lr.test | R Documentation |
lr.test allows to test between two nested ghype models whether there is enough evidence for the alternative (more complex) model compared to the null model.
lr.test(
nullmodel,
altmodel,
df = NULL,
Beta = TRUE,
seed = NULL,
nempirical = NULL,
parallel = FALSE,
returnBeta = FALSE,
method = NULL
)
nullmodel |
ghype object. The null model |
altmodel |
ghype object. The alternative model |
df |
optional scalar. the number of degrees of freedom. |
Beta |
boolean, whether to use empirical Beta distribution approximation. Default TRUE |
seed |
scalar, seed for the empirical distribution. |
nempirical |
optional scalar, number of replicates for empirical beta distribution. |
parallel |
optional, number of cores to use or boolean for parallel computation. If passed TRUE uses all cores-1, else uses the number of cores passed. If none passed performed not in parallel. |
returnBeta |
boolean, return estimated parameters of Beta distribution? Default FALSE. |
method |
string, for internal use |
p-value of test. If returnBeta=TRUE returns the p-value together with the parameters of the beta distribution.
data("adj_karate")
regularmodel <- regularm(graph = adj_karate, directed = FALSE, selfloops = FALSE)
confmodel <- scm(graph = adj_karate, directed = FALSE, selfloops = FALSE)
lr.test(nullmodel = regularmodel, altmodel = confmodel, seed = 123)
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