x < 100] ) edge_groups <- as.list(1:length(hym_tree$edge.length)) nhpp_psd <- add_pseudodata(Edge.groups = edge_groups, Pseudo.data = psd, Path.data = nhpp) # Check NHPP path data plus pseudodata for an arbitrary branch. nhpp_psd[[5]]
Wrapper function for making a plot of an object of class 'Picture' using the 'make_pic' function.
anat_plot(picture, anat_layers, plot_stat, color_palette, scale_lim)anat_plot(picture, anat_layers, plot_stat, color_palette, scale_lim)
picture |
grImport object. A vector image imported in R using the 'readPicture' function from grImport. |
anat_layers |
numeric. A named vector with the layer IDs obtained using the 'get_vector_ids_list' function. |
plot_stat |
numeric. A named vector with values corresponding to the branch statistics to be plotted and names matching the names in the layer IDs. |
color_palette |
A character vector or function defining a color palette. |
scale_lim |
numeric. A pair of values defining the lower and upper limits of the scale. |
A plot of the object of class 'Picture' with the assigned colors to different anatomical regions.
Diego Porto
data("HAO", "hym_graph", "hym_img", "hym_kde") # Get picture. picture <- hym_img # Get picture layers from three anatomical regions. terms_list <- as.list(c("HAO:0000397", "HAO:0000576", "HAO:0000626")) terms_list <- setNames(terms_list, c("head", "mesosoma", "metasoma")) anat_layers <- get_vector_ids_list(terms = terms_list , ONT = HAO, GR = hym_graph) # Get mean rates all branches for the three anatomical regions. plot_stat <- lapply(hym_kde, function(x) unlist(lapply(x$loess.lambda.mean, function(x) mean(x) )) ) plot_stat <- do.call(cbind, plot_stat) # Select an arbitrary branch. plot_stat <- plot_stat[50,] # Set scale. scale_lim <- range(plot_stat) # Get color palette. hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") # Plot picture. ## Not run: anat_plot(picture, anat_layers, plot_stat, hm.palette(100), scale_lim) ## End(Not run)data("HAO", "hym_graph", "hym_img", "hym_kde") # Get picture. picture <- hym_img # Get picture layers from three anatomical regions. terms_list <- as.list(c("HAO:0000397", "HAO:0000576", "HAO:0000626")) terms_list <- setNames(terms_list, c("head", "mesosoma", "metasoma")) anat_layers <- get_vector_ids_list(terms = terms_list , ONT = HAO, GR = hym_graph) # Get mean rates all branches for the three anatomical regions. plot_stat <- lapply(hym_kde, function(x) unlist(lapply(x$loess.lambda.mean, function(x) mean(x) )) ) plot_stat <- do.call(cbind, plot_stat) # Select an arbitrary branch. plot_stat <- plot_stat[50,] # Set scale. scale_lim <- range(plot_stat) # Get color palette. hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") # Plot picture. ## Not run: anat_plot(picture, anat_layers, plot_stat, hm.palette(100), scale_lim) ## End(Not run)
Function to plot the color scale bar.
color.bar( pal, min, max = -min, nticks = 11, ticks = seq(min, max, len = nticks), title = "" )color.bar( pal, min, max = -min, nticks = 11, ticks = seq(min, max, len = nticks), title = "" )
pal |
character. A vector with color IDs. |
min |
numeric. Value for lower limit of the scale. |
max |
numeric. Value for upper limit of the scale. |
nticks |
integer. Number of subdivisions of the scale. |
title |
character. A legend for the scale bar. |
A plot of the color scale bar.
Sergei Tarasov
stat <- runif(10, 0.25, 1) hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") ## Not run: color.bar(hm.palette(100), min = min(stat), max = max(stat), ticks = round(c(min(stat), max(stat)/2, max(stat)), 2), title = "") ## End(Not run)stat <- runif(10, 0.25, 1) hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") ## Not run: color.bar(hm.palette(100), min = min(stat), max = max(stat), ticks = round(c(min(stat), max(stat)/2, max(stat)), 2), title = "") ## End(Not run)
Calculates the derivative of the normalized Markov KDE or normalized loess smoothing over edges.
derivative_KDE(tree.discr, Edge.KDE.stat)derivative_KDE(tree.discr, Edge.KDE.stat)
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
Edge.KDE.stat |
list. A list with the estimated normalized or loess smoothing KDEs for each edge. |
A list with the distribution of the derivatives calculated for each edge.
Sergei Tarasov
data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Calculate derivatives. Edge_KDE$loess.lambda.mean.deriv <- derivative_KDE(tree_discr, Edge_KDE_stat) # Check derivatives of some arbitrary branch. Edge_KDE$loess.lambda.mean.deriv[[5]]data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Calculate derivatives. Edge_KDE$loess.lambda.mean.deriv <- derivative_KDE(tree_discr, Edge_KDE_stat) # Check derivatives of some arbitrary branch. Edge_KDE$loess.lambda.mean.deriv[[5]]
Discretizes tree edges into identical bins given a selected resolution value.
discr_Simmap(tree, res)discr_Simmap(tree, res)
tree |
simmap or phylo object. |
res |
integer. A resolution value for the discretization of tree edges. |
A simmap or phylo object.
Sergei Tarasov
data("hym_stm") tree <- hym_stm[[1]][[1]] stm_discr <- discr_Simmap(tree, res = 100) # Check some arbitrary branch. tree$maps[[10]] stm_discr$maps[[10]]data("hym_stm") tree <- hym_stm[[1]][[1]] stm_discr <- discr_Simmap(tree, res = 100) # Check some arbitrary branch. tree$maps[[10]] stm_discr$maps[[10]]
Discretizes tree edges of a list of trees.
discr_Simmap_all(tree, res)discr_Simmap_all(tree, res)
tree |
multiSimmap or multiPhylo object. |
res |
integer. A resolution value for the discretization of tree edges. |
A multiSimmap or multiPhylo object.
Sergei Tarasov
data("hym_stm") tree_list <- hym_stm[[1]] stm_discr_list <- discr_Simmap_all(tree_list, res = 100) # Check some arbitrary branch of some arbitrary tree. tree_list[[1]]$maps[[10]] stm_discr_list[[1]]$maps[[10]]data("hym_stm") tree_list <- hym_stm[[1]] stm_discr_list <- discr_Simmap_all(tree_list, res = 100) # Check some arbitrary branch of some arbitrary tree. tree_list[[1]]$maps[[10]] stm_discr_list[[1]]$maps[[10]]
Gets the information necessary for making an edgeplot, where the tree is plotted in a space where the x axis is the time and y axis the scale of the desired statistics.
edge_profiles4plotting(tree.discr, Edge.KDE.stat)edge_profiles4plotting(tree.discr, Edge.KDE.stat)
tree.discr |
phylo object. A discretized tree using the 'discr_Simmap' function. |
Edge.KDE.stat |
list. A list with the distributions of the estimated parameter of KDEs for each edge. |
A tibble with X and Y coordinates and other information necessary for making an edgeplot.
Sergei Tarasov
data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Make edgeplot nhpp data. stat_prof <- edge_profiles4plotting(tree_discr, Edge_KDE_stat)data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Make edgeplot nhpp data. stat_prof <- edge_profiles4plotting(tree_discr, Edge_KDE_stat)
Wrapper function for plotting edge profiles and contmap from NHPP.
edgeplot(map_stat, prof_stat, plot.cont = TRUE)edgeplot(map_stat, prof_stat, plot.cont = TRUE)
map_stat |
contMap object. A contMap obtained using the 'make_contMap_KDE' function. |
prof_stat |
tibble. A tibble with the edgeplot information obtained using the 'edge_profiles4plotting' function. |
plot.cont |
logical. Whether to plot also the contMap or not. |
A plot with the edge profiles and contMap of the selected statistic (e.g. branch rates).
Diego Porto
data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Make contmap nhpp data. map_stat <- make_contMap_KDE(tree_discr, Edge_KDE_stat) # Make edgeplot nhpp data. prof_stat <- edge_profiles4plotting(tree_discr, Edge_KDE_stat) # Plot. ## Not run: edgeplot(map_stat, prof_stat) ## End(Not run)data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Make contmap nhpp data. map_stat <- make_contMap_KDE(tree_discr, Edge_KDE_stat) # Make edgeplot nhpp data. prof_stat <- edge_profiles4plotting(tree_discr, Edge_KDE_stat) # Plot. ## Not run: edgeplot(map_stat, prof_stat) ## End(Not run)
Estimate the bandwidth for the Markov KDE.
estimate_band_W( tree.discr, data.path, band.width = c("bw.nrd0", "bw.nrd0", "bw.ucv", "bw.bcv", "bw.SJ") )estimate_band_W( tree.discr, data.path, band.width = c("bw.nrd0", "bw.nrd0", "bw.ucv", "bw.bcv", "bw.SJ") )
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
data.path |
list. A list of path data obtained from the 'make_data_NHPP_KDE_Markov_kernel' function. |
band.width |
character. Bandwidth selectors for the KDE, as in density. |
A numeric vector.
Sergei Tarasov
data("hym_hm", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get hamming data from the head characters. hm <- hym_hm$head # Make NHPP path data. nhpp_psd <- make_data_NHPP_KDE_Markov_kernel(hm, add.psd = TRUE) # Calculate bandwidth. bdw <- estimate_band_W(tree_discr, nhpp_psd, band.width = "bw.nrd0") mean(bdw)data("hym_hm", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get hamming data from the head characters. hm <- hym_hm$head # Make NHPP path data. nhpp_psd <- make_data_NHPP_KDE_Markov_kernel(hm, add.psd = TRUE) # Calculate bandwidth. bdw <- estimate_band_W(tree_discr, nhpp_psd, band.width = "bw.nrd0") mean(bdw)
Estimated the normalized Markov KDE for each edge averaged across all possible root-tip paths.
estimate_edge_KDE(tree.discr, Path.data, h)estimate_edge_KDE(tree.discr, Path.data, h)
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
Path.data |
numeric. A list of path data obtained from the 'make_data_NHPP_KDE_Markov_kernel' function. |
h |
numeric. A value for the bandwidth calculated using the 'estimate_band_W' function. |
A list with the estimated unnormalized ($Maps.mean) and normalized ($Maps.mean.norm) KDEs for each edge.
Sergei Tarasov
data("hym_nhpp", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Make NHPP path data. nhpp <- hym_nhpp$head # Add pseudo data to path data. psd <- lapply(nhpp, function(x) -x[x < 100] ) edge_groups <- as.list(1:length(hym_tree$edge.length)) nhpp_psd <- add_pseudodata(Edge.groups = edge_groups, Pseudo.data = psd, Path.data = nhpp) # Calculate bandwidth. bdw <- estimate_band_W(tree_discr, nhpp_psd, band.width = "bw.nrd0") bdw <- mean(bdw) # Estimate non-normalized and normalized edge KDE. Edge_KDE <- estimate_edge_KDE(tree_discr, nhpp_psd, h = bdw) # Check KDE data for normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.norm[[5]] # Check KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean[[5]]data("hym_nhpp", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Make NHPP path data. nhpp <- hym_nhpp$head # Add pseudo data to path data. psd <- lapply(nhpp, function(x) -x[x < 100] ) edge_groups <- as.list(1:length(hym_tree$edge.length)) nhpp_psd <- add_pseudodata(Edge.groups = edge_groups, Pseudo.data = psd, Path.data = nhpp) # Calculate bandwidth. bdw <- estimate_band_W(tree_discr, nhpp_psd, band.width = "bw.nrd0") bdw <- mean(bdw) # Estimate non-normalized and normalized edge KDE. Edge_KDE <- estimate_edge_KDE(tree_discr, nhpp_psd, h = bdw) # Check KDE data for normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.norm[[5]] # Check KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean[[5]]
Estimated the unnormalized Markov KDE for each edge averaged across all possible root-tip paths.
estimate_edge_KDE_Markov_kernel_unnorm(tree.discr, Path.data, h = 10)estimate_edge_KDE_Markov_kernel_unnorm(tree.discr, Path.data, h = 10)
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
Path.data |
numeric. A list of path data obtained from the 'make_data_NHPP_KDE_Markov_kernel' function. |
h |
numeric. A value for the bandwidth calculated using the 'estimate_band_W' function. |
A list with the estimated unnormalized KDEs ($Maps.mean) for each edge.
Sergei Tarasov
data("hym_nhpp", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Make NHPP path data. nhpp <- hym_nhpp$head # Add pseudo data to path data. psd <- lapply(nhpp, function(x) -x[x < 100] ) edge_groups <- as.list(1:length(hym_tree$edge.length)) nhpp_psd <- add_pseudodata(Edge.groups = edge_groups, Pseudo.data = psd, Path.data = nhpp) # Calculate bandwidth. bdw <- estimate_band_W(tree_discr, nhpp_psd, band.width = "bw.nrd0") bdw <- mean(bdw) # Estimate non-normalized and normalized edge KDE. Edge_KDE <- estimate_edge_KDE_Markov_kernel_unnorm(tree_discr, nhpp_psd, h = bdw) # Check KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean[[5]]data("hym_nhpp", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Make NHPP path data. nhpp <- hym_nhpp$head # Add pseudo data to path data. psd <- lapply(nhpp, function(x) -x[x < 100] ) edge_groups <- as.list(1:length(hym_tree$edge.length)) nhpp_psd <- add_pseudodata(Edge.groups = edge_groups, Pseudo.data = psd, Path.data = nhpp) # Calculate bandwidth. bdw <- estimate_band_W(tree_discr, nhpp_psd, band.width = "bw.nrd0") bdw <- mean(bdw) # Estimate non-normalized and normalized edge KDE. Edge_KDE <- estimate_edge_KDE_Markov_kernel_unnorm(tree_discr, nhpp_psd, h = bdw) # Check KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean[[5]]
Returns all characters located (associated) with a given ontology term.
get_descendants_chars(ontology, annotations = "auto", terms, ...)get_descendants_chars(ontology, annotations = "auto", terms, ...)
ontology |
ontology_index object. |
annotations |
character. Sets which annotations to use: "auto" means automatic annotations, "manual" means manual annotations. Alternatively, any other list of element containing annotations can be specified. |
terms |
character. IDs of ontology terms for which descendants are queried. |
... |
other parameters for ontologyIndex::get_descendants() function. |
The vector of character IDs.
Sergei Tarasov
data("HAO") HAO$terms_selected_id <- list("CH1" = c("HAO:0000653"), "CH2" = c("HAO:0000653")) get_descendants_chars(HAO, annotations = "manual", "HAO:0000653")data("HAO") HAO$terms_selected_id <- list("CH1" = c("HAO:0000653"), "CH2" = c("HAO:0000653")) get_descendants_chars(HAO, annotations = "manual", "HAO:0000653")
Internal function. Not exported.
get_path_edges(tree.merge, node)get_path_edges(tree.merge, node)
Sergei Tarasov
Get state colors for ploting stochastic character maps when there many states.
get_rough_state_cols(tree)get_rough_state_cols(tree)
tree |
simmap object. |
A character vector with colors associated with state names.
Sergei Tarasov
data("hym_stm_amalg") # Get one sample of stochastic map from head. tree <- hym_stm_amalg$head[[5]] # Plot one amalgamated stochastic map from head. ## Not run: phytools::plotSimmap(tree, get_rough_state_cols(tree), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) ## End(Not run)data("hym_stm_amalg") # Get one sample of stochastic map from head. tree <- hym_stm_amalg$head[[5]] # Plot one amalgamated stochastic map from head. ## Not run: phytools::plotSimmap(tree, get_rough_state_cols(tree), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) ## End(Not run)
Internal function. Not exported.
get_states_path(tree.merge, node)get_states_path(tree.merge, node)
Sergei Tarasov
Given an ontology_index object, data.frame with ontology term labels, and data.frame with picture information (see examples), produces a named vector with layer IDs to be used in the 'make_pic' function.
get_vector_ids_list(terms_list, ONT, GR)get_vector_ids_list(terms_list, ONT, GR)
ONT |
ontology_index object. |
GR |
data.frame. A data.frame with the picture information. It contains the matches between all ontology term labels and layer IDs in the Picture object. The first column corresponds to the ontology term labels, the second to the ontology IDs, and the third to the layer IDs in the Picture object. |
term_list |
list. A named list with ontology terms to get layer IDs for. The first column corresponds to the ontology term labels, the second to the ontology IDs. |
A named vector with the layer IDs corresponding to or descending from the ontology term label queried.
Diego S. Porto
data("HAO", "hym_graph") # Get picture layers from three anatomical regions. terms_list <- as.list(c("HAO:0000397", "HAO:0000576", "HAO:0000626")) terms_list <- setNames(terms_list, c("head", "mesosoma", "metasoma")) get_vector_ids_list(terms = terms_list , ONT = HAO, GR = hym_graph)data("HAO", "hym_graph") # Get picture layers from three anatomical regions. terms_list <- as.list(c("HAO:0000397", "HAO:0000576", "HAO:0000626")) terms_list <- setNames(terms_list, c("head", "mesosoma", "metasoma")) get_vector_ids_list(terms = terms_list , ONT = HAO, GR = hym_graph)
Given an ontology_index object, ontology term label, and data.frame with picture information (see examples), produces a named vector with layer IDs to be used in the 'make_pic' function.
get_vector_ids_per_term(term = "HAO:0000349", ONT, GR)get_vector_ids_per_term(term = "HAO:0000349", ONT, GR)
term |
character. An ontology term label to get the corresponding layer IDs in the Picture object. |
ONT |
ontology_index object. |
GR |
data.frame. A data.frame with the picture information. It contains the matches between all ontology term labels and layer IDs in the Picture object. The first column corresponds to the ontology term labels, the second to the ontology IDs, and the third to the layer IDs in the Picture object. |
A named vector with the layer IDs corresponding to or descending from the ontology term label queried.
Sergei Tarasov
data("HAO", "hym_graph") # Get picture layers from head. get_vector_ids_per_term(term = "HAO:0000397", ONT = HAO, GR = hym_graph)data("HAO", "hym_graph") # Get picture layers from head. get_vector_ids_per_term(term = "HAO:0000397", ONT = HAO, GR = hym_graph)
Checks the integral of normalized Markov KDE or normalized loess smoothing over edges.
integrate_edge_KDE(tree.discr, Edge.KDE.list)integrate_edge_KDE(tree.discr, Edge.KDE.list)
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
Edge.KDE.list |
list. A list with the normalized KDEs or loess smoothing for each edge. |
A numeric value for the integral over all edges.
Sergei Tarasov
data("hym_kde", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get non-normalized and normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head # Check integrals. integrate_edge_KDE(tree_discr, Edge_KDE$Maps.mean.norm) integrate_edge_KDE(tree_discr, Edge_KDE$Maps.mean.loess.norm)data("hym_kde", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get non-normalized and normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head # Check integrals. integrate_edge_KDE(tree_discr, Edge_KDE$Maps.mean.norm) integrate_edge_KDE(tree_discr, Edge_KDE$Maps.mean.loess.norm)
Internal function. Not exported.
join_edges(tree.discr, edge.profs)join_edges(tree.discr, edge.profs)
Sergei Tarasov
Internal function. Not exported.
KDE_unnorm_trunc_Markov(x, h, dat)KDE_unnorm_trunc_Markov(x, h, dat)
Sergei Tarasov
Internal function. Not exported.
KDE_unnormalized_scalar_Markov_kernel(x, h, dat)KDE_unnormalized_scalar_Markov_kernel(x, h, dat)
Sergei Tarasov
Takes a list of charater annotations and creates an edge matrix comprising two columns: from and to. The list to table conversion can be done using ldply function from plyr package: plyr::ldply(list, rbind).
list2edges(annotated.char.list, col_order_inverse = FALSE)list2edges(annotated.char.list, col_order_inverse = FALSE)
annotated.char.list |
character list. A character list with ontology annotations. |
col_order_inverse |
logical. The default creates the first columns consisting of character IDs and the second columns consisting of ontology annotations. The inverse order changes the columns order. |
Two-column matrix.
Sergei Tarasov
annot_list <- list("CH1" = c("HAO:0000933", "HAO:0000958"), "CH2" = c("HAO:0000833", "HAO:0000258")) list2edges(annot_list)annot_list <- list("CH1" = c("HAO:0000933", "HAO:0000958"), "CH2" = c("HAO:0000833", "HAO:0000258")) list2edges(annot_list)
Calculates loess smoothing for the unnormalized Markov KDE obtained from the 'estimate_edge_KDE_Markov_kernel_unnorm' function.
loess_smoothing_KDE(tree.discr, Edge.KDE)loess_smoothing_KDE(tree.discr, Edge.KDE)
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
Edge.KDE |
list. A list with the estimated unnormalized KDEs ($Maps.mean) for each edge. |
A list with the loess smoothing calculated for each edge.
Sergei Tarasov
data("hym_kde", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get non-normalized and normalized edge KDE data. Edge_KDE <- hym_kde$head # Calculate smoothing of edge KDE data. Edge_KDE$Maps.mean.loess <- suppressWarnings(loess_smoothing_KDE(tree_discr, Edge_KDE)) # Check smoothing of KDE data for normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.loess.norm[[5]] # Check smoothing of KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.loess[[5]]data("hym_kde", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get non-normalized and normalized edge KDE data. Edge_KDE <- hym_kde$head # Calculate smoothing of edge KDE data. Edge_KDE$Maps.mean.loess <- suppressWarnings(loess_smoothing_KDE(tree_discr, Edge_KDE)) # Check smoothing of KDE data for normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.loess.norm[[5]] # Check smoothing of KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.loess[[5]]
Produces a color scale for a given statistic of evolutionary rate.
make_colors(Stat, palette)make_colors(Stat, palette)
Stat |
numeric. A named vector where values are the statistics, and names are ontology term labels. |
palette |
A character vector or function defining a color palette. |
A character vector where elements are color IDs and names are the input ontology term labels.
Sergei Tarasov
stat <- setNames(runif(5, 0.1, 10), c("cranium", "fore_wing", "hind_wing", "pronotum", "propectus") ) hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") cols.maps <- make_colors(stat, palette = hm.palette(100)) cols.mapsstat <- setNames(runif(5, 0.1, 10), c("cranium", "fore_wing", "hind_wing", "pronotum", "propectus") ) hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") cols.maps <- make_colors(stat, palette = hm.palette(100)) cols.maps
Produces a relative color scale for a given statistic of evolutionary rate.
make_colors_relative_scale(Stat, palette, lims)make_colors_relative_scale(Stat, palette, lims)
Stat |
numeric. A named vector where values are the statistics, and names are ontology term labels. |
palette |
A character vector or function defining a color palette. |
lims |
numeric. A pair of values defining the lower and upper limits of the scale. |
A character vector where elements are color IDs and names are the input ontology term labels.
Sergei Tarasov
stat <- setNames(runif(5, 0.1, 10), c("cranium", "fore_wing", "hind_wing", "pronotum", "propectus") ) hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") cols.maps <- make_colors_relative_scale(stat, palette = hm.palette(100), lims = c(min(stat), max(stat))) cols.mapsstat <- setNames(runif(5, 0.1, 10), c("cranium", "fore_wing", "hind_wing", "pronotum", "propectus") ) hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") cols.maps <- make_colors_relative_scale(stat, palette = hm.palette(100), lims = c(min(stat), max(stat))) cols.maps
Produces a contMap object for plotting the NHPP.
make_contMap_KDE(tree.discr, Edge.KDE.stat)make_contMap_KDE(tree.discr, Edge.KDE.stat)
tree.discr |
phylo object. A discretized tree using the 'discr_Simmap' function. |
Edge.KDE.stat |
list. A list with the distributions of the estimated parameter of KDEs for each edge. |
A 'contMap' object.
Sergei Tarasov
data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Make contmap nhpp data. nhpp_map <- make_contMap_KDE(tree_discr, Edge_KDE_stat) # Plot contmap. ## Not run: phytools::plot.contMap(nhpp_map, lwd = 3, outline = F, legend = F, ftype = "off", plot = F) ## End(Not run)data("hym_tree", "hym_kde") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$loess.lambda.mean # Make contmap nhpp data. nhpp_map <- make_contMap_KDE(tree_discr, Edge_KDE_stat) # Plot contmap. ## Not run: phytools::plot.contMap(nhpp_map, lwd = 3, outline = F, legend = F, ftype = "off", plot = F) ## End(Not run)
Gets data on changing times between states for all edges of a given sample of trees for the Markov kernel density estimator (KDE).
make_data_NHPP_KDE_Markov_kernel(Tb.trees, add.psd = TRUE)make_data_NHPP_KDE_Markov_kernel(Tb.trees, add.psd = TRUE)
Tb.trees |
data.frame. A tibble obtained with the 'path_hamming_over_trees_KDE' function. |
add.psd |
logical. Whether to add pseudodata or not. Default is TRUE. |
A list with changing times between states for all edges of a given sample of trees.
Sergei Tarasov
data("hym_hm") # Get hamming data from the head characters. hm <- hym_hm$head # Make NHPP path data. nhpp <- make_data_NHPP_KDE_Markov_kernel(hm) # Check NHPP path data for an arbitrary branch. nhpp[[5]]data("hym_hm") # Get hamming data from the head characters. hm <- hym_hm$head # Make NHPP path data. nhpp <- make_data_NHPP_KDE_Markov_kernel(hm) # Check NHPP path data for an arbitrary branch. nhpp[[5]]
Gets data on changing times between states for a given edge of a given sample of trees for the Markov kernel density estimator (KDE).
make_data_NHPP_over_edge_MarkovKDE(Tb.trees, Focal.Edge)make_data_NHPP_over_edge_MarkovKDE(Tb.trees, Focal.Edge)
Tb.trees |
data.frame. A tibble obtained with the 'path_hamming_over_trees_KDE' function. |
Focal.Edge |
integer. A value indicating the edge ID. |
A numeric vector with changing times between states for a given edge.
Sergei Tarasov
Internal function. Not exported.
Assigns colors to picture ID layers (@paths) of an object of class 'Picture'. The object should be a PS or ESP vector illustration imported using the grImport package. Colors are taken from cols.maps argument were the palette indicates the scale of the desired statistics for the evolutionary rates.
make_pic(picture, layers, cols.maps)make_pic(picture, layers, cols.maps)
picture |
grImport object. A vector image imported in R using the 'readPicture' function from grImport. |
layers |
numeric. A named vector where values indicate the layer IDs in the Picture object and names indicate the anatomy ontology term labels. |
cols.maps |
character. A named vector where elements correspond to color IDs and names indicate the anatomy ontology term labels. |
An object of class 'Picture' with the assigned colors to different anatomical regions.
Sergei Tarasov
data("HAO", "hym_graph", "hym_img", "hym_kde") # Get picture. picture <- hym_img # Get picture layers from three anatomical regions. terms_list <- as.list(c("HAO:0000397", "HAO:0000576", "HAO:0000626")) terms_list <- setNames(terms_list, c("head", "mesosoma", "metasoma")) anat_layers <- get_vector_ids_list(terms = terms_list , ONT = HAO, GR = hym_graph) # Get mean rates all branches for the three anatomical regions. plot_stat <- lapply(hym_kde, function(x) unlist(lapply(x$loess.lambda.mean, function(x) mean(x) )) ) plot_stat <- do.call(cbind, plot_stat) # Select an arbitrary branch. plot_stat <- plot_stat[50,] # Set scale. scale_lim <- range(plot_stat) # Get color palette. hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") cols_maps <- make_colors_relative_scale(plot_stat, palette = hm.palette(100), lims = scale_lim) # Plot picture. new_pic <- make_pic(picture, anat_layers, cols_maps) ## Not run: grImport::grid.picture(new_pic) ## End(Not run)data("HAO", "hym_graph", "hym_img", "hym_kde") # Get picture. picture <- hym_img # Get picture layers from three anatomical regions. terms_list <- as.list(c("HAO:0000397", "HAO:0000576", "HAO:0000626")) terms_list <- setNames(terms_list, c("head", "mesosoma", "metasoma")) anat_layers <- get_vector_ids_list(terms = terms_list , ONT = HAO, GR = hym_graph) # Get mean rates all branches for the three anatomical regions. plot_stat <- lapply(hym_kde, function(x) unlist(lapply(x$loess.lambda.mean, function(x) mean(x) )) ) plot_stat <- do.call(cbind, plot_stat) # Select an arbitrary branch. plot_stat <- plot_stat[50,] # Set scale. scale_lim <- range(plot_stat) # Get color palette. hm.palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Spectral"), space = "Lab") cols_maps <- make_colors_relative_scale(plot_stat, palette = hm.palette(100), lims = scale_lim) # Plot picture. new_pic <- make_pic(picture, anat_layers, cols_maps) ## Not run: grImport::grid.picture(new_pic) ## End(Not run)
Produces a posterior distribution from a given list of statistics calculated with the 'posterior_lambda_KDE' function.
make_postPois_KDE(Edge.KDE.stat, lambda.post, lambda.post.stat = "Mean")make_postPois_KDE(Edge.KDE.stat, lambda.post, lambda.post.stat = "Mean")
Edge.KDE.stat |
list. A list with the estimated normalized or loess smoothing KDEs for each edge. |
lambda.post |
list. A list with the distribution statistics calculated with the 'posterior_lambda_KDE' function. |
lambda.post.stat |
character. A value with the statistic to be used. |
A list with the distribution of the selected statistic for each edge.
Sergei Tarasov
data("hym_stm_amalg", "hym_kde") # Get a sample of ten stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] tree_list <- merge_tree_cat_list(tree_list) # Calculate posterior poisson statistics. lambda_post <- posterior_lambda_KDE(tree_list) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$Maps.mean.loess.norm # Make posterior poisson distribution. Edge_KDE$lambda.mean <- make_postPois_KDE(Edge_KDE_stat, lambda_post, lambda.post.stat = "Mean") # Check posterior poisson of some arbitrary branch. ## Not run: plot(density(Edge_KDE$lambda.mean[[5]]), main = "", xlab = "Rates") ## End(Not run)data("hym_stm_amalg", "hym_kde") # Get a sample of ten stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] tree_list <- merge_tree_cat_list(tree_list) # Calculate posterior poisson statistics. lambda_post <- posterior_lambda_KDE(tree_list) # Get smoothing of normalized edge KDE data for mean rates. Edge_KDE <- hym_kde$head Edge_KDE_stat <- Edge_KDE$Maps.mean.loess.norm # Make posterior poisson distribution. Edge_KDE$lambda.mean <- make_postPois_KDE(Edge_KDE_stat, lambda_post, lambda.post.stat = "Mean") # Check posterior poisson of some arbitrary branch. ## Not run: plot(density(Edge_KDE$lambda.mean[[5]]), main = "", xlab = "Rates") ## End(Not run)
Wrapper function for plotting morphospaces obtained using the MultiScale.simmap' function.
mds_plot(MD, Tslice = max(MD$Points$time))mds_plot(MD, Tslice = max(MD$Points$time))
MD |
list. A list with the morphospace information obtained using the 'MultiScale.simmap' function. |
Tslice |
numeric. The value for the temporal slices to be plotted, from root to tip. For example, if Tslice = 25, then all points in the morphospaces from time 0 (root) to 25 will be plotted. |
Diego Porto
data("hym_stm_mds") # Get a sample of 5 amalgamated stochastic maps. tree_list <- hym_stm_mds tree_list <- merge_tree_cat_list(tree_list) # Multidimensional scaling for an arbitrary tree. ## Not run: MD <- suppressWarnings(MultiScale.simmap(tree_list[[1]], add.noise = c(0.3,0.3))) MD_plot <- mds_plot(MD, Tslice = 10) MD_plot MD_plot <- mds_plot(MD, Tslice = 50) MD_plot MD_plot <- mds_plot(MD, Tslice = 200) MD_plot MD_plot <- mds_plot(MD, Tslice = 280) MD_plot ## End(Not run)data("hym_stm_mds") # Get a sample of 5 amalgamated stochastic maps. tree_list <- hym_stm_mds tree_list <- merge_tree_cat_list(tree_list) # Multidimensional scaling for an arbitrary tree. ## Not run: MD <- suppressWarnings(MultiScale.simmap(tree_list[[1]], add.noise = c(0.3,0.3))) MD_plot <- mds_plot(MD, Tslice = 10) MD_plot MD_plot <- mds_plot(MD, Tslice = 50) MD_plot MD_plot <- mds_plot(MD, Tslice = 200) MD_plot MD_plot <- mds_plot(MD, Tslice = 280) MD_plot ## End(Not run)
Merges identical state bins over the same branch in the discretized stochastic map.
merge_branch_cat(br)merge_branch_cat(br)
br |
numeric or character vector. The branches of the tree. |
A numeric or character vector with merged identical bins.
Sergei Tarasov
data("hym_stm") tree <- hym_stm[[1]][[1]] stm_discr <- discr_Simmap(tree, res = 100) # Check some arbitrary branch. br1 <- stm_discr$maps[[50]] br1 br2 <- merge_branch_cat(br1) br2 sum(br1) == br2data("hym_stm") tree <- hym_stm[[1]][[1]] stm_discr <- discr_Simmap(tree, res = 100) # Check some arbitrary branch. br1 <- stm_discr$maps[[50]] br1 br2 <- merge_branch_cat(br1) br2 sum(br1) == br2
Merges identical state bins over a tree in the discretized stochastic map.
merge_tree_cat(tree)merge_tree_cat(tree)
tree |
simmap object. |
A tree with merged identical bins.
Sergei Tarasov
data("hym_stm") tree <- hym_stm[[1]][[1]] tree <- discr_Simmap(tree, res = 100) stm_merg <- merge_tree_cat(tree) # Check some arbitrary branch. br1 <- tree$maps[[50]] br1 br2 <- stm_merg$maps[[50]] br2 sum(br1) == br2data("hym_stm") tree <- hym_stm[[1]][[1]] tree <- discr_Simmap(tree, res = 100) stm_merg <- merge_tree_cat(tree) # Check some arbitrary branch. br1 <- tree$maps[[50]] br1 br2 <- stm_merg$maps[[50]] br2 sum(br1) == br2
A wrapper function to merge identical state bins over a tree list.
merge_tree_cat_list(tree.list)merge_tree_cat_list(tree.list)
tree.list |
multiSimmap object. |
A list of trees with merged identical bins.
Diego S. Porto
data("hym_stm") tree_list <- hym_stm[[1]] tree_list <- discr_Simmap_all(tree_list, res = 100) stm_merg_list <- merge_tree_cat_list(tree_list) # Check some arbitrary branch of some arbitrary tree. br1 <- tree_list[[1]]$maps[[50]] br1 br2 <- stm_merg_list[[1]]$maps[[50]] br2 sum(br1) == br2data("hym_stm") tree_list <- hym_stm[[1]] tree_list <- discr_Simmap_all(tree_list, res = 100) stm_merg_list <- merge_tree_cat_list(tree_list) # Check some arbitrary branch of some arbitrary tree. br1 <- tree_list[[1]]$maps[[50]] br1 br2 <- stm_merg_list[[1]]$maps[[50]] br2 sum(br1) == br2
Performs multidimensional scaling (MDS) based on hamming distances among character state vectors from one stochastic character map.
MultiScale.simmap(tree.merge, add.noise = NULL)MultiScale.simmap(tree.merge, add.noise = NULL)
tree.merge |
simmap object. A stochastic character map after being processed by the discr_Simmap and merge_tree_cat functions. |
add.noise |
numeric. A vector of length 2 or NULL. Indicates if noise should be added or not. Useful if there are many identical states occupying the same points in the 2D coordinates of the morphospace plot. The noise is calculated as var(V)*add.noise. |
A list of tibbles – Points, Lines, and Edge.map – correponding to tree branch information to plot.
Sergei Tarasov
data("hym_stm_mds") # Get a sample of 5 amalgamated stochastic maps. tree_list <- hym_stm_mds tree_list <- merge_tree_cat_list(tree_list) # Multidimensional scaling for an arbitrary tree. ## Not run: MD <- suppressWarnings(MultiScale.simmap(tree_list[[1]], add.noise = c(0.3,0.3))) ## End(Not run)data("hym_stm_mds") # Get a sample of 5 amalgamated stochastic maps. tree_list <- hym_stm_mds tree_list <- merge_tree_cat_list(tree_list) # Multidimensional scaling for an arbitrary tree. ## Not run: MD <- suppressWarnings(MultiScale.simmap(tree_list[[1]], add.noise = c(0.3,0.3))) ## End(Not run)
Normalizes the loess smoothing for the Markov KDE.
normalize_KDE(tree.discr, Maps.mean.loess)normalize_KDE(tree.discr, Maps.mean.loess)
tree.discr |
simmap or phylo object. A discretized tree using the 'discr_Simmap' function. |
Maps.mean.loess |
list. A list with the loess smoothing calculated for each edge using the 'loess_smoothing_KDE' function. |
A list with the normalized loess smoothing calculated for each edge.
Sergei Tarasov
data("hym_kde", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get non-normalized and normalized edge KDE data. Edge_KDE <- hym_kde$head # Calculate smoothing of edge KDE data. Edge_KDE$Maps.mean.loess <- suppressWarnings(loess_smoothing_KDE(tree_discr, Edge_KDE)) # Normalize smoothing edge KDE data. Edge_KDE$Maps.mean.loess.norm <- normalize_KDE(tree_discr, Edge_KDE$Maps.mean.loess) # Check smoothing of KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.loess[[5]]data("hym_kde", "hym_tree") # Get reference tree. tree_discr <- discr_Simmap(hym_tree, res = 4000) # Get non-normalized and normalized edge KDE data. Edge_KDE <- hym_kde$head # Calculate smoothing of edge KDE data. Edge_KDE$Maps.mean.loess <- suppressWarnings(loess_smoothing_KDE(tree_discr, Edge_KDE)) # Normalize smoothing edge KDE data. Edge_KDE$Maps.mean.loess.norm <- normalize_KDE(tree_discr, Edge_KDE$Maps.mean.loess) # Check smoothing of KDE data for non-normalized mean rates from an arbitrary branch. Edge_KDE$Maps.mean.loess[[5]]
Wrapper function to perform the final paramo stacking of maps for a set of anatomy ontology terms.
paramo(rac_query, tree.list, ntrees)paramo(rac_query, tree.list, ntrees)
rac_query |
character list. Named list obtained from the RAC_query function. |
tree.list |
multiSimmap or multiPhylo object. Named list with stochastic character maps. |
ntrees |
integer. Number of trees to stack. |
A list of stacked stochastic character maps.
Diego S. Porto
char_info <- hym_annot[1:2] # Query for three anatomical regions. terms <- c("head", "mesosoma", "metasoma") query <- RAC_query(char_info, HAO, terms) # Select the first three characters for each anatomical region. query <- lapply(query, function(x) x[1:3]) # Subset the list of multiple maps. tree_list <- hym_stm[unname(unlist(query))] tree_list <- lapply(tree_list, function(x) discr_Simmap_all(x, res = 100)) tree_list_amalg <- paramo(query, tree_list, ntrees = 100) tree_list_amalg <- lapply(tree_list_amalg, function(x) do.call(c,x) ) # Get one sample of map from head. stm_hd <- tree_list_amalg$head[[1]] # Get one sample of map from mesosoma. stm_ms <- tree_list_amalg$mesosoma[[1]] # Get one sample of map from metasoma. stm_mt <- tree_list_amalg$metasoma[[1]] # Plot one amalgamated stochastic map from each anatomical region. ## Not run: phytools::plotSimmap(stm_hd, get_rough_state_cols(stm_hd), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) phytools::plotSimmap(stm_ms, get_rough_state_cols(stm_ms), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) phytools::plotSimmap(stm_mt, get_rough_state_cols(stm_mt), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) ## End(Not run)char_info <- hym_annot[1:2] # Query for three anatomical regions. terms <- c("head", "mesosoma", "metasoma") query <- RAC_query(char_info, HAO, terms) # Select the first three characters for each anatomical region. query <- lapply(query, function(x) x[1:3]) # Subset the list of multiple maps. tree_list <- hym_stm[unname(unlist(query))] tree_list <- lapply(tree_list, function(x) discr_Simmap_all(x, res = 100)) tree_list_amalg <- paramo(query, tree_list, ntrees = 100) tree_list_amalg <- lapply(tree_list_amalg, function(x) do.call(c,x) ) # Get one sample of map from head. stm_hd <- tree_list_amalg$head[[1]] # Get one sample of map from mesosoma. stm_ms <- tree_list_amalg$mesosoma[[1]] # Get one sample of map from metasoma. stm_mt <- tree_list_amalg$metasoma[[1]] # Plot one amalgamated stochastic map from each anatomical region. ## Not run: phytools::plotSimmap(stm_hd, get_rough_state_cols(stm_hd), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) phytools::plotSimmap(stm_ms, get_rough_state_cols(stm_ms), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) phytools::plotSimmap(stm_mt, get_rough_state_cols(stm_mt), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) ## End(Not run)
Performs the final stacking of maps for a set of stochastic character maps stored in a list.
paramo.list(cc, tree.list, ntrees = 1)paramo.list(cc, tree.list, ntrees = 1)
cc |
character. Characters IDs to stack. |
tree.list |
multiSimmap or multiPhylo object. Named list with stochastic character maps. |
ntrees |
integer. Number of trees to stack. |
A list of stacked stochastic character maps.
Sergei Tarasov
data("hym_stm") # Select the first five characters. tree_list <- hym_stm[1:5] tree_list <- lapply(tree_list, function(x) discr_Simmap_all(x, res = 100)) tree_list_amalg <- paramo.list(names(tree_list), tree_list, ntrees = 100) tree_list_amalg <- do.call(c, tree_list_amalg) # Plot one amalgamated stochastic map. ## Not run: phytools::plotSimmap(tree_list_amalg[[1]], get_rough_state_cols(tree_list_amalg[[1]]), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) ## End(Not run)data("hym_stm") # Select the first five characters. tree_list <- hym_stm[1:5] tree_list <- lapply(tree_list, function(x) discr_Simmap_all(x, res = 100)) tree_list_amalg <- paramo.list(names(tree_list), tree_list, ntrees = 100) tree_list_amalg <- do.call(c, tree_list_amalg) # Plot one amalgamated stochastic map. ## Not run: phytools::plotSimmap(tree_list_amalg[[1]], get_rough_state_cols(tree_list_amalg[[1]]), lwd = 3, pts = F,ftype = "off", ylim = c(0,100)) ## End(Not run)
Calculates the hamming distance between states for a given path.
path_hamming(Path)path_hamming(Path)
Path |
data.frame. A tibble with state information about a given path (from root to a given node). The tibble is the output obtained from the get_states_path function. The columns give information on state changes, time spent on each state, and edge IDs. |
The input tibble with two additional columns giving information on absolute and normalized hamming distances.
Sergei Tarasov
Internal function. Not exported.
Calculates hamming distances for all paths in a given discretized tree.
path_hamming_over_all_edges(tree.merge)path_hamming_over_all_edges(tree.merge)
tree.merge |
simmap object. A discretized simmap using the 'discr_Simmap' function where identical state bins were merged using the 'merge_tree_cat' function. |
A tibble with information on state changes, time spent on each state, edge IDs, absolute and normalized hamming distances for all edges in a tree.
Sergei Tarasov
data("hym_stm_amalg") # Get one sample of stochastic maps from head. tree <- hym_stm_amalg$head[[1]] tree <- merge_tree_cat(tree) # Calculate hamming distances. ph <- suppressWarnings(path_hamming_over_all_edges(tree)) phdata("hym_stm_amalg") # Get one sample of stochastic maps from head. tree <- hym_stm_amalg$head[[1]] tree <- merge_tree_cat(tree) # Calculate hamming distances. ph <- suppressWarnings(path_hamming_over_all_edges(tree)) ph
Calculates hamming distances for all paths in each discretized tree of a list.
path_hamming_over_trees_KDE(tree.list)path_hamming_over_trees_KDE(tree.list)
tree.list |
multiSimmap object. |
A tibble with information on state changes, time spent on each state, edge IDs, absolute and normalized hamming distances for all edges and all trees in a list.
Sergei Tarasov
data("hym_stm_amalg") # Get ten samples of stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] tree_list <- merge_tree_cat_list(tree_list) # Calculate hamming distances. ph <- suppressWarnings(path_hamming_over_trees_KDE(tree_list)) phdata("hym_stm_amalg") # Get ten samples of stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] tree_list <- merge_tree_cat_list(tree_list) # Calculate hamming distances. ph <- suppressWarnings(path_hamming_over_trees_KDE(tree_list)) ph
Wrapper function for applying the pNHPP method.
pNHPP( stm_amalg, tree = tree, res = res, add.psd = TRUE, band.width = c("bw.nrd0", "bw.nrd0", "bw.ucv", "bw.bcv", "bw.SJ"), lambda.post.stat = "Mean" )pNHPP( stm_amalg, tree = tree, res = res, add.psd = TRUE, band.width = c("bw.nrd0", "bw.nrd0", "bw.ucv", "bw.bcv", "bw.SJ"), lambda.post.stat = "Mean" )
stm_amalg |
multiSimmap object. A list of amalgamated stochastic maps. |
tree |
simmap or phylo object. A reference tree for discretization. |
res |
integer. A resolution value for the discretization of tree edges. |
add.psd |
logical. Whether to add pseudodata or not in the 'make_data_NHPP_KDE_Markov_kernel' function. Default is TRUE. |
band.width |
character. Bandwidth selectors for the KDE in the 'estimate_band_W' function. |
lambda.post.stat |
character. A value with the statistic to be used in the 'make_postPois_KDE' function. |
A list with the estimated Markov KDE for all edges, the contMap object for plotting the NHPP, and the information necessary for making the edgeplot.
Diego Porto
## Not run: # Load data. data("hym_stm", "hym_stm_amalg") # Get a reference tree for discretization. tree <- hym_stm[[1]][[1]] # Get ten samples of stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] # Run the pNHPP method. nhpp_test <- pNHPP(tree_list, tree, res = 500, add.psd = TRUE, band.width = 'bw.nrd', lambda.post.stat = 'Mean') ## End(Not run)## Not run: # Load data. data("hym_stm", "hym_stm_amalg") # Get a reference tree for discretization. tree <- hym_stm[[1]][[1]] # Get ten samples of stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] # Run the pNHPP method. nhpp_test <- pNHPP(tree_list, tree, res = 500, add.psd = TRUE, band.width = 'bw.nrd', lambda.post.stat = 'Mean') ## End(Not run)
Calculates the required statitics for the posterior distribution of number of state changes across all branches of all trees.
posterior_lambda_KDE(tree.list)posterior_lambda_KDE(tree.list)
tree.list |
multiSimmap object. |
A list with mean ($Mean), standard deviation ($SD), and 95HPD interval ($Q_2.5 and $Q_97.5) calculated for the posterior distribution.
Sergei Tarasov
data("hym_stm_amalg") # Get a sample of ten stochastic maps from head. tree_list <- hym_stm_amalg$head tree_list <- merge_tree_cat_list(tree_list[1:10]) # Calculate posterior poisson statistics. posterior_lambda_KDE(tree_list)data("hym_stm_amalg") # Get a sample of ten stochastic maps from head. tree_list <- hym_stm_amalg$head tree_list <- merge_tree_cat_list(tree_list[1:10]) # Calculate posterior poisson statistics. posterior_lambda_KDE(tree_list)
Simulates a distribution of number of state changes across all branches of all trees
posterior_lambda_KDE_Distr(tree.list, n.sim = 10, BR.name)posterior_lambda_KDE_Distr(tree.list, n.sim = 10, BR.name)
tree.list |
multiSimmap object. |
n.sim |
integer. Number of simulations. |
BR.name |
character. A label name for the anatomical region. |
A tibble with the simulated distribution.
Sergei Tarasov
data("hym_stm_amalg") # Get a sample of ten stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] tree_list <- merge_tree_cat_list(tree_list) # Simulate posterior poisson distribution. posterior_lambda_KDE_Distr(tree_list, n.sim = 10, BR.name = "head")data("hym_stm_amalg") # Get a sample of ten stochastic maps from head. tree_list <- hym_stm_amalg$head[1:10] tree_list <- merge_tree_cat_list(tree_list) # Simulate posterior poisson distribution. posterior_lambda_KDE_Distr(tree_list, n.sim = 10, BR.name = "head")
Returns a named list aggregating characters under a specified set of terms (e.g., body regions).
RAC_query(char_info, ONT, terms)RAC_query(char_info, ONT, terms)
char_info |
data.frame. A data.frame with two columns: the first column with character IDs and the second column with ontology IDs. |
ONT |
ontology_index object. |
terms |
character. The set of terms to aggregate characters. |
A named list with character groups.
Sergei Tarasov
data("HAO", "hym_annot") char_info <- hym_annot[1:2] # Query for three anatomical regions. terms <- c("head", "mesosoma", "metasoma") query <- RAC_query(char_info, HAO, terms)data("HAO", "hym_annot") char_info <- hym_annot[1:2] # Query for three anatomical regions. terms <- c("head", "mesosoma", "metasoma") query <- RAC_query(char_info, HAO, terms)
Imports stochastic character maps file from RevBayes into R.
read_Simmap_Rev(file, start = 1, end = 1, save = NULL)read_Simmap_Rev(file, start = 1, end = 1, save = NULL)
file |
character. Path to the RevBayes file. |
start |
integer. First tree of the sample to start reading the RevBayes file. |
end |
integer. Last tree of the sample to finish reading the RevBayes file. |
save |
character. Name to save output file. |
A tree in 'phylip' format.
Sergei Tarasov
## Not run: stm <- read_Simmap_Rev("revbayes.stm", start = 200, end = 500, save = NULL) stm <- phytools::read.simmap(text = stm, format = "phylip") phytools::plotSimmap(stm[[1]]) ## End(Not run)## Not run: stm <- read_Simmap_Rev("revbayes.stm", start = 200, end = 500, save = NULL) stm <- phytools::read.simmap(text = stm, format = "phylip") phytools::plotSimmap(stm[[1]]) ## End(Not run)
Internal function. Not exported.
stack_stm(stm.list)stack_stm(stm.list)
Sergei Tarasov
Internal function. Not exported.
stack2(x, y)stack2(x, y)
Sergei Tarasov