| Title: | Bayesian ANalysis Of Compositional Covariance |
|---|---|
| Description: | BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. |
| Authors: | Emma Schwager [aut, cre], Curtis Huttenhower [aut] |
| Maintainer: | George Weingart <[email protected]>, Curtis Huttenhower <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.31.0 |
| Built: | 2024-10-30 03:37:13 UTC |
| Source: | https://github.com/bioc/banocc |
BAnOCC is a package for inferring correlations between features in
compositional data, where each sample sums to one. It provides one
object, banocc_model and one function, run_banocc
banocc_model has the stan model code to be compiled using
rstan::stan.
run_banocc takes a compiled model, and returns the 'stanfit' object
resulting from a call to rstan::sampling
get_banocc_output takes a 'stanfit' object or the output of
run_banocc and returns a list with the posterior median and
credible interval estimates
This is the literal model used for fitting in Stan
banocc_modelbanocc_model
An object of class character of length 1.
The BAnOCC model as a string to be compiled with
rstan::stan_model
data(compositions_null) ## Not run: compiled_banocc_model <- rstan::stan_model(model_code = banocc_model) ## End(Not run)data(compositions_null) ## Not run: compiled_banocc_model <- rstan::stan_model(model_code = banocc_model) ## End(Not run)
These are the normalized samples corresponding to counts_hard_null.
They should have a very different correlation structure from the counts.
In particular, there should be one strong, positive association which
is not present in the count correlation structure.
compositions_hard_nullcompositions_hard_null
A data frame with 1000 rows (compositional samples) and 9 variables (the features)
A data frame with 1000 compositional samples from 9 features,
generated by dividing each row of counts_hard_null by its sum.
These are the normalized data corresponding to counts_neg_spike. The
count data have one negative feature correlation, but the compositional
correlation strucutre should be different.
compositions_neg_spikecompositions_neg_spike
A data frame with 1000 rows (compositional samples) and 9 variables (the features)
A data frame with 1000 compositional samples from 9 features,
generated by dividing each row of counts_neg_spike by its sum.
These are the normalized samples corresponding to counts_null. They
should have a similar (but not identical) correlation structure.
compositions_nullcompositions_null
A data frame with 1000 rows (compositional samples) and 9 variables (the features)
A data frame with 1000 compositional samples from 9 features,
generated by dividing each row of counts_null by its sum.
These are the normalized data corresponding to counts_pos_spike. The
count data have one positive feature correlation, but the compositional
correlation structure should be different.
compositions_pos_spikecompositions_pos_spike
A data frame with 1000 rows (compositional samples) and 9 variables (the features)
A data frame with 1000 compositional samples from 9 features,
generated by dividing each row of counts_pos_spike by its sum.
Nine features are draw independently from very different log-normal
distributions whose means and variances are positively correlated. This
means that the compositions generated from this dataset
(see compositions_hard_null) should be have a correlation
structure very different from that of these counts.
counts_hard_nullcounts_hard_null
A data frame with 1000 rows (samples) and 9 variables (the features)
A data frame with 1000 unconstrained samples from 9 features.
Nine features are drawn from a log-normal distribution with one negative
correlation. The resulting compositions are in
compositions_neg_spike
counts_neg_spikecounts_neg_spike
A data frame with 1000 rows (samples) and 9 variables (the features)
A data frame with 1000 unconstrained samples from 9 features.
Nine features are drawn independently from similar log-normal
distributions to generate null count data. Because the feature
distributions are very similar, the compositions generated from
this dataset (see compositions_null), should have a correlation
structure similar to that of the counts.
counts_nullcounts_null
A data frame with 1000 rows (the samples) and 9 variables (the features)
A data frame with 1000 unconstrained samples from 9 features.
Nine features are drawn from a log-normal distribution with one positive
correlation. The resulting compositions are in
compositions_pos_spike.
counts_pos_spikecounts_pos_spike
A data frame with 1000 rows (samples) and 9 variables (the features)
A data frame with 1000 unconstrained samples from 9 features.
Takes a model fit from BAnOCC, evaluates convergence and generates appropriate convergence metrics and inference
get_banocc_output(banoccfit, conf_alpha = 0.05, get_min_width = FALSE, calc_snc = TRUE, eval_convergence = TRUE, verbose = FALSE, num_level = 0)get_banocc_output(banoccfit, conf_alpha = 0.05, get_min_width = FALSE, calc_snc = TRUE, eval_convergence = TRUE, verbose = FALSE, num_level = 0)
banoccfit |
Either a |
conf_alpha |
The percentage of the posterior density outside the
credible interval. That is, a |
get_min_width |
A boolean value: should the minimum CI width that includes zero be calculated? |
calc_snc |
Boolean: should the scaled neighborhood criterion be calculated? |
eval_convergence |
Boolean: if 'TRUE', convergence will be evaluated using the Rhat statistic, and the fit output (estimates, credible intervals, etc.) will be missing if this statistic does not indicate convergence. |
verbose |
Print informative statements as the function executes? |
num_level |
The number of indentations to add to the output when
|
Returns a named list with the following elements:
The 1-conf_alpha * 100% credible intervals
The correlation estimates, which are the marginal posterior medians
Only present if the get_min_width
argument is TRUE. The minimum CI width that includes zero for
each correlation.
Only present if the calc_snc argument is
TRUE. The scaled neighborhood criterion for each correlation.
The stanfit object returned by the call to
run_banocc.
Only present if the banoccfit argument is
specified as the output of a call to run_banocc. It will be
missing if banoccfit is specified as a stanfit object.
vignette("banocc-vignette") for more examples.
data(compositions_null) ## Not run: compiled_banocc_model <- rstan::stan_model(model_code=banocc_model) b_fit <- run_banocc(C=compositions_null, compiled_banocc_model=compiled_banocc_model) b_output <- get_banocc_output(banoccfit=b_fit) ## End(Not run)data(compositions_null) ## Not run: compiled_banocc_model <- rstan::stan_model(model_code=banocc_model) b_fit <- run_banocc(C=compositions_null, compiled_banocc_model=compiled_banocc_model) b_output <- get_banocc_output(banoccfit=b_fit) ## End(Not run)
Runs BAnOCC to fit the model and generate appropriate convergence metrics and inference.
run_banocc(compiled_banocc_model, C, n = rep(0, ncol(C)), L = 10 * diag(ncol(C)), a = 0.5, b = 0.01, cores = getOption("mc.cores", 1L), chains = 4, iter = 50, warmup = floor(iter/2), thin = 1, init = NULL, control = NULL, verbose = FALSE, num_level = 0)run_banocc(compiled_banocc_model, C, n = rep(0, ncol(C)), L = 10 * diag(ncol(C)), a = 0.5, b = 0.01, cores = getOption("mc.cores", 1L), chains = 4, iter = 50, warmup = floor(iter/2), thin = 1, init = NULL, control = NULL, verbose = FALSE, num_level = 0)
compiled_banocc_model |
The compiled stan model (as with
|
C |
The dataset as a data frame or matrix. This should be N by P with N samples as the rows and P features as the columns. |
n |
The prior mean for m; vectors of length less than P (the number
of features/columns of |
L |
The prior variance-covariance for m (must be
positive-definite with dimension PxP where P=number of features/columns
in |
a |
The shape parameter of a gamma distribution (the prior on the shrinkage parameter lambda) |
b |
The rate parameter of a gamma distribution (the prior on the shrinkage parameter lambda) |
cores |
Number of cores to use when executing the chains in parallel,
which defaults to 1 but we recommend setting the |
chains |
A positive integer specifying the number of Markov chains. The default is 4. |
iter |
A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000. |
warmup |
A positive integer specifying the number of warmup (aka burnin)
iterations per chain. If step-size adaptation is on (which it is by default),
this also controls the number of iterations for which adaptation is run (and
hence these warmup samples should not be used for inference). The number of
warmup iterations should not be larger than |
thin |
A positive integer specifying the period for saving samples. The default is 1, which is usually the recommended value. |
init |
The initial values as a list (see
|
control |
A named |
verbose |
Print informative statements as the function executes? |
num_level |
The number of indentations to add to the output when
|
Returns a named list with the following elements:
The data formatted as a named list that includes the
input data (C) and the prior parameters (n, L,
a, b)
The stanfit object returned by the call to
sampling
vignette("banocc-vignette") for more examples.
data(compositions_null) ## Not run: compiled_banocc_model <- rstan::stan_model(model_code=banocc_model) b_stanfit <- run_banocc(C=compositions_null, compiled_banocc_model=compiled_banocc_model) ## End(Not run)data(compositions_null) ## Not run: compiled_banocc_model <- rstan::stan_model(model_code=banocc_model) b_stanfit <- run_banocc(C=compositions_null, compiled_banocc_model=compiled_banocc_model) ## End(Not run)