| Title: | Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data |
|---|---|
| Description: | Bayesian reconstruction of disease outbreaks using epidemiological and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>. Campbell, F, Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>. |
| Authors: | Thibaut Jombart [aut], Finlay Campbell [aut, cre], Rich Fitzjohn [aut], Gerry Tonkin-Hill [ctb], Kristjan Eldjarn [ctb], Alexis Robert [ctb] |
| Maintainer: | Finlay Campbell <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.3 |
| Built: | 2024-09-25 14:21:00 UTC |
| Source: | https://github.com/reconhub/outbreaker2 |
This function takes a function f and a series of named arguments, and
returns a closure of f which will only rely on one single argument
'param'. This is used to reduce the number of arguments passed around to
likelihood or movement functions. This functionality is used internally when
creating closures of custom moves in bind_moves.
bind_to_function(f, ...)bind_to_function(f, ...)
f |
The function to which arguments are bound. |
... |
Named arguments to bind to the function's environment. |
Initial code by Rich FitzJohn (see 'references') with some adaptations by Thibaut Jombart
Initial code comes from the partially_apply function in
the 'rodeint' package richfitz/rodeint.
These functions are different color palettes (color-generating functions) used in outbreaker.
chains_pal(n) cases_pal(n)chains_pal(n) cases_pal(n)
n |
a number of colors to be created |
Thibaut Jombart ([email protected])
plot(1:8, col = chains_pal(8), cex = 10, pch = 20)plot(1:8, col = chains_pal(8), cex = 10, pch = 20)
This function defines settings for outbreaker. It takes a list of named items as input, performs various checks, set defaults where arguments are missing, and return a correct list of settings. If no input is given, it returns the default settings.
create_config(..., data = NULL) ## S3 method for class 'outbreaker_config' print(x, ...)create_config(..., data = NULL) ## S3 method for class 'outbreaker_config' print(x, ...)
... |
further arguments to be passed to other methods |
data |
an optional list of data items as returned by
|
x |
an |
Acceptables arguments for ... are:
the tree used to initialize the MCMC. Can be either a
character string indicating how this tree should be computed, or a vector of
integers corresponding to the tree itself, where the i-th value corresponds
to the index of the ancestor of 'i' (i.e., init.tree[i] is the
ancestor of case i). Accepted character strings are "seqTrack" (uses
seqTrack algorithm to generate the initial tree - see function
seqTrack in the package adegenet), "random" (ancestor randomly
selected from preceding cases), and "star" (all cases coalesce to the first
case). Note that for SeqTrack, all cases should have been sequenced.
a vector of integers indicating the initial values of
alpha, where the i-th value indicates the ancestor of case 'i'; defaults to
NULL, in which ancestries are defined from init_tree.
a (recycled) vector of integers indicating the initial values of kappa; defaults to 1.
a vector of integers indicating the initial values of
t_inf, i.e. dates of infection; defaults to NULL, in which case
the most likely t_inf will be determined from the delay to
reporting/symptoms distribution, and the dates of reporting/symptoms,
provided in data.
initial value for the mutation rates
initial value for the reporting probability
initial value for the contact reporting coverage
initial value for the non-infectious contact rate
an integer indicating the number of iterations in the MCMC, including the burnin period
a vector of logicals indicating, for each case, if the ancestry should be estimated ('moved' in the MCMC), or not, defaulting to TRUE; the vector is recycled if needed.
a vector of logicals indicating, for each case, if the dates of infection should be estimated ('moved' in the MCMC), or not, defaulting to TRUE; the vector is recycled if needed.
a logical indicating whether the mutation rates should be estimated ('moved' in the MCMC), or not, all defaulting to TRUE.
a logical indicating whether the reporting probability should be estimated ('moved' in the MCMC), or not, all defaulting to TRUE.
a logical indicating whether the contact reporting coverage should be estimated ('moved' in the MCMC), or not at all, defaulting to TRUE.
a logical indicating whether the non-infectious contact rate should be estimated ('moved' in the MCMC), or not at all, defaulting to TRUE.
a logical indicating whether the number of generations between two successive cases should be estimated ('moved' in the MCMC), or not, all defaulting to TRUE.
a logical indicating whether the reporting probability should be estimated ('moved' in the MCMC), or not, all defaulting to TRUE.
the number of iterations of the MCMC
the frequency at which MCMC samples are retained for the output
the standard deviation for the Normal proposal for the mutation rates
the standard deviation for the Normal proposal for the reporting probability
the standard deviation for the Normal proposal for the contact reporting coverage
the standard deviation for the Normal proposal for the non-infectious contact rate
the proportion of ancestries to move at each iteration of the MCMC
the proportion of infection dates to move at each iteration of the MCMC
the size of the batch of random number pre-generated
a logical indicating if the paranoid mode should be used; this mode is used for performing additional tests during outbreaker; it makes computations substantially slower and is mostly used for debugging purposes.
earliest infection date possible, expressed as days since the first sampling
an integer indicating the largest number of generations between any two linked cases; defaults to 5
a numeric value indicating the rate of the exponential prior for the mutation rate 'mu'
a numeric vector of length 2 indicating the first and second parameter of the beta prior for the reporting probability 'pi'
a numeric vector of length 2 indicating the first and second parameter of the beta prior for the contact reporting coverage 'eps'
a numeric vector of length 2 indicating the first and second parameter of the beta prior for the non-infectious contact rate 'lambda'
a logical indicating if the contact tracing data is directed or not. If yes, the first column represents the infector and the second column the infectee. If ctd is provided as an epicontacts objects, directionality will be taken from there.
a logical indicating if a progress bar should be displayed
Thibaut Jombart ([email protected])
outbreaker_data to check and process data for outbreaker
## see default settings create_config() ## change defaults create_config(move_alpha = FALSE, n_iter = 2e5, sample_every = 1000)## see default settings create_config() ## change defaults create_config(move_alpha = FALSE, n_iter = 2e5, sample_every = 1000)
This function creates initial outputs and parameter states for outbreaker.
create_param(data = outbreaker_data(), config = create_config())create_param(data = outbreaker_data(), config = create_config())
data |
A list of data items as returned by |
config |
A list of settings as returned by |
A list containing two components $store and
$current. store is a list with the class
outbreaker_store, used for storing 'saved' states of the
MCMC. current is a list with the class outbreaker_param, used
for storing 'current' states of the MCMC.
outbreaker_store class content:
size: The length of the list, corresponding to the number of
samples saved from the MCMC.
step: A vector of integers of length size, storing the
steps of the MCMC corresponding to the saved samples.
post: A numeric vector of length size, storing
log-posterior values.
like: A numeric vector of length size, storing
log-likelihood values.
prior: A numeric vector of length size,
storing log-prior values.
alpha: A list of length size. Each item of the list is
an integer vector of length data$N, storing indices (from 1 to N) of
infectors for each case.
t_inf: A list of length size. Each item of the list is
an integer vector of length data$N, storing dates of infections for
each case.
mu: A numeric vector of length size, storing values of
the mutation rate.
kappa: A list of length size. Each item of the list is
an integer vector of length data$N, storing the number of generations
before the last sampled ancestor for each case.
pi: A numeric vector of length size, storing values of
the reporting probability.
eps: A numeric vector of length size, storing values of
the contact reporting coverage.
lambda: A numeric vector of length size, storing values of
the non-infectious contact rate.
counter: A counter used to keep track of the current iteration
of the MCMC (used internally).
outbreaker_param class content:
alpha: An integer vector of length data$N, storing
indices (from 1 to N) of infectors for each case.
t_inf: An integer vector of length data$N, storing dates
of infections for each case.
mu: The value of the mutation rate.
kappa: An integer vector of length data$N, storing the
number of generations before the last sampled ancestor for each case.
pi: The value of the reporting probability.
eps: The value of the contact reporting coverage.
lambda: The value of the non-infectious contact rate.
Thibaut Jombart ([email protected])
## load data x <- fake_outbreak data <- outbreaker_data(dates = x$sample, dna = x$dna, w_dens = x$w) ## modify config settings config <- create_config(move_alpha = FALSE, n_iter = 2e5, sample_every = 1000) ## create param object param <- create_param(data = data, config = config)## load data x <- fake_outbreak data <- outbreaker_data(dates = x$sample, dna = x$dna, w_dens = x$w) ## modify config settings config <- create_config(move_alpha = FALSE, n_iter = 2e5, sample_every = 1000) ## create param object param <- create_param(data = data, config = config)
This function is used to specify customised likelihood functions for outbreaker. Custom functions are specified as a named list or series of comma-separated, named arguments, indicating which log-likelihood component they compute. Values currently available are:
custom_likelihoods(...) ## S3 method for class 'custom_likelihoods' print(x, ...)custom_likelihoods(...) ## S3 method for class 'custom_likelihoods' print(x, ...)
... |
a named list of functions, each computing a log-likelihood component. |
x |
an |
genetic: the genetic likelihood; by default, the function
cpp_ll_genetic is used.
timing_sampling: the likelihood of sampling times; by default, the function
cpp_ll_timing_sampling is used.
timing_infections: the likelihood of infection times; by default, the function
cpp_ll_timing_infections is used.
reporting: the likelihood of the reporting process; by default,
the function cpp_ll_reporting is used.
contact: the likelihood of the contact tracing data; by default,
the function cpp_ll_contact is used.
All log-likelihood functions should have the following arguments, in this order:
data: a list of named items containing input data as returned by
outbreaker_data
param: a list of parameters with the class
create_param
A named list of list(function, arity) pairs with the class
custom_likelihood, each function implementing a customised
log-likelihood component of outbreaker. Functions which are not
customised will result in a list(NULL, 0) component. Any function with
arity 3 must have the third parameter default to NULL.
a list of named functions
Thibaut Jombart ([email protected])
See customization vignette for detailed examples on how to customize likelihoods.
## specify a null model by disabling all likelihood components f_null <- function(data, param) { return(0.0) } null_model <- custom_likelihoods(genetic = f_null, timing_sampling = f_null, timing_infections = f_null, reporting = f_null, contact = f_null) null_config <- list(find_import = FALSE, n_iter = 100, sample_every = 1) ## load data x <- fake_outbreak data <- outbreaker_data(dates = x$sample, dna = x$dna, w_dens = x$w) res_null <- outbreaker(data = data, config = null_config, likelihoods = null_model) ## visualise ancestries to see if all transmission trees have been explored plot(res_null, type = "alpha")## specify a null model by disabling all likelihood components f_null <- function(data, param) { return(0.0) } null_model <- custom_likelihoods(genetic = f_null, timing_sampling = f_null, timing_infections = f_null, reporting = f_null, contact = f_null) null_config <- list(find_import = FALSE, n_iter = 100, sample_every = 1) ## load data x <- fake_outbreak data <- outbreaker_data(dates = x$sample, dna = x$dna, w_dens = x$w) res_null <- outbreaker(data = data, config = null_config, likelihoods = null_model) ## visualise ancestries to see if all transmission trees have been explored plot(res_null, type = "alpha")
This function is used to specify customised movement functions (a.k.a. samplers) for outbreaker. Custom functions are specified as a named list or series of comma-separated, named arguments, indicating which type of movement they implement. Values currently available are:
custom_moves(...) ## S3 method for class 'outbreaker_moves' print(x, ...)custom_moves(...) ## S3 method for class 'outbreaker_moves' print(x, ...)
... |
A list or a series of named, comma-separated functions implementing movements of parameters or augmented data. |
x |
an |
mu: movement of the mutation rate; by default, the function
cpp_move_mu is used.
pi: movement of the reporting probability; by default, the function
cpp_move_pi is used.
eps: movement of the contact reporting coverage; by default, the
function cpp_move_eps is used.
lambda: the movement of the non-infectious contact rate; the
function cpp_move_lambda is used.
alpha: movement of the transmission tree, by randomly proposing
infectors in the pool of cases infected before; by default, the function
cpp_move_alpha is used.
swap_cases: movement of the transmission tree, by swapping
infectors and infected cases; by default, the function
cpp_move_swap_cases is used.
t_inf: movement of the date of infection; by default, the
function cpp_move_t_inf is used.
kappa: movement of the number generations between cases; by
default, the function cpp_move_kappa is used.
Movement functions must have an argument param, which is a list of
parameters and augmented data of the class create_param.
Each movement function will be enclosed with its other arguments, so that the
resulting function will have a single argument 'param'. For non-standard
movements (i.e. none of the names specified above), the closure will contain:
data: a list of named items containing input data as returned by
outbreaker_data
config: a list of named items containing input data as returned by
create_config
likelihoods: a list of named custom likelihood functions as returned by
custom_likelihoods
priors: a list of named custom prior functions as returned by
custom_priors
A list of movement functions with a single argument 'param', with
class outbreaker_moves.
Thibaut Jombart ([email protected]).
See customization vignette for detailed examples on how to customise movement functions.
Priors can be specified in several ways in outbreaker2 (see details and examples). The most flexible way to specify a prior is to provide a prior function directly. This function must take an argument 'param', which is a list which contains all the states of the parameters and augmented data. See the documentation of create_param for more information.
custom_priors(...) ## S3 method for class 'custom_priors' print(x, ...)custom_priors(...) ## S3 method for class 'custom_priors' print(x, ...)
... |
A list or a series of named, comma-separated functions implementing priors. Each function must have a single argument, which corresponds to a 'outbreaker_param' list. |
x |
an |
There are three ways a user can specify priors:
1) Default: this is what happens when the 'config' has default values of
prior parameters.
2) Customized parameters: in this case, the prior functions are the default
ones from the package, but will use custom parameters, specified by the user
through create_config.
3) Customized functions: in this case, prior functions themselves are
specified by the user, through the '...' argument of 'custom_priors'. The
requirements is that such functions must have either hard-coded parameters or
enclosed values. They will take a single argument which is a list containing
all model parameters with the class 'outbreaker_param'. ALL PRIORS functions
are expected to return values on a LOG SCALE.
Priors currently used for the model are:
mu (mutation rate): default function is an exponential
distribution implemented in outbreaker:::cpp_prior_mu. New prior
functions should use x$mu to refer to the current value of mu,
assuming their argument is called x.
pi (reporting probability): default function is a beta
distribution implemented in outbreaker:::cpp_prior_pi. New prior
functions should use x$pi to refer to the current value of pi,
assuming their argument is called x.
eps (contact reporting coverage): default function is a beta
distribution implemented in outbreaker:::cpp_prior_eps. New prior
functions should use x$eps to refer to the current value of eps,
assuming their argument is called x.
lambda (non-infectious contact rate): default function is a
beta distribution implemented in outbreaker:::cpp_prior_lambda. New
prior functions should use x$lambda to refer to the current value of
lambda, assuming their argument is called x.
A list of custom functions with class custom_priors. Values
set to NULL will be ignored and default functions will be used
instead.
Thibaut Jombart ([email protected]).
See customization vignette for detailed examples on how to customize priors.
## BASIC CONFIGURATION custom_priors() ## SPECIFYING PRIOR PARAMETERS ## - this will need to be passed to outbreaker default_config <- create_config() new_config <- create_config(prior_mu = 1e-5, prior_pi = c(2, 1)) ## - to check the prior manually, default settings: param <- list(mu = 0.001, pi = 0.9) outbreaker2:::cpp_prior_mu(param, default_config) outbreaker2:::cpp_prior_pi(param, default_config) outbreaker2:::cpp_prior_mu(param, new_config) outbreaker2:::cpp_prior_pi(param, new_config) ## these correspond to: dexp(0.001, 0.01, log = TRUE) dbeta(0.9, 2, 1, log = TRUE) ## SPECIFYING A PRIOR FUNCTION ## flat prior for pi between 0.5 and 1 f <- function(x) {ifelse(x$pi > 0.5, log(2), log(0))} priors <- custom_priors(pi = f) priors # this should be passed to outbreaker ## test the prior manually priors$pi(list(pi=1)) priors$pi(list(pi=.6)) priors$pi(list(pi=.2)) priors$pi(list(pi=.49))## BASIC CONFIGURATION custom_priors() ## SPECIFYING PRIOR PARAMETERS ## - this will need to be passed to outbreaker default_config <- create_config() new_config <- create_config(prior_mu = 1e-5, prior_pi = c(2, 1)) ## - to check the prior manually, default settings: param <- list(mu = 0.001, pi = 0.9) outbreaker2:::cpp_prior_mu(param, default_config) outbreaker2:::cpp_prior_pi(param, default_config) outbreaker2:::cpp_prior_mu(param, new_config) outbreaker2:::cpp_prior_pi(param, new_config) ## these correspond to: dexp(0.001, 0.01, log = TRUE) dbeta(0.9, 2, 1, log = TRUE) ## SPECIFYING A PRIOR FUNCTION ## flat prior for pi between 0.5 and 1 f <- function(x) {ifelse(x$pi > 0.5, log(2), log(0))} priors <- custom_priors(pi = f) priors # this should be passed to outbreaker ## test the prior manually priors$pi(list(pi=1)) priors$pi(list(pi=.6)) priors$pi(list(pi=.2)) priors$pi(list(pi=.49))
This dataset is a small (30 cases) simulated outbreak originally used to
illustrate outbreaker, and used for the same purposes in
outbreaker2. This list contains the following:
fake_outbreakfake_outbreak
An object of class list of length 6.
$onset: A vector of integers representing dates of onset.
$sample: A vector of integers representing the dates of isolation.
$dna: A DNAbin matrix of pathogen genome sequences.
$w: A numeric vector giving the empirical distribution of the
generation time.
$ances: A vector of integers indicating, for each case, the true
infectors. NA represents imported cases.
Thibaut Jombart [email protected]
names(fake_outbreak) fake_outbreaknames(fake_outbreak) fake_outbreak
This function returns an environment containing all C++ functions (bound to R using Rcpp) used for priors, likelihoods, and movements of parameters in outbreaker2.
get_cpp_api()get_cpp_api()
An environment containing Rcpp bindings to C++ functions used internally in outbreaker2. All functions are named as cpp_[type]_[component], where 'type' can be:
'prior': prior computation
'll': likelihood computation
'move': movement of parameters or augmented data
And where 'component' can be:
'mu': (parameter) mutation rate
'pi': (parameter) reporting probability
'lambda': (parameter) non-infectious contact rate
'eps': (parameter) contact reporting coverage
'alpha': (augmented data) ancestries of the cases
'kappa': (augmented data) generation between cases on transmission chains
't_inf': (augmented data) dates of infections
'timing_infections': (likelihood component) timing between infectors and infectees
'timing_sampling': (likelihood component) timing between infection and reporting / isolation
'timing': (likelihood component) sum of the two timing components
'genetic': (likelihood component) genetic diversity accumulated along transmission chains
'reporting': (likelihood component) reporting of cases
'all': (likelihood component) sum of all likelihood components
'swap_cases': (type of movement) swap infectors and infectees on a transmission tree
For a description of the arguments of these functions, see the Rcpp_API
vignette (vignette("Rcpp_API", package = "outbreaker2")).
Thibaut Jombart ([email protected])
## get functions in an environment api <- get_cpp_api() api ## check content of the environment ls(api) ## test the prior for mu args(api$cpp_prior_mu) config <- create_config() api$cpp_prior_mu(list(mu = 0.00123), config) dexp(0.00123, rate = config$prior_mu, log = TRUE)## get functions in an environment api <- get_cpp_api() api ## check content of the environment ls(api) ## test the prior for mu args(api$cpp_prior_mu) config <- create_config() api$cpp_prior_mu(list(mu = 0.00123), config) dexp(0.00123, rate = config$prior_mu, log = TRUE)
The function outbreaker is the main function of the package. It runs
processes various inputs (data, configuration settings, custom priors,
likelihoods and movement functions) and explores the space of plausible
transmission trees of a densely sampled outbreaks.
outbreaker( data = outbreaker_data(), config = create_config(), priors = custom_priors(), likelihoods = custom_likelihoods(), moves = custom_moves() )outbreaker( data = outbreaker_data(), config = create_config(), priors = custom_priors(), likelihoods = custom_likelihoods(), moves = custom_moves() )
data |
a list of named items containing input data as returned by
|
config |
a set of settings as returned by |
priors |
a set of log-prior functions as returned by
|
likelihoods |
a set of log-likelihood functions as returned by
|
moves |
a set of movement functions as returned by
|
The emphasis of 'outbreaker2' is on modularity, which enables customisation
of priors, likelihoods and even movements of parameters and augmented data by
the user. This the dedicated vignette on this topic
vignette("outbreaker2_custom").
Thibaut Jombart ([email protected])
Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N (2014). Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Computational Biology.
outbreaker_data to process input data, and
create_config to process/set up parameters
outbreaker_data: function to process input data
create_config: function to create default and customise
configuration settings
custom_priors: function to specify customised prior
functions
custom_likelihoods: function to specify customised likelihoods
functions
custom_moves: function to create default and customise movement
functions
## get data data(fake_outbreak) dat <- fake_outbreak ## Not run: ## run outbreaker out <- outbreaker(data = list(dna = dat$dna, dates = dat$onset, w_dens = dat$w), config = list(n_iter = 2e4, sample_every = 200)) plot(out) as.data.frame(out) ## run outbreaker, no DNA sequences out2 <- outbreaker(data = list(dates = dat$onset, w_dens = w), config = list(n_iter = 2e4, sample_every = 200)) plot(out2) as.data.frame(out2) ## End(Not run)## get data data(fake_outbreak) dat <- fake_outbreak ## Not run: ## run outbreaker out <- outbreaker(data = list(dna = dat$dna, dates = dat$onset, w_dens = dat$w), config = list(n_iter = 2e4, sample_every = 200)) plot(out) as.data.frame(out) ## run outbreaker, no DNA sequences out2 <- outbreaker(data = list(dates = dat$onset, w_dens = w), config = list(n_iter = 2e4, sample_every = 200)) plot(out2) as.data.frame(out2) ## End(Not run)
This function performs various checks on input data given to outbreaker. It takes a list of named items as input, performs various checks, set defaults where arguments are missing, and return a correct list of data input. If no input is given, it returns the default settings.
outbreaker_data(..., data = list(...))outbreaker_data(..., data = list(...))
... |
a list of data items to be processed (see description) |
data |
optionally, an existing list of data item as returned by |
Acceptables arguments for ... are:
dates a vector indicating the collection dates, provided either as
integer numbers or in a usual date format such as Date or
POSIXct format. By convention, zero will indicate the oldest date. If
the vector is named, the vector names will be used for matching cases to
contact tracing data and labelled DNA sequences.
the DNA sequences in DNAbin format (see
read.dna in the ape package); this can be imported from a
fasta file (extension .fa, .fas, or .fasta) using adegenet's function
fasta2DNAbin.
the contact tracing data provided as a matrix/dataframe of two columns, indicating a reported contact between the two individuals whose ids are provided in a given row of the data, or an epicontacts object. In the case of the latter, linelist IDs will be used for matching dates and DNA sequences
a vector of numeric values indicating the generation time distribution, reflecting the infectious potential of a case t = 1, 2, ... time steps after infection. By convention, it is assumed that newly infected patients cannot see new infections on the same time step. If not standardized, this distribution is rescaled to sum to 1.
similar to w_dens, except that this is the distribution
of the colonization time, i_e. time interval during which the pathogen can
be sampled from the patient.
Thibaut Jombart ([email protected])
x <- fake_outbreak outbreaker_data(dates = x$sample, dna = x$dna, w_dens = x$w)x <- fake_outbreak outbreaker_data(dates = x$sample, dna = x$dna, w_dens = x$w)
This package provides a statistical platform for reconstructing transmission
trees ('who infects whom') in densely sampled disease outbreaks. It
reimplements, as a particular case, the model of 'outbreaker' (see
references). 'outbreaker2' extends and replaces 'outbreaker'.
The emphasis of 'outbreaker2' is on modularity, which enables customisation
of priors, likelihoods and even movements of parameters and augmented data by
the user. This the dedicated vignette on this topic
vignette("outbreaker2_custom").
The main functions of the package are:
outbreaker: main function to run analyses
outbreaker_data: function to process input data
create_config: function to create default and customise
configuration settings
custom_priors: function to specify customised prior
functions
custom_likelihoods: function to specify customised likelihoods
functions
custom_moves: function to create default and customise movement
functions
Thibaut Jombart [email protected]
Several methods are defined for instances of the class
outbreaker_chains, returned by outbreaker, including:
print, plot, summary
## S3 method for class 'outbreaker_chains' print(x, n_row = 3, n_col = 8, ...) ## S3 method for class 'outbreaker_chains' plot( x, y = "post", type = c("trace", "hist", "density", "alpha", "t_inf", "kappa", "network"), group = NULL, burnin = 0, min_support = 0.1, labels = NULL, ... ) ## S3 method for class 'outbreaker_chains' summary(object, burnin = 0, method = c("mpa", "decycle"), ...)## S3 method for class 'outbreaker_chains' print(x, n_row = 3, n_col = 8, ...) ## S3 method for class 'outbreaker_chains' plot( x, y = "post", type = c("trace", "hist", "density", "alpha", "t_inf", "kappa", "network"), group = NULL, burnin = 0, min_support = 0.1, labels = NULL, ... ) ## S3 method for class 'outbreaker_chains' summary(object, burnin = 0, method = c("mpa", "decycle"), ...)
x |
an |
n_row |
the number of rows to display in head and tail; defaults to 3. |
n_col |
the number of columns to display; defaults to 8. |
... |
further arguments to be passed to other methods |
y |
a character string indicating which element of an
|
type |
a character string indicating the kind of plot to be used (see details) |
group |
a vector of character strings indicating the parameters to display, or "all" to display all global parameters (non node-specific parameters). |
burnin |
the number of iterations to be discarded as burnin |
min_support |
a number between 0 and 1 indicating the minimum support of ancestries to be plotted; only used if 'type' is 'network' |
labels |
a vector of length N indicating the case labels (must be provided in the same order used for dates of symptom onset) |
object |
an |
method |
the method used to determine consensus ancestries. 'mpa' (maximum posterior ancestry) simply returns the posterior ancestry with the highest posterior support for each case, even if this includes cycles. 'decycle' will return the maximum posterior ancestry, except when cycles are detected, in which case the link in the cycle with the lowest support is pruned and the tree recalculated. |
type indicates the type of graphic to plot:
trace to visualise MCMC traces for parameters or augmented data (plots the
log-likelihood by default)
hist to plot histograms of quantitative values
density to plot kernel density estimations of quantitative values
alpha to visualise the posterior frequency of ancestries
network to visualise the transmission tree; note that
this opens up an interactive plot and requires a web browser with
Javascript enabled; the argument 'min_support' is useful to select only the
most supported ancestries and avoid displaying too many links
kappa to visualise the distributions generations between cases and their
ancestor/infector
Thibaut Jombart ([email protected])
See introduction vignette for detailed examples on how to visualise outbreaker_chains objects.
This function simulates contact data from a transmission tree. The model assumes that all transmission pairs have experienced contact, and that there is no false-positive reporting of contacts. The probability of contact occuring between a non-transmision pair is given by the parameter lambda. The probability of reporting a contact (transmission pair or not) is given by the parameters eps.
sim_ctd(tTree, eps, lambda)sim_ctd(tTree, eps, lambda)
tTree |
a dataframe or matrix of two columns, with each row providing the ids (numerical or as characters) of a transmission pair |
eps |
the contact reporting coverage, defined as the probability of reporting a contact (transmission pair or not) |
lambda |
the non-infectious contact rate, defined as the probability of contact between a non-transmission pair. |
Finlay Campbell ([email protected])
## load data x <- fake_outbreak tTree <- data.frame(from = x$ances, to = seq_along(x$ances)) ## simulate contact data with coverage of 80% and 10% probability of ## contact between non-transmission pairs ctd <- outbreaker2:::sim_ctd(tTree, eps = 0.8, lambda = 0.1) ## inspect contact data head(ctd)## load data x <- fake_outbreak tTree <- data.frame(from = x$ances, to = seq_along(x$ances)) ## simulate contact data with coverage of 80% and 10% probability of ## contact between non-transmission pairs ctd <- outbreaker2:::sim_ctd(tTree, eps = 0.8, lambda = 0.1) ## inspect contact data head(ctd)