Package 'lorad'

Sequence data used in gtrig vignette

Description

Sequence data used in gtrig vignette

Usage

gtrigsamples

Format

gtrigsamples

A data frame with 10,001 rows and 35 columns:

Iteration

MCMC iteration

Posterior

Log of the unnormalized posterior density

Likelihood

Log likelihood

Prior

Log of the prior density

alpha

Shape parameter of the (mean=1) Gamma distribution of among-site rate heterogeneity

edge_length_proportions.1.

Proportion of total tree length used by edge 1

edge_length_proportions.2.

Proportion of total tree length used by edge 2

edge_length_proportions.3.

Proportion of total tree length used by edge 3

edge_length_proportions.4.

Proportion of total tree length used by edge 4

edge_length_proportions.5.

Proportion of total tree length used by edge 5

edge_length_proportions.6.

Proportion of total tree length used by edge 6

edge_length_proportions.7.

Proportion of total tree length used by edge 7

edgelens.1.

Edge length 1

edgelens.2.

Edge length 2

edgelens.3.

Edge length 3

edgelens.4.

Edge length 4

edgelens.5.

Edge length 5

edgelens.6.

Edge length 6

edgelens.7.

Edge length 7

er.1.

Exchangeability parameter for A to C

er.2.

Exchangeability parameter for A to G

er.3.

Exchangeability parameter for A to T

er.4.

Exchangeability parameter for C to G

er.5.

Exchangeability parameter for C to T

er.6.

Exchangeability parameter for G to T

pi.1.

Nucleotide relative frequency for A

pi.2.

Nucleotide relative frequency for C

pi.3.

Nucleotide relative frequency for G

pi.4.

Nucleotide relative frequency for t

pinvar

Proportion of invariable sites

site_rates.1.

Rate for site category 1

site_rates.2.

Rate for site category 1

site_rates.3.

Rate for site category 1

site_rates.4.

Rate for site category 1

tree_length

Tree length (sum of all edge lengths) in substitutions per site

Source

The program RevBayes (version 1.2.1) was used to obtain a sample from the Bayesian posterior distribution for 5 green plant rbcL sequences under a GTR+I+G model.

---

Sequence data used in k80 vignette

Description

Sequence data used in k80 vignette

Usage

k80samples

Format

k80samples

A data frame with 10,000 rows and 4 columns:

iter

Iteration

log.kernel

Log unnormalized posterior

edgelen

Edge length in substitutions per site

kappa

Transition transversion rate ratio

Source

doi:10.1093/sysbio/syad007

---

Calculate a sum on log scale

Description

Calculates the (natural) log of a sum without leaving the log scale by factoring out the largest element.

Usage

lorad_calc_log_sum(logx)

Arguments

logx	Numeric vector in which elements are on log scale

Value

The log of the sum of the (exponentiated) elements supplied in logx

---

Calculates the LoRaD estimate of the marginal likelihood

Description

Provided with a data frame containing sampled paraneter vectors and a dictionary relating column names to parameter types, returns a named character vector containing the following quantities:

• logML (the estimated log marginal likelihood)

• nsamples (number of samples)

• nparams (length of each parameter vector)

• training_frac (fraction of samples used for training)

• tsamples (number of samples used for training)

• esamples (number of sampled used for etimation)

• coverage (nominal fraction of the estimation sampled used)

• esamplesused (number of estimation samples actually used for estimation)

• realized_coverage (actual fraction of estimation sample used)

• rmax (lowest radial distance: defines boundary of working parameter space)

• log_delta (volume under the unnormalized posterior inside working parameter space)

Usage

lorad_estimate(params, colspec, training_frac, training_mode, coverage)

Arguments

params	Data frame in which rows are sample points and columns are parameters, except that last column holds the log posterior kernel
colspec	Named character vector associating column names in params with column specifications
training_frac	Number between 0 and 1 specifying the training fraction
training_mode	One of random, left, or right, specifying how training fraction is chosen
coverage	Number between 0 and 1 specifying fraction of training sample used to compute working parameter space

Value

Named character vector of length 11.

Examples

normals <- rnorm(1000000,0,10)
prob_normals <- dnorm(normals,0,10,log=TRUE) 
proportions <- rbeta(1000000,1,2)
prob_proportions <- dbeta(proportions,1,2,log=TRUE)
lengths <- rgamma(1000000, 10, 1)
prob_lengths <- dgamma(lengths,10,1,log=TRUE)
paramsdf <- data.frame(
    normals,prob_normals,
    proportions, prob_proportions,
    lengths, prob_lengths)
columnkey <- c(
    "normals"="unconstrained", 
    "prob_normals"="posterior", 
    "proportions"="proportion", 
    "prob_proportions"="posterior", 
    "lengths"="positive", 
    "prob_lengths"="posterior")
results <- lorad_estimate(paramsdf, columnkey, 0.5, 'random', 0.1)
lorad_summary(results)

---

Transforms unconstrained parameters to have the same location and scale

Description

Standardizes parameters that have already been transformed (if necessary) to have unconstrained support. Standardization involves subtracting the sample mean and dividing by the sample standard deviation. Assumes that the log posterior kernel (i.e. the log of the unnormalized posterior) is the last column in the supplied data frame.

Usage

lorad_standardize(df, coverage)

Arguments

df	Data frame containing a column for each model parameter sampled and a final column of log posterior kernel values
coverage	Fraction of the training sample used to compute working parameter space

Value

List containing the log-Jacobian of the standardization transformation, the inverse square root matrix, a vector of column means, and rmax (radial distance to furthest point in working parameter space)

---

Transforms training sample using training sample means and standard deviations

Description

Transforms training sample using training sample means and standard deviations

Usage

lorad_standardize_estimation_sample(standardinfo, y)

Arguments

standardinfo	List containing the log Jacobian of the standardization transformation, the inverse square root matrix, the column means, and rmax (the radial distance representing the edge of the working parameter space)
y	Data frame containing a column for each transformed model parameter in the estimation sample, with last column being the log kernel values

Value

A new data frame consisting of the standardized estimation sample with log kernel in last column

---

Summarize output from lorad_estimate()

Description

Summarize output from lorad_estimate()

Usage

lorad_summary(results)

Arguments

results

Named character vector returned from lorad_estimate()

Value

String containing a summary of the supplied results object

Examples

normals <- rnorm(1000000,0,10)
prob_normals <- dnorm(normals,0,10,log=TRUE) 
paramsdf <- data.frame(normals,prob_normals)
columnkey <- c("normals"="unconstrained", "prob_normals"="posterior")
results <- lorad_estimate(paramsdf, columnkey, 0.5, 'left', 0.1)
lorad_summary(results)

---

Log (or log-ratio) transform parameters having constrained support

Description

Log-transforms parameters with support (0,infinity), log-ratio transforms K-dimensional parameters with support a (K-1)-simplex, logit transforms parameters with support [0,1], and leaves unchanged parameters with unconstrained support (-infinity, infinity).

Usage

lorad_transform(params, colspec)

Arguments

params	Data frame containing a column for each model parameter sampled as well as one or more columns that, when summed, constitute the log joint posterior kernel
colspec	Named character vector matching each column name in params with a column specification

Value

A new data frame comprising transformed parameter values with a final column holding the log joint posterior kernel

---