Package 'MALECOT'

Description

Change the active set of a MALECOT project

Usage

active_set(project, set)

Arguments

Examples

# TODO

Description

Bind data in bi-allelic format to MALECOT project. Data should be formatted as a dataframe with samples in rows and loci in columns. Genetic data should be coded as 1 (homozygote REF allele), 0 (homozygote ALT allele), or 0.5 (heterozygote). Additional meta-data columns can be specified, including a column for sample IDs and a column for sampling population.

Usage

bind_data_biallelic(project, df, ID_col = 1, pop_col = NULL,
  data_cols = NULL, ID = NULL, pop = NULL, missing_data = -9,
  name = NULL, check_delete_output = TRUE)

Arguments

Examples

# TODO

Description

Bind data in multi-allelic format to MALECOT project. Data should be formatted as a dataframe with three columns: "sample_ID", "locus" and "haplotype". Each row of this dataframe specifies a haplotype that was observed at that locus in that individual. Haplotypes should be coded as positive integers.

Usage

bind_data_multiallelic(project, df, pop = NULL, missing_data = -9,
  alleles = NULL, name = NULL, check_delete_output = TRUE)

Arguments

Examples

# TODO

Description

Simple function to check that MALECOT package has loaded successfully. Prints "MALECOT loaded successfully!" if so.

Usage

check_MALECOT_loaded()

Description

Delete a given parameter set from a MALECOT project.

Usage

delete_set(project, set = NULL, check_delete_output = TRUE)

Arguments

Examples

# TODO

Description

Returns effective sample size (ESS) of chosen model run.

Usage

get_ESS(project, K = NULL)

Arguments

Examples

# TODO

Description

Compares qmatrix output for a chosen value of K against a target_group vector. Returns the order of target_group groups, such that there is the best possible alignment against the qmatrix. For example, if the vector returned is c(2,3,1) then the second group in the target vector should be matched against the first group in the qmatrix, followed by the third group in the target vector against the second group in the qmatrix, followed by the first group in the target vector against the third group in the qmatrix.

Usage

get_group_order(project, K, target_group)

Arguments

Examples

# TODO

Description

Determine if object is of class malecot_project.

Usage

is.malecot_project(x)

Arguments

Details

TODO

Examples

# TODO

Description

MALECOT package

Description

Import file from the inst/extdata folder of this package

Usage

malecot_file(name)

Arguments

Description

Define empty malecot_project object

Usage

malecot_project()

Details

TODO

Examples

# TODO

Description

Expand a series of colours by interpolation to produce any number of colours from a given series. The pattern of interpolation is designed so that (n+1)th value contains the nth value plus one more colour, rather than being a completely different series. For example, running more_colours(5) and more_colours(4), the first 4 colours will be shared between the two series.

Usage

more_colours(n = 5, raw_cols = col_hot_cold())

Arguments

Description

TODO

Usage

new_set(project, name = "(no name)", lambda = 1,
  COI_model = "poisson", COI_max = 20, COI_manual = NULL,
  estimate_COI_mean = TRUE, COI_mean = 3, COI_dispersion = 2,
  estimate_error = FALSE, e1 = 0, e2 = 0, e1_max = 0.2,
  e2_max = 0.2)

Arguments

Details

TODO

Examples

# TODO

Description

Produce MCMC autocorrelation plot of the log-likelihood

Usage

plot_acf(project, K = NULL, rung = NULL, col = "black")

Arguments

Description

Plot COI 95% credible intervals of current active set

Usage

plot_COI(project, K = NULL)

Arguments

Details

TODO

Examples

# TODO

Description

Plot COI_mean 95% credible intervals of current active set

Usage

plot_COI_mean(project, K = NULL, deme_order = NULL)

Arguments

Details

TODO

Examples

# TODO

Description

Plot Metropolis-coupling acceptance rates

Usage

plot_coupling(project, K = NULL)

Arguments

Description

Produce MCMC density plot of the log-likelihood

Usage

plot_density(project, K = NULL, rung = NULL, col = "black")

Arguments

Description

Plot error rate 95% credible intervals of current active set

Usage

plot_e(project, K = NULL)

Arguments

Details

TODO

Examples

# TODO

Description

Plot GTI path of current active set

Usage

plot_GTI_path(project, K = NULL, axis_type = 1)

Arguments

Description

Plot log-evidence estimates over K

Usage

plot_logevidence_K(project)

Arguments

Description

Plot log-evidence estimates over parameter sets

Usage

plot_logevidence_model(project)

Arguments

Description

Plot loglikelihood 95% credible intervals of current active set

Usage

plot_loglike(project, K = NULL, axis_type = 1,
  connect_points = FALSE, connect_whiskers = FALSE)

Arguments

Description

Produce diagnostic plots of the log-likelihood.

Usage

plot_loglike_dignostic(project, K = NULL, rung = NULL, col = "black")

Arguments

Description

Plot allele frequency 95% credible intervals of current active set

Usage

plot_p(project, K = NULL, deme = 1)

Arguments

Details

TODO

Examples

# TODO

Description

Plot posterior K

Usage

plot_posterior_K(project)

Arguments

Description

Plot posterior model

Usage

plot_posterior_model(project)

Arguments

Description

Produce plot of the prior on COI for given parameters. Options include the uniform distribution, and a modified form of Poisson and negative binomial distribution (see details).

Usage

plot_prior_COI(COI_model = "poisson", COI_mean = 3,
  COI_dispersion = 2, COI_max = 20)

Arguments

Details

The prior on COI can be uniform, Poisson, or negative binomial. In the uniform case there is an equal chance of any given sample having a COI between 1 and COI_max (inclusive). In the Poisson and negative binomial cases it is important to note that the distribution is over (COI-1), rather than over COI. This is because both Poisson and negative binomial distributions allow for 0 values, which cannot be the case here because observed samples must contain at least 1 genotype. Poisson and negative binomial distributions are also truncated at COI_max.

The full probability mass distribution for the Poisson case with COI_mean$= \mu$ and COI_max$= M$ can be written

$Pr(COI = n) = z (\mu-1)^(n-1) exp(-(\mu-1)) / (n-1)!$

where $z$ is a normalising constant that ensures the distribution sums to unity, and is defined as:

$1/z = \sum_{i=1}^M (\mu-1)^(i-1) exp(-(\mu-1)) / (i-1)!$

The mean of this distribution will generally be very close to $\mu$, and the variance will be close to $\mu-1$ (strictly it will approach these values as $M$ tends to infinity).

The full probability mass distribution for the negative binomial case with COI_mean$= \mu$, COI_dispersion$= v/\mu$ and COI_max$= M$ can be written

$Pr(COI = n) = z \Gamma(n-1+N)/( \Gamma(N)(n-1)! ) p^N (1-p)^(n-1)$

where $N = (\mu-1)^2/(v-\mu+1)$, $p = (\mu-1)/v$, and $z$ is a normalising constant that ensures the distribution sums to unity, and is defined as:

$1/z = \sum_{i=1}^M \Gamma(i-1+N)/( \Gamma(N)(i-1)! ) p^N (1-p)^(i-1)$

The mean of this distribution will generally be very close to $\mu$ and the variance will be close to $v$ (strictly it will approach these values as $M$ tends to infinity).

Description

Produce plot of the prior on COI for given parameters. This prior is Beta in the bi-allelic case, and Dirichlet in the multi-allelic case.

Usage

plot_prior_p(lambda = 1, alleles = NULL)

Arguments

Description

Produce posterior allocation plot of current active set.

Usage

plot_structure(project, K = NULL, base_colours = col_hot_cold(),
  divide_ind_on = FALSE)

Arguments

Description

Produce MCMC trace plot of the log-likelihood at each iteration.

Usage

plot_trace(project, K = NULL, rung = NULL, col = "black")

Arguments

Description

Calling print() on an object of class malecot_project results in custom output. This function therefore stands in for the base print() function, and is equivalent to running print(unclass(x)).

Usage

print_full(x, ...)

Arguments

Description

Custom print function for class malecot_project, printing a summary of the key elements (also equivalent to summary(x)). To do an ordinary print() of all elements of the project, use the print_full() function.

Usage

## S3 method for class 'malecot_project'
print(x, ...)

Arguments

Description

When a new value of K is added in to the analysis it affects all downstream evidence estimates that depend on this K - for example the overall model evidence integrated over K. This function therefore looks through all values of K in the active set and recalculates all downstream elements as needed.

Usage

recalculate_evidence(project)

Arguments

Description

Run the main MALECOT MCMC. Model parameters are taken from the current active parameter set, and MCMC parameters are passed in as arguments. All output is stored within the project.

Usage

run_mcmc(project, K = NULL, precision = 0.01, burnin = 1000,
  samples = 1000, rungs = 1, GTI_pow = 3, auto_converge = TRUE,
  converge_test = 100, solve_label_switching_on = TRUE,
  coupling_on = TRUE, cluster = NULL, pb_markdown = FALSE,
  store_acceptance = TRUE, store_raw = TRUE, silent = FALSE)

Arguments

Examples

# TODO

Description

Simulate genetic data from the same model used in the MALECOT inference step.

Usage

sim_data(n = 100, L = 24, K = 3, data_format = "biallelic",
  pop_col_on = TRUE, alleles = 2, lambda = 1,
  COI_model = "poisson", COI_max = 20, COI_manual = rep(-1, n),
  COI_mean = 3, COI_dispersion = 2, e1 = 0, e2 = 0,
  prop_missing = 0)

Arguments

Details

TODO

Examples

# TODO

Description

TODO - text

Usage

sim_data_safe(..., data_format = "biallelic", no_invariant_loci = TRUE,
  no_missing_samples = TRUE, no_missing_loci = TRUE,
  max_attempts = 1000)

Arguments

Details

TODO

Examples

# TODO

Description

Overload summary function for class malecot_project

Usage

## S3 method for class 'malecot_project'
summary(object, ...)

Arguments