banocc: Bayesian ANalysis Of Compositional Covariance

#' Simulated count data with no feature correlations
#'
#' 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 \code{compositions_null}), should have a correlation
#'   structure similar to that of the counts.
#'
#' @format A data frame with 1000 rows (the samples)
#'   and 9 variables (the features)
#' @return A data frame with 1000 unconstrained samples from 9 features.
"counts_null"

#' Simulated compositional data with no feature correlations
#'
#' These are the normalized samples corresponding to \code{counts_null}. They
#'   should have a similar (but not identical) correlation structure.
#'
#' @format A data frame with 1000 rows (compositional samples) and 9
#'   variables (the features)
#' @return A data frame with 1000 compositional samples from 9 features,
#'   generated by dividing each row of \code{counts_null} by its sum.
"compositions_null"

#' Simulated count data with no feature correlations
#'
#' 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 \code{compositions_hard_null}) should be have a correlation
#'   structure very different from that of these counts.
#'
#' @format A data frame with 1000 rows (samples) and 9 variables (the
#'   features)
#' @return A data frame with 1000 unconstrained samples from 9 features.
"counts_hard_null"

#' Simulated compositional data with no feature correlations
#'
#' These are the normalized samples corresponding to \code{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.
#'
#' @format A data frame with 1000 rows (compositional samples) and 9
#'   variables (the features)
#' @return A data frame with 1000 compositional samples from 9 features,
#'   generated by dividing each row of \code{counts_hard_null} by its sum.
"compositions_hard_null"

#' Simulated count data with one positive feature correlation
#'
#' Nine features are drawn from a log-normal distribution with one positive
#'   correlation. The resulting compositions are in
#'   \code{compositions_pos_spike}.
#'
#' @format A data frame with 1000 rows (samples) and 9 variables (the
#'   features)
#' @return A data frame with 1000 unconstrained samples from 9 features.
"counts_pos_spike"

#' Simulated compositional data with a positive count correlation
#'
#' These are the normalized data corresponding to \code{counts_pos_spike}. The
#'   count data have one positive feature correlation, but the compositional
#'   correlation structure should be different.
#'
#' @format A data frame with 1000 rows (compositional samples) and 9
#'   variables (the features)
#' @return A data frame with 1000 compositional samples from 9 features,
#'   generated by dividing each row of \code{counts_pos_spike} by its sum.
"compositions_pos_spike"

#' Simulated count data with one negative feature correlation
#'
#' Nine features are drawn from a log-normal distribution with one negative
#'   correlation. The resulting compositions are in
#'   \code{compositions_neg_spike}
#'
#' @format A data frame with 1000 rows (samples) and 9 variables (the
#'   features)
#' @return A data frame with 1000 unconstrained samples from 9 features.
"counts_neg_spike"

#' Simulated compositional data with a negative count correlation
#'
#' These are the normalized data corresponding to \code{counts_neg_spike}. The
#'   count data have one negative feature correlation, but the compositional
#'   correlation strucutre should be different.
#'
#' @format A data frame with 1000 rows (compositional samples) and 9
#'   variables (the features)
#' @return A data frame with 1000 compositional samples from 9 features,
#'   generated by dividing each row of \code{counts_neg_spike} by its sum.
"compositions_neg_spike"