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‎.Rbuildignore

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@@ -36,3 +36,6 @@ README[.]md
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inst/CODECOV_EXCLUSIONS
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^docker$
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^\.github$
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# Wont include it for now
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^R/aphylo_hier\.R

‎DESCRIPTION

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@@ -2,8 +2,8 @@ Package: aphylo
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Title: Statistical Inference and Prediction of Annotations in Phylogenetic Trees
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Authors@R: person("George", "Vega Yon", role=c("aut","cre"),
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email="g.vegayon@gmail.com", comment = c(ORCID = "0000-0002-3171-0844"))
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Version: 0.2-0
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Date: 2021-11-16
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Version: 0.2-1
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Date: 2022-01-01
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Description: aphylo implements a parsimonious evolutionary model to analyze and
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predict gene-functional annotations in phylogenetic trees as described in Vega
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Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. With a focus on
@@ -19,7 +19,7 @@ Imports: Rcpp (>= 0.12.1), Matrix, methods, coda, fmcmc,
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Suggests: covr, knitr, tinytest, AUC, rmarkdown,
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VignetteBuilder: knitr
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LinkingTo: Rcpp
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RoxygenNote: 7.1.1
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RoxygenNote: 7.1.2
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Encoding: UTF-8
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URL: https://github.com/USCbiostats/aphylo
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BugReports: https://github.com/USCbiostats/aphylo/issues

‎NAMESPACE

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@@ -87,7 +87,6 @@ export(accuracy_sifter)
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export(aphylo_cv)
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export(aphylo_formula)
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export(aphylo_from_data_frame)
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export(aphylo_hier)
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export(aphylo_mcmc)
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export(aphylo_mle)
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export(auc)

‎NEWS.md

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# Changes in aphylo version 0.3-0
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* Preparing for CRAN.
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* Added examples to every function.
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* Fixed bug in `auc()`: Was still using NAs in the predicted values.
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* Fixed bug in `aphylo_formulae()`: Passing subsetted trees, e.g., `atree[[1]]`,
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now works.
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# Changes in aphylo version 0.1.99
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* First version

‎R/RcppExports.R

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@@ -83,6 +83,13 @@ Tree_get_ann <- function(phy) {
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#' - `auc` A numeric value. Area Under the Curve.
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#' - `cutoffs` A vector of length `nc` with the cutoffs used.
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#' @export
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#' @examples
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#' set.seed(8381)
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#' x <- rdrop_annotations(raphylo(50), .3)
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#' ans <- aphylo_mcmc(x ~ mu_d + mu_s + Pi)
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#' ans_auc <- auc(predict(ans, loo = TRUE), x[,1,drop=TRUE])
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#' print(ans_auc)
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#' plot(ans_auc)
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auc <- function(pred, labels, nc = 200L, nine_na = TRUE) {
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.Call(`_aphylo_auc`, pred, labels, nc, nine_na)
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}

‎R/accuracy_sifter.R

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@@ -24,6 +24,13 @@
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#'
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#' When `highlight = ""`, no highlight is done.
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#'
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#' @examples
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#' set.seed(81231)
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#' atree <- raphylo(100, psi = c(0,0), P = 3)
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#' ans <- aphylo_mcmc(atree ~ mu_d + mu_s + Pi)
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#'
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#' accuracy_sifter(ans)
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#'
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#' @export
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accuracy_sifter <- function(
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pred,

‎R/aphylo-ape-info.R

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@@ -7,6 +7,17 @@
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#' @name ape-methods
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#' @family information
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#' @importFrom ape as.phylo plot.phylo Nedge Ntip Nnode
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#' @examples
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#' set.seed(12312)
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#' atree <- raphylo(50, P = 2)
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#' Nnode(atree)
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#' Ntip(atree)
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#' Nedge(atree)
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#'
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#' multitree <- rmultiAphylo(10, 50, P = 2)
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#' Nnode(multitree)
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#' Ntip(multitree)
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#' Nedge(multitree)
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NULL
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#' @method Nedge aphylo

‎R/aphylo_hier.R

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@@ -12,7 +12,17 @@
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#' @family parameter estimation
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#' @details The parameters `priors`, `check_informative`, and `reduced_pseq`
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#' are silently ignored in this function.
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#' @export
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# #' @export # NOT FOR
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#' @noRd
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#' @examples
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#' set.seed(123189)
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#' aphylos <- rmultiAphylo(10, 50, P = 2)
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#'
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#' ans <- aphylo_hier(
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#' aphylos ~ mu_d + mu_s + Pi,
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#' params = c(0.9, .5, .1, .05, .5),
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#' classes = rep(c(1,2), 5)
25+
#' )
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aphylo_hier <- function(
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model,
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params,
@@ -22,9 +32,8 @@ aphylo_hier <- function(
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hyper_params = NULL,
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env = parent.frame(),
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verbose = TRUE,
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multicore = FALSE,
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nchains = 1L,
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use_optim = TRUE,
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control = ls(),
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priors = NULL,
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check_informative = NULL,
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reduced_pseq = NULL
@@ -238,3 +247,4 @@ aphylo_hier <- function(
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# return(ans)
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}
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‎R/aphylo_mcmc.R

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@@ -2,11 +2,11 @@
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#' @rdname aphylo_mcmc
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#' @details `APHYLO_DEFAULT_MCMC_CONTROL` lists the default values for the MCMC
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#' estimation:
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#' - `nsteps`: \Sexpr{aphylo::APHYLO_DEFAULT_MCMC_CONTROL$nsteps}
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#' - `burnin`: \Sexpr{aphylo::APHYLO_DEFAULT_MCMC_CONTROL$burnin}
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#' - `thin` : \Sexpr{aphylo::APHYLO_DEFAULT_MCMC_CONTROL$thin}
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#' - `nchains` : \Sexpr{aphylo::APHYLO_DEFAULT_MCMC_CONTROL$nchains}
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#' - `multicore` : \Sexpr{aphylo::APHYLO_DEFAULT_MCMC_CONTROL$multicore}
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#' - `nsteps`: `1e4L`
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#' - `burnin`: `5e3L`
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#' - `thin` : `10L`
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#' - `nchains` : `2L`
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#' - `multicore` : `FALSE`
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#' - `conv_checker` : `fmcmc::convergence_auto(5e3)`
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#'
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#' For more information about the MCMC estimation process, see [fmcmc::MCMC()].
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#' tree <- sim_tree(200)
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#'
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#' # Simulating functions
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#' dat <- raphylo(
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#' atree <- raphylo(
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#' tree = tree,
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#' psi = c(.01, .03),
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#' mu_d = c(.05, .02),

‎R/balance.R

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#' Functional banlance of a tree
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#' Functional balance of a tree
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#'
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#' This function computes the distance between .5 and the observed proportion
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#' of ones for each function in a tree.

‎R/cv.R

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#' with argument `loo = TRUE`.
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#'
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#' @export
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#' @examples
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#' # It takes about two minutes to run this example
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#' \dontrun{
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#'
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#' set.seed(123)
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#' atrees <- rmultiAphylo(10, 10, P = 1)
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#'
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#' cv_multi <- aphylo_cv(atrees ~ mu_d + mu_s + Pi)
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#' cv_single <- aphylo_cv(atrees[[1]] ~ mu_d + mu_s + Pi)
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#'
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#' }
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aphylo_cv <- function(...) UseMethod("aphylo_cv")
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#' @export

‎R/estimation.R

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#' \item{varcovar}{A matrix of size 5*5. The estimated covariance matrix.}
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#'
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#' @name aphylo_estimates
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#' @examples
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#' set.seed(7881)
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#' atree <- raphylo(40, P = 2)
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#' res <- aphylo_mcmc(atree ~ mu_d + mu_s + Pi)
51+
#'
52+
#' print(res)
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#' coef(res)
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#' vcov(res)
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#' plot(res)
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NULL
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new_aphylo_estimates <- function(

‎R/formulas.R

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#' - `Pi`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$Pi}
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#'
102102
APHYLO_PARAM_DEFAULT <- structure(
103-
.Data = c(.1, .1, .9, .9, .1, .1, .9, .9, .1),
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.Data = c(.1, .05, .9, .5, .1, .05, 1.0, 1.0, .5),
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names = APHYLO_PARAM_NAMES
105105
)
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392392
model_call <- aphylo_call(params, priors)
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# Is the LHS an aphylo object?
395-
if (!exists(as.character(val[[2]]), envir = env))
396-
stop("The object -", as.character(val[[2]]), "- can't be found.", call. = FALSE)
395+
LHS <- tryCatch(eval(val[[2]], envir = env), error = function(e) e)
396+
if (inherits(LHS, "error"))
397+
stop("The object -", deparse(val[[2]]), "- can't be found.", call. = FALSE)
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398-
LHS <- eval(val[[2]], envir = env)
399399
if (!is.aphylo(LHS) && !is.multiAphylo(LHS) && !inherits(LHS, "aphylo_pruner"))
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stop(
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"The LHS of the equation should be either a list or a single ",

‎R/indexing.R

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#' the [aphylo] object. Whenever `i` is specified, then aphylo returns the corresponding
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#' annotations.
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#' @name aphylo-index
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#' @examples
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#' set.seed(12312)
13+
#' atree <- raphylo(50, P = 4)
14+
#' atree[1:10,]
15+
#' atree[,2:3]
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#' atree[, 2:3, drop = TRUE]
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#'
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#'
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NULL
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1321
#' @export

‎R/plot.R

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#' @return In the case of `plot.aphylo`, `NULL`.
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#' @family aphylo methods
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#' @export
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#' @examples
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#' set.seed(7172)
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#' atree <- raphylo(20)
29+
#' plot(atree)
2630
plot.aphylo <- function(
2731
x,
2832
y = NULL,

‎R/prediction_score.R

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#' If `include.labels = NULL` and `ncol(x$expected) > 40`,
352352
#' then `include.labels=FALSE` by default.
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#' @aliases plot-prediction
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#' @examples
355+
#' set.seed(8783)
356+
#' atree <- raphylo(29)
357+
#' ans <- aphylo_mle(atree ~ mu_d + mu_s + Pi)
358+
#' pred_s <- prediction_score(ans)
359+
#'
360+
#' pred_s
361+
#' plot(pred_s)
354362
plot.aphylo_prediction_score <- function(
355363
x,
356364
y = NULL,

‎R/prune.R

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#'
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#' @examples
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#' set.seed(1)
17-
#' x <- raphylo(10)
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#' x <- raphylo(20)
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#' pruner <- new_aphylo_pruner(x)
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#'
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#' # Computing loglike
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#' Pi = .05,
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#' eta = c(.90, .80)
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#' )
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#'
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#' dist2root(pruner)
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#' get_postorder(pruner)
2932
#' @name new_aphylo_pruner
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NULL
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‎R/read_nhx.R

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#' )
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#' @export
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#' @references "NHX - New Hampshire eXtended \[version 2.0\]",
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#' \url{https://sites.google.com/site/cmzmasek/home/software/forester/nhx}
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#' \url{https://en.wikipedia.org/wiki/Newick_format#New_Hampshire_X_format}
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#' @family reading
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read_nhx <- function(fn, txt) {
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‎R/read_sifter.R

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#' @param family_id Character scalar. Name of the family
8080
#' @param file Character scalar passed to [cat].
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#' @export
82+
#' @examples
83+
#' set.seed(882)
84+
#' atree <- raphylo(5)
85+
#' write_pli(
86+
#' family_id = "a family",
87+
#' protein_name = atree$tree$tip.label,
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#' protein_number = 1:Ntip(atree),
89+
#' go_number = "GO:123123123123"
90+
#' )
91+
#' # Possible outcome:
92+
#' #<?xml version="1.0"?>
93+
#' #<Family>
94+
#' # <FamilyID>a family</FamilyID>
95+
#' # <Protein>
96+
#' # <ProteinName>1</ProteinName>
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#' # <ProteinNumber>1</ProteinNumber>
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#' # <GONumber>[GO:123123123123]</GONumber>
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#' # <MOC>[EXP]</MOC>
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#' # </Protein>
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#' # <Protein>
102+
#' # <ProteinName>2</ProteinName>
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#' # <ProteinNumber>2</ProteinNumber>
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#' # <GONumber>[GO:123123123123]</GONumber>
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#' # <MOC>[EXP]</MOC>
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#' # </Protein>
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#' # <Protein>
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#' # <ProteinName>3</ProteinName>
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#' # <ProteinNumber>3</ProteinNumber>
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#' # <GONumber>[GO:123123123123]</GONumber>
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#' # <MOC>[EXP]</MOC>
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#' # </Protein>
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#' # <Protein>
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#' # <ProteinName>4</ProteinName>
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#' # <ProteinNumber>4</ProteinNumber>
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#' # <GONumber>[GO:123123123123]</GONumber>
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#' # <MOC>[EXP]</MOC>
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#' # </Protein>
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#' # <Protein>
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#' # <ProteinName>5</ProteinName>
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#' # <ProteinNumber>5</ProteinNumber>
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#' # <GONumber>[GO:123123123123]</GONumber>
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#' # <MOC>[EXP]</MOC>
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#' # </Protein>
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#' #</Family>
82126
write_pli <- function(
83127
family_id, protein_name, protein_number, go_number, moc = "EXP",
84128
file = "") {

‎R/simulation.R

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node.type = function(n) sample.int(2, size = n, replace = TRUE, prob = c(.2, .8)) - 1,
274274
P = 1L,
275275
psi = c(.05, .05),
276-
mu_d = c(.90, .90),
277-
mu_s = c(.10, .05),
276+
mu_d = c(.90, .50),
277+
mu_s = c(.05, .02),
278278
eta = c(1.0, 1.0),
279279
Pi = 0.2,
280280
informative = getOption("aphylo_informative", FALSE),

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