test_that("normal plotting", {
set.seed(5)
phy <- ape::rphylo(n = 4, birth = 1, death = 0)
traits <- c(0, 1, 1, 0)
params <- secsse::id_paramPos(c(0, 1), 2)
params[[1]][] <- c(0.2, 0.2, 0.1, 0.1)
params[[2]][] <- 0.01
params[[3]][, ] <- 0.1
diag(params[[3]]) <- NA
# Thus, we have for both, rates
# 0A, 1A, 0B and 1B. If we are interested in the posterior probability of
# trait 0,we have to provide a helper function that sums the probabilities of
# 0A and 0B, e.g.:
helper_function <- function(x) {
return(sum(x[c(5, 7)]) / sum(x)) # normalized by total sum, just in case.
}
testthat::expect_message(
px <- plot_state_exact(parameters = params,
phy = phy,
traits = traits,
num_concealed_states = 2,
sampling_fraction = c(1, 1),
num_steps = 10,
prob_func = helper_function)
)
testthat::expect_true(inherits(px, "ggplot"))
})
test_that("cla plotting", {
skip_on_cran()
parenthesis <- "(((6:0.2547423371,(1:0.0496153503,4:0.0496153503):0.2051269868):0.1306304758,(9:0.2124135406,5:0.2124135406):0.1729592723):1.151205247,(((7:0.009347664296,3:0.009347664296):0.2101416075,10:0.2194892718):0.1035186448,(2:0.2575886319,8:0.2575886319):0.06541928469):1.213570144);" #nolint
phylotree <- ape::read.tree(file = "", parenthesis)
traits <- c(2, 0, 1, 0, 2, 0, 1, 2, 2, 0)
num_concealed_states <- 3
idparslist <- cla_id_paramPos(traits, num_concealed_states)
idparslist$lambdas[2, ] <- rep(1, 9)
idparslist[[2]][] <- 4
masterBlock <- matrix(5, ncol = 3, nrow = 3, byrow = TRUE)
diag(masterBlock) <- NA
diff.conceal <- FALSE
idparslist[[3]] <- q_doubletrans(traits, masterBlock, diff.conceal)
testthat::expect_output(
startingpoint <- DDD::bd_ML(brts = ape::branching.times(phylotree))
)
intGuessLamba <- startingpoint$lambda0
intGuessMu <- startingpoint$mu0
idparsopt <- c(1)
initparsopt <- c(rep(intGuessLamba, 1))
idparsfix <- c(0, 4, 5)
parsfix <- c(0, 0, 0.01)
tol <- c(1e-04, 1e-05, 1e-07)
maxiter <- 1000 * round((1.25) ^ length(idparsopt))
optimmethod <- "subplex"
cond <- "proper_cond"
root_state_weight <- "proper_weights"
sampling_fraction <- c(1, 1, 1)
testthat::expect_message(
testthat::expect_warning(
model_R <- cla_secsse_ml(
phylotree,
traits,
num_concealed_states,
idparslist,
idparsopt,
initparsopt,
idparsfix,
parsfix,
cond,
root_state_weight,
sampling_fraction,
tol,
maxiter,
optimmethod,
num_cycles = 1,
verbose = FALSE)
))
helper_function <- function(x) {
return(sum(x[c(10, 13, 16)]) / sum(x))
}
testthat::expect_message(
px <- secsse::plot_state_exact(parameters = model_R$MLpars,
phy = phylotree,
traits = traits,
num_concealed_states =
num_concealed_states,
sampling_fraction = sampling_fraction,
cond = cond,
root_state_weight = root_state_weight,
prob_func = helper_function)
)
testthat::expect_true(inherits(px, "ggplot"))
})
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