Main tutorial
Quick-to-run example
A simple use-case that shows how deepSTRAPP can be
used to test for differences in diversification rates between
two trait states along evolutionary times is available here
and within R: vignette("main_tutorial").
This tutorial presents the main functions in a typical
deepSTRAPP workflow.
For more advanced uses,
please refer to the vignettes/tutorials below.
Advanced uses / tutorials
This vignette points to tutorials detailing how to
use the [deepSTRAPP] package beyond the simple use-case
presented in the README file and also available here:
vignette("main_tutorial").
The following tutorials present more advanced usages
of deepSTRAPP. They provide explanations on available arguments and
interpretations of results of deepSTRAPP across multiple types of
data.
4/ Explore the STRAPP test options
● Test
different hypotheses:
vignette("explore_STRAPP_test_types").
- Type of STRAPP tests: two-tailed
vs. one-tailed.
- Continuous: “negative” or “positive” correlation.
- Binary with hypothesis: (A > B) vs. (B > A).
- Multinominal: Hypotheses for all post hoc tests.
6/ Handle uncertainty
● Handle
uncertainty in trait and rate estimates:
vignette("handle_uncertainty").
Explore the three strategies available: - ‘rates_only’: Only accounts
for diversification-rate uncertainty across BAMM posterior samples. -
‘paired’: Accounts for both diversification-rate and ancestral
trait/range reconstruction uncertainty by pairing BAMM posterior samples
with stochastic maps. - ‘full’: Accounts for both diversification-rate
and ancestral reconstruction uncertainty by evaluating every combination
of BAMM posterior samples and stochastic maps.
7/ Import external analyses
● Import
external analyses:
vignette("import_external_analyses").
Import and format results of external analyses of trait-evolution
histories and diversification dynamics, and make them ready-to-use as
inputs for a deepSTRAPP run.