LogicRegr: A wrapper for running the Bayesian logic regression based...

In aliaksah/EMJMCMC2016: EMJMCMC

A wrapper for running the Bayesian logic regression based inference in a easy to use way

Usage

LogicRegr(formula, data, family = "Gaussian",prior = "J",report.level = 0.5, d = 20, cmax = 5, kmax = 20, p.and = 0.9, p.not = 0.05, p.surv = 0.1,ncores = -1, n.mods = 1000 ,advanced)

Arguments

formula	a formula object for the model to be addressed
data	a data frame object containing variables and observations corresponding to the formula used
family	a string taking values of either "Gaussian" or "Bernoulli" correspodning to the linear or logistic Bayesian logic regression contexts
prior	character values "J" or "G" corresponing either to Jeffey's or robust g prior
report.level	a numeric value in (0,1) specifying the treshold for detections based on the marginal inclusion probabilities
d	population size for the GMJMCMC algorithm
cmax	the maximal allowed depth of logical expressions to be considered
kmax	the maximal number of logical expressions per model
p.and	probability of AND parameter of GMJMCMC algorithm
p.not	probability of applying logical NOT in GMJMCMC algorithm
p.surv	minimal survival probabilities for the features to be allowed to enter the next population
ncores	the maximal number of cores (and GMJMCMC threads) to be addressed in the analysis
n.mods	the number of the best models in the thread to calculate marginal inclusion probabilities
advanced	should only be adrresed by experienced users to tune advanced parameters of GMJMCMC, advanced corresponds to the vector of tuning parameters of runemjmcmc function

Value

a list of

feat.stat	detected logical expressions and their marginal inclusion probabilities
predictions	NULL currently, since LogrRegr function is not designed for predictions at the moment, which is still possible in its expert mother function pinferunemjmcmc
allposteriors	all visited by GMJMCMC logical expressions and their marginal inclusion probabilities
threads.stats	a vector of detailed outputs of individual ncores threads of GMJMCMC run

See Also

EMJMCMC::runemjmcmc, EMJMCMC::pinferunemjmcmc

Examples

set.seed(040590)
X1= as.data.frame(array(data = rbinom(n = 50*1000,size = 1,prob = runif(n = 50*1000,0,1)),dim = c(1000,50)))
Y1=rnorm(n = 1000,mean = 1+0.7*(X1$V1*X1$V4) + 0.8896846*(X1$V8*X1$V11)+1.434573*(X1$V5*X1$V9),sd = 1)
X1$Y1=Y1

#specify the initial formula
formula1 = as.formula(paste(colnames(X1)[51],"~ 1 +",paste0(colnames(X1)[-c(51)],collapse = "+")))
data.example = as.data.frame(X1)


#run the inference with robust g prior
res4G = LogicRegr(formula = formula1,data = data.example,family = "Gaussian",prior = "G",report.level = 0.5,d = 15,cmax = 2,kmax = 15,p.and = 0.9,p.not = 0.01,p.surv = 0.2,ncores = 32)
print(res4G$feat.stat)
#run the inference with Jeffrey's prior
res4J = LogicRegr(formula = formula1,data = data.example,family = "Gaussian",prior = "J",report.level = 0.5,d = 15,cmax = 2,kmax = 15,p.and = 0.9,p.not = 0.01,p.surv = 0.2,ncores = 32)
print(res4J$feat.stat)

aliaksah/EMJMCMC2016 documentation built on July 27, 2023, 5:48 a.m.