richelbilderbeek
commented
7 years ago I guess you are right. testit must have shifted from Suggests to Requires. Thanks!
Closed SimonGreenhill closed 7 years ago
richelbilderbeek
commented
7 years ago I guess you are right. testit must have shifted from Suggests to Requires. Thanks!
lintr-bot
commented
7 years ago inst/doc/trees_with_root_edge.R:8:7: style: Variable or function name should be snake_case.
tree1$edge.length <- tree1$edge.length / tree1$edge.length[1]
^~~~~~~~~~~inst/doc/trees_with_root_edge.R:12:7: style: Variable or function name should be snake_case.
tree2$root.edge <- 1
^~~~~~~~~inst/doc/walkthrough.R:53:12: style: Remove spaces before the left parenthesis in a function call.
return (t)
^inst/doc/walkthrough.R:58:12: style: Remove spaces before the left parenthesis in a function call.
return ( rexp(n = 1, rate = 10) )
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return ( rexp(n = 1, rate = 10) )
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return ( rexp(n = 1, rate = 10) )
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return( dexp( val[1], rate = 10) )
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return( dexp( val[1], rate = 10) )
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return( dexp( val[1], rate = 10) )
^inst/doc/walkthrough.R:74:1: style: Variable or function name should be snake_case.
A <- abc_smc_nltt(
^inst/doc/walkthrough.R:85:1: style: Variable or function name should be snake_case.
LL_B <- function(params, phy) {
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ML <- stats::optimize(f = fun, interval = c(0, 1))
^~inst/doc/walkthrough.R:108:1: style: Variable or function name should be snake_case.
B <- mcmc_nltt(obs, LL_B, parameters = c(0.5),
^inst/doc/walkthrough.R:114:1: style: Variable or function name should be snake_case.
B.mcmc <- coda::as.mcmc(B)
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# @param current vector Current parameter combination for which we are determining the weight
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# @param sigma scalar standard deviation of the perturbation
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# @param prior_density_function function Function to calculate the prior probability of a set of parameters
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for ( i in seq_along( particles)) {
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for ( i in seq_along( particles)) {
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for (j in seq_along( current)) {
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return (numerator / sum( vals))
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return (numerator / sum( vals))
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# @brief apply the ABC routine used in our Methods in Ecology and Evolution Paper
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# @param statistics vector A vector containing functions that take a tree as an argument and return a single scalar value (the statistic).
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# @param simulation_function vector A function that implements the diversification model and returns an object of class phylo
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# @param init_epsilon_values vector A vector containing the initial threshold values for the summary statistics from the vector statistics.
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# @param prior_generating_function function Function to generate parameters from the prior distribution of these parameters (e.g. a function returning lambda and mu in case of the birth-death model)
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# @param prior_density_function function Function to calculate the prior probability of a set of parameters (should match prior_generating_function in shape)
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# @param number_of_particles scalar Number of particles to be used per iteration of the ABC-SMC algorithm.
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# @param sigma scalar Standard deviation of the perturbance distribution (perturbance distribution is a gaussian with mean 0).
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# @param stop_rate scalar If the acceptance rate drops below \code{stopRate}, stop the ABC-SMC algorithm and assume convergence.
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# @return matrix A matrix with n columns, where n is the number of parameters you are trying to estimate.
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#' A function to perform Approximate Bayesian Computation within an Sequential Markov Chain (ABC-SMC), for diversification analysis of phylogenetic trees.
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#' @description This function performs ABC-SMC as described in Toni 2009 for given diversification model, provided a phylogenetic tree. ABC-SMC is not limited to only using the normalized LTT as statistic.
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#' @param tree an object of class \code{"phylo"}; the tree upon which we want to fit our diversification model
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#' @param statistics A vector containing functions that take a tree as an argument and return a single scalar value (the statistic).
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#' @param simulation_function A function that implements the diversification model and returns an object of class \code{"phylo"}.
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#' @param init_epsilon_values A vector containing the initial threshold values for the summary statistics from the vector \code{statistics}.
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#' @param prior_generating_function Function to generate parameters from the prior distribution of these parameters (e.g. a function returning lambda and mu in case of the birth-death model)
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#' @param prior_density_function Function to calculate the prior probability of a set of parameters.
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#' @param number_of_particles Number of particles to be used per iteration of the ABC-SMC algorithm.
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#' @param sigma Standard deviation of the perturbance distribution (perturbance distribution is a gaussian with mean 0).
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#' @param stop_rate If the acceptance rate drops below \code{stopRate}, stop the ABC-SMC algorithm and assume convergence.
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#' @return A matrix with \code{n} columns, where \code{n} is the number of parameters you are trying to estimate.
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#' @references Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M.P.H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187-202.
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/ABC_SMC.R:161:22: style: Do not place spaces around code in parentheses or square brackets.
new_params <- list( c( seq_along(parameters)))
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new_params <- list( c( seq_along(parameters)))
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previous_params <- list( c( seq_along(parameters)))
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previous_params <- list( c( seq_along(parameters)))
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for (i in 1:50 ) {
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new_params <- list( c( seq_along(parameters))) #clear new params
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new_params <- list( c( seq_along(parameters))) #clear new params
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if ( abs(stats[k] - obs_statistics[k]) > epsilon[i, k] ) {
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if ( abs(stats[k] - obs_statistics[k]) > epsilon[i, k] ) {
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if ( accept ) {
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if ( accept ) {
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if ( (number_accepted) %%
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if ( (number_accepted / tried) < stop_rate) {
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for (m in seq_along( parameters)) {
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add <- c( add, previous_params[[k]][m])
^R/ABC_SMC.R:279:10: style: Remove spaces before the left parenthesis in a function call.
return (output)
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# @brief Estimate the likelihood of a given tree, provided a likelihood function, using a Monte Carlo Markov Chain
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# @param likelihood_function function Function that calculates the likelihood of our diversification model, given the tree.
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# function should be of the format function(parameters, phy).
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# @param parameters vector Initial parameters to start the chain.
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# @param logtransforms scalar Whether to perform jumps on logtransformed parameters (TRUE) or not (FALSE)
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# @param burnin scalar Length of the burnin, default is 30% of iterations
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# @param sigma scalar Standard deviation of the jumping distribution, which is N(0, sigma).
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# @return mcmc An MCMC object, as used by the package "coda".
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chain <- array(dim = c( floor( iterations / thinning) + 1,
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chain <- array(dim = c( floor( iterations / thinning) + 1,
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for ( j in seq_along(parameters) ) {
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for ( j in seq_along(parameters) ) {
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if ( logtransforms[j] == TRUE ) {
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if ( logtransforms[j] == TRUE ) {
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if ( parameters[j] == 0) {
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if ( is.finite(new_pp) &&
^R/ABC_SMC.R:355:59: style: Do not place spaces around code in parentheses or square brackets.
new_pp - pp + hr > log(stats::runif(1, 0, 1)) ) {
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if ( is.finite(new_pp) &&
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new_pp - pp + hr > log(stats::runif(1, 0, 1)) ) {
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if ( (i) %% ( (iterations - burnin) / print_frequency) == 0) {
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if ( (i) %% ( (iterations - burnin) / print_frequency) == 0) {
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if ( (i - burnin) %% thinning == 0 ) {
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if ( (i - burnin) %% thinning == 0 ) {
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chain[ (i - burnin) / thinning + 1, ] <- parameters
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return( coda::as.mcmc(chain) )
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return( coda::as.mcmc(chain) )
^R/get_average_nltt_matrix.R:3:1: style: Lines should not be more than 80 characters.
#' @param phylogenies the phylogenies, supplied as either a list or a multiPhylo object, where the phylogenies are of type 'phylo'
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#' @param dt The timestep resolution, where 1/dt is the number of points evaluated
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#' @return A matrix of timepoints with the average number of (normalized) lineages through (normalized) time
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/get_braching_times.R:15:12: style: Remove spaces before the left parenthesis in a function call.
return (c(brts[1] + phylogeny$root.edge, brts))
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return (brts)
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return (1:length(phylogeny$tip.label))
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return (2:length(phylogeny$tip.label))
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#' @param phylogenies the phylogenies, supplied as either a list or a multiPhylo object, where the phylogenies are of type 'phylo'
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#' @param dt The timestep resolution, where 1/dt is the number of points evaluated
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#' @return A dataframe of timepoints with the nLTT value of each phylogeny in time
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return (brts)
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return (ns)
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#' @return (scalar) normalized Lineage-Through-Time difference between tree1 & tree2
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return (nLTT::nltt_diff( tree1, tree2, distance_method, ignore_stem))
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return (nLTT::nltt_diff( tree1, tree2, distance_method, ignore_stem))
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#' @param lineages2 the number of lineages, usually one to the number of lineages
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#' \item{"ago: "}{the branching times are postive, as these are in time units ago}
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#' \item{"since: "}{the branching times are negative, as these are in time units since present}
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testit::assert(length(b_times ) == length(lineages ))
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testit::assert(length(b_times ) == length(lineages ))
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testit::assert(length(b_times ) == length(lineages ))
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testit::assert(length(b_times ) == length(lineages ))
^R/nLTTDiff.R:80:3: style: Variable or function name should be snake_case.
b_times_N <- 1.0 - b_times / min(b_times) #normalize branching times
^~~~~~~~~R/nLTTDiff.R:81:3: style: Variable or function name should be snake_case.
lineages_N <- lineages / max(lineages) #normalize lineages
^~~~~~~~~~R/nLTTDiff.R:86:3: style: Variable or function name should be snake_case.
b_times2_N <- 1 - b_times2 / min(b_times2) #normalize branching times
^~~~~~~~~~R/nLTTDiff.R:87:3: style: Variable or function name should be snake_case.
lineages2_N <- lineages2 / max(lineages2) #normalize lineages
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return (nltt_diff_exact_norm_brts(
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#' @param lineages_N the number of lineages, usually one to the number of lineages
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#' @param lineages2_N the number of lineages, usually one to the number of lineages
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b_times_N,
^~~~~~~~~R/nLTTDiff.R:112:3: style: Variable or function name should be snake_case.
lineages_N,
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b_times2_N,
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lineages2_N,
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diff <- diff + dt * abs( lins1 - lins2) #update the difference
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diff <- diff + dt * ( lins1 - lins2) * ( lins1 - lins2)
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diff <- diff + dt * ( lins1 - lins2) * ( lins1 - lins2)
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return ( diff)
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return ( diff)
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#' Calculates the exact difference between the lineage through time curves of tree1 & tree2 (normalized in time and for the number of lineages)
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#' @return (scalar) normalized Lineage-Through-Time difference between tree1 & tree2
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return (
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# @param distance_method string Method to calculate the difference, either absolute, or squared
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# @return scalar normalized Lineage-Through-Time difference between tree1 & tree2
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#' This function takes two ultrametric phylogenetic trees, calculates the normalized Lineage-Through-Time statistic for both trees and then calculates the difference between the two statistics.
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#' @title Calculate the difference between two normalized Lineage-Through-Time curves, given two phylogenetic trees.
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#' @param distance_method Chosen measurement of distance between the two nLTT curves, options are (case sensitive):\cr
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if ( distance_method != "abs" && distance_method != "squ") {
^R/nLTTDiff.R:299:27: style: Do not place spaces around code in parentheses or square brackets.
diff <- nLTT::nltt_diff( tree1, tree2, distance_method, ignore_stem)
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return (diff)
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# @param distance_method string Method to calculate the difference, either absolute, or squared
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# @param ignore_stem logical Should the phylogeny its stem be ignored?
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# @return scalar normalized Lineage-Through-Time difference between tree1 & tree2
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#' Calculate the exact difference between two normalized Lineage-Through-Time curves, given two phylogenetic trees.
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#' @description This function takes two ultrametric phylogenetic trees, calculates the normalized Lineage-Through-Time statistic for both trees and then calculates the exact difference between the two statistics. Whereas the function \code{nLTTstat} uses an approximation to calculate the difference (which is faster for large trees), the function \code{nLTTstat_exact} calculates the exact difference, and should generally be preferred. Although the estimates are highly similar, \code{nLTTstat_exact} tends to return slightly higher values.
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#' Chosen measurement of distance between the two nLTT curves, options are (case sensitive):\cr
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/nLTTDiff.R:364:33: style: Do not place spaces around code in parentheses or square brackets.
diff <- nLTT::nltt_diff_exact( tree1, tree2, distance_method, ignore_stem)
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return (diff)
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#' @param dt The timestep resolution, where 1/dt is the number of points evaluated
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#' @param replot If false, start a clean plot. If true, plot the new data over the current
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#' This function uses a modified version of the ltt.plot function from \code{"ape"} to plot the normalized number of lineages through normalized time, where the number of lineages is normalized by dividing by the number of tips of the tree, and the time is normalized by the total time between the most common recent ancestor and the present, such that t(MRCA) = 0 & t(present) = 1.
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/plot_nLTT.R:17:1: style: Lines should not be more than 80 characters.
#' @usage nltt_plot(phy, xlab = "Normalized Time", ylab = "Normalized Lineages", ...)
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/plot_nLTT.R:29:23: style: Do not place spaces around code in parentheses or square brackets.
nltt_plot <- function( phy, xlab = "Normalized Time",
^R/plot_nLTT.R:40:30: style: Do not place spaces around code in parentheses or square brackets.
xy <- ape::ltt.plot.coords( phy, backward = TRUE, tol = 1e-6)
^R/plot_nLTT.R:43:28: style: Do not place spaces around code in parentheses or square brackets.
xy[, 1] <- xy[, 1] + abs( min( xy[, 1])) #make sure time runs from 0..T
^R/plot_nLTT.R:43:33: style: Do not place spaces around code in parentheses or square brackets.
xy[, 1] <- xy[, 1] + abs( min( xy[, 1])) #make sure time runs from 0..T
^R/plot_nLTT.R:44:28: style: Do not place spaces around code in parentheses or square brackets.
xy[, 1] <- xy[, 1] / max( xy[, 1]) #normalize time
^R/plot_nLTT.R:61:1: style: Lines should not be more than 80 characters.
#' This is a modified version of the \code{ape} function ltt.lines: add the normalized Lineage-Through-Time statistic of a phylogenetic tree to an already existing plot
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/plot_nLTT.R:80:30: style: Do not place spaces around code in parentheses or square brackets.
xy <- ape::ltt.plot.coords( phy, backward = TRUE, tol = 1e-6)
^R/plot_nLTT.R:81:28: style: Do not place spaces around code in parentheses or square brackets.
xy[, 2] <- xy[, 2] / max( xy[, 2]) #normalize number lineages
^R/plot_nLTT.R:83:28: style: Do not place spaces around code in parentheses or square brackets.
xy[, 1] <- xy[, 1] + abs( min( xy[, 1])) #make sure time runs from 0..T
^R/plot_nLTT.R:83:33: style: Do not place spaces around code in parentheses or square brackets.
xy[, 1] <- xy[, 1] + abs( min( xy[, 1])) #make sure time runs from 0..T
^R/plot_nLTT.R:84:28: style: Do not place spaces around code in parentheses or square brackets.
xy[, 1] <- xy[, 1] / max( xy[, 1]) #normalize time
^R/stretch_nltt_matrix.R:4:1: style: Lines should not be more than 80 characters.
#' @param dt The resultion, a value e [0.0001, 1]. If 'dt' is set to a very small value, this function will stop
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/stretch_nltt_matrix.R:47:12: style: Do not place spaces around code in parentheses or square brackets.
m <- m[ -rows_to_delete, ]
^R/stretch_nltt_matrix.R:65:15: style: Do not place spaces around code in parentheses or square brackets.
n <- matrix( c(n_ts, n_ns), ncol = 2, nrow = n_nrow)
^tests/testthat/test-abc_smc_nltt.R:6:50: style: Remove spaces before the left parenthesis in a function call.
Sys.getenv("HOME") == "/home/p230198") return ()
^tests/testthat/test-abc_smc_nltt.R:13:12: style: Remove spaces before the left parenthesis in a function call.
return (t)
^tests/testthat/test-abc_smc_nltt.R:17:12: style: Remove spaces before the left parenthesis in a function call.
return ( rexp(n = 1, rate = 10) )
^tests/testthat/test-abc_smc_nltt.R:17:13: style: Do not place spaces around code in parentheses or square brackets.
return ( rexp(n = 1, rate = 10) )
^tests/testthat/test-abc_smc_nltt.R:17:36: style: Do not place spaces around code in parentheses or square brackets.
return ( rexp(n = 1, rate = 10) )
^tests/testthat/test-abc_smc_nltt.R:21:11: style: Do not place spaces around code in parentheses or square brackets.
return( dexp( val[1], rate = 10) )
^tests/testthat/test-abc_smc_nltt.R:21:17: style: Do not place spaces around code in parentheses or square brackets.
return( dexp( val[1], rate = 10) )
^tests/testthat/test-abc_smc_nltt.R:21:36: style: Do not place spaces around code in parentheses or square brackets.
return( dexp( val[1], rate = 10) )
^tests/testthat/test-abc_smc_nltt.R:27:3: style: Variable or function name should be snake_case.
LL_BD <- function(params, phy) {
^~~~~tests/testthat/test-abc_smc_nltt.R:41:3: style: Variable or function name should be snake_case.
ML <- stats::optimize(f = tofit, interval = c(0, 1))
^~tests/testthat/test-abc_smc_nltt.R:47:3: style: Variable or function name should be snake_case.
A <- abc_smc_nltt(
^tests/testthat/test-abc_smc_nltt.R:66:12: style: Remove spaces before the left parenthesis in a function call.
return (t)
^tests/testthat/test-abc_smc_nltt.R:70:12: style: Do not place spaces around code in parentheses or square brackets.
return( rexp(n = 2, rate = 1) )
^tests/testthat/test-abc_smc_nltt.R:70:34: style: Do not place spaces around code in parentheses or square brackets.
return( rexp(n = 2, rate = 1) )
^tests/testthat/test-abc_smc_nltt.R:77:12: style: Remove spaces before the left parenthesis in a function call.
return ( dexp( val[1], rate = 1) *
^tests/testthat/test-abc_smc_nltt.R:77:13: style: Do not place spaces around code in parentheses or square brackets.
return ( dexp( val[1], rate = 1) *
^tests/testthat/test-abc_smc_nltt.R:77:19: style: Do not place spaces around code in parentheses or square brackets.
return ( dexp( val[1], rate = 1) *
^tests/testthat/test-abc_smc_nltt.R:78:21: style: Do not place spaces around code in parentheses or square brackets.
dexp( val[2], rate = 1) )
^tests/testthat/test-abc_smc_nltt.R:78:39: style: Do not place spaces around code in parentheses or square brackets.
dexp( val[2], rate = 1) )
^tests/testthat/test-get_branching_times.R:17:13: style: Variable or function name should be snake_case.
phylogeny$root.edge <- 2
^~~~~~~~~tests/testthat/test-get_n_lineages.R:7:13: style: Variable or function name should be snake_case.
phylogeny$root.edge <- 2
^~~~~~~~~tests/testthat/test-mcmc_nltt.R:6:51: style: Remove spaces before the left parenthesis in a function call.
Sys.getenv("HOME") == "/home/p230198") return ()
^tests/testthat/test-mcmc_nltt.R:13:3: style: Variable or function name should be snake_case.
LL_BD <- function(params, phy) {
^~~~~tests/testthat/test-mcmc_nltt.R:27:37: style: Do not place spaces around code in parentheses or square brackets.
return(-1 * LL_BD(params, tree1) )
^tests/testthat/test-mcmc_nltt.R:30:3: style: Variable or function name should be snake_case.
ML <- optim(par = c(1, 0.001), fn = tofit)
^~
codecov-io
commented
7 years ago Merging #36 into master will not change coverage. The diff coverage is
n/a.
@@ Coverage Diff @@
## master #36 +/- ##
=======================================
Coverage 95.57% 95.57%
=======================================
Files 12 12
Lines 565 565
=======================================
Hits 540 540
Misses 25 25Continue to review full report at Codecov.
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Looks like
testitis a requirement --nLTTstat_exactfails to run without it, probably because of L203 and others.