Run BRS
BRS.RdThis function runs BRS. It allows the user either to run BRS only once on the original data or to run BRS on bootstrapped samples
Usage
BRS(
df,
Y,
maxLen,
trainProp,
numIter = 500L,
numChain = 2L,
numMine = 5000L,
supp = 5L,
alpha_1 = 50L,
beta_1 = 1L,
alpha_2 = 50L,
beta_2 = 1L,
prior_type = "beta",
alpha_l = NULL,
beta_l = NULL,
lambda = NULL,
nu = NULL,
print = FALSE,
bootstrap = FALSE,
reps = 1L,
sampleSize = NULL,
seed = NULL
)Arguments
- df
dataframe of binary features
- Y
vector of binary outcome
- maxLen
integer maximum length of rules
- trainProp
numeric for proportion of data to use as training data
- numIter
integer for number of iterations in simulated annealing
- numChain
integer for number of chains of simulated annealing
- numMine
integer for number of rules to mine
- supp
integer for percent minimum support
- alpha_1
numeric for alpha_+ from the paper
- beta_1
beta_+
- alpha_2
alpha_-
- beta_2
beta_-
- prior_type
string for the prior type. Either "beta" for BRS-BetaBinomial or "poisson" for BRS-Poisson
- alpha_l
vector of alpha_l for l=1...maxLen. If set to NULL and prior_type="beta", values will be automatically generated. Ignored if prior_type="poisson"
- beta_l
vector of beta_l for l=1...maxLen. If set to NULL and prior_type="beta", values will be automatically generated. Ignored if prior_type="poisson"
- lambda
numeric rate parameter for the prior on the number of rules
- nu
numeric rate parameter for the prior on the length of rules
logical whether to print progress of algorithm
- bootstrap
logical for whether to bootstrap
- reps
integer for number of bootstrap reps
- sampleSize
integer for bootstrap sample size. If set to NULL, default is the number of observations in data