plot_tree_with_post | R Documentation |
Plot a tree and the PGLMM posterior on phylogenetic effects
plot_tree_with_post(
tree_file,
meta_file,
fit,
labels,
covariates = c("age", "gender"),
offset = NULL,
outcome = "crc",
omit_na = FALSE,
ladderize = TRUE,
color_bars = FALSE,
verbose = TRUE,
trim_pattern = NULL,
return_tree_df = FALSE
)
tree_file |
either a path to a tree file readable by ape::read.tree() or an object of class "phylo" that is already read into R. |
meta_file |
either a data frame of metadata or a path to file containing the metadata |
fit |
a pglmm fit from |
labels |
the ordered tip labels from the tree |
covariates |
covariates to account for (as a vector of strings) |
offset |
a variable to include as an offset |
outcome |
the name of the outcome variable |
omit_na |
logical indicating whether to omit incomplete cases of the metadata |
ladderize |
logical indicating whether to run |
color_bars |
if true, show color bars below the plot showing the covariates and outcome variables. |
trim_pattern |
optional pattern to trim from tip labels of the tree |
return_tree_df |
if true, return a list containing 1) the plot, 2) the segment data frame, and 3) the labelled terminal segment data frame. Otherwise, just return the plot. |
The whiskers of each box plot are the 90% posterior intervals, the box is the 50% interval, and the middle line is the posterior mean.
The labels
needs to contain the leaf labels in the order produced by the
Cholesky factorization of the correlation matrix (which is how the data are passed to the
sampler). This is not necessarily the order of the leaves on the x-axis of the tree, nor the
order of the samples in the metadata. The simplest way to get the sample_ids in the proper
order is to take the sample_id
column from the model_input result from anpan_pglmm().
either the plot or (if return_tree_df = TRUE) a list containing the plot, the segment df, the terminal segment df, and the yrep df.
## Not run:
# Using the result simulated in the vignette:
plot_tree_with_post(tr, metadata,
fit = result$pglmm_fit,
labels = result$model_input$sample_id,
outcome = 'outcome',
covariate = 'covariate',
color_bars = TRUE)
## End(Not run)
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