selectModel | R Documentation |
Plots log likelihood of the different models and selects the best one based on the maximum likelihood (or specified by the user).
selectModel(
object,
select = NULL,
type = "derivative",
keepBinaryMatrix = TRUE,
keepModels = TRUE,
...
)
object |
Initialized cisTopic object or list of LDA models. |
select |
Number of topics of the selected model. If NULL, the model with the best log likelihood is picked. |
type |
Method for automatic selection of the best number of topics. By default, we use 'derivative' which calculates the second derivative in each point and takes the maximum (recommended with runWarpLDAModels, where curves are less noisy). Alternatively, if set to 'maximum', the model with the maximum log-likelihood is selected (recommended with runCGSModels). For WarpLDA models, the minimum perplexity can be also used to select the best model. |
keepBinaryMatrix |
Whether to keep the binary accessibility matrix within the cisTopic object. |
keepModels |
Whether to keep all the models within the cisTopic object. |
... |
Ignored. |
Returns a cisTopic object (when the input is a cisTopic object) with the selected model stored in object@selected.model, and the log likelihoods of the models in object@log.lik.
The unnormalized (when using runCGSModels) or normalized (when using runWarpLDAModels) cell assignments throughtout the sampling
iterations are stored in cisTopicObject@selected.model$document_expects
; while the corresponding
unnormalized region assignments are stored in cisTopicObject@selected.model$topics
.
bamfiles <- c('example_1.bam', 'example_2.bam', 'example_3.bam')
regions <- 'example.bed'
cisTopicObject <- CreatecisTopicObjectfromBAM(bamfiles, regions)
cisTopicObject <- runCGSModels(cisTopicObject)
cisTopicObject <- selectModel(cisTopicObject)
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