Functions for performing Bayesian similarity regression, and evaluating the probability of association between sets of ontological terms and binary response vector. A random model is compared with one in which the log odds of a true response is linked to the semantic similarity between terms and a latent characteristic ontological profile.
|Date of publication||2016-10-15 18:51:56|
|Maintainer||Daniel Greene <firstname.lastname@example.org>|
|License||GPL (>= 2)|
get_term_marginals: Calculate marginal probability of terms inclusion in 'phi'...
get_terms: Get full set of terms to use in inference procedure based on...
log_BF: Calculate log Bayes factor for similarity the model,...
plot.sim_reg_summary: Plot summary of 'sim_reg_output' object
plot_term_marginals: Create ontological plot of marginal probabilities of terms
print.sim_reg_output: Print 'sim_reg_output' object
print.sim_reg_summary: Print 'sim_reg_summary' object
prob_association: Calculate probability of association between 'y' and 'x'
sim_reg: Similarity regression
SimReg-package: Similarity Regression Functions
sum_log_probs: Calculate sum of log probabilities on log scale without...
summary.sim_reg_output: Get summary of 'sim_reg_output' object
term_marginals: Calculate marginal probability of terms inclusion in 'phi'