lr.test.Rd
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 |
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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)#> #> LR test #> #> data: #> lr = 300.34, df = 33, p-value < 2.2e-16 #> alternative hypothesis: one.sided #> 95 percent confidence interval: #> 19.67212 51.84259 #>