Description Usage Arguments Methods (by generic) Slots
hdpSampleMulti class for multiple independent hdpSampleChain objects for the same HDP
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ## S4 method for signature 'hdpSampleMulti'
as.list(x, ...)
## S4 method for signature 'hdpSampleMulti'
chains(x, ...)
## S4 method for signature 'hdpSampleMulti'
numcomp(x)
## S4 method for signature 'hdpSampleMulti'
prop.ex(x)
## S4 method for signature 'hdpSampleMulti'
comp_cos_merge(x)
## S4 method for signature 'hdpSampleMulti'
comp_categ_counts(x)
## S4 method for signature 'hdpSampleMulti'
comp_dp_counts(x)
## S4 method for signature 'hdpSampleMulti'
comp_categ_distn(x)
## S4 method for signature 'hdpSampleMulti'
comp_dp_distn(x)
|
x |
Object of class hdpSampleMulti |
... |
unused |
as.list
: Convert to list class
chains
: Get list of hdpSampleChain objects
numcomp
: Get number of extracted components for hdpSampleMulti
prop.ex
: Get proportion of dataset explained (on average) for hdpSampleMulti
comp_cos_merge
: Get cos.merge setting for hdpSampleMulti
comp_categ_counts
: Get sample vs category counts for each component
comp_dp_counts
: Get sample vs component counts for each DP
comp_categ_distn
: Get mean distribution over data categories for each component
comp_dp_distn
: Get mean distribution over components for each DP
chains
List of hdpSampleChain objects storing multiple independent runs of the posterior sampling chain for the same data and HDP struct
numcomp
Number of global components extracted by hdp_extract_components
(not including component 0)
prop.ex
(Average) proportion of dataset explained by the extracted components
comp_cos_merge
cos.merge
setting used by hdp_extract_components
comp_categ_counts
List of matrices (one for each component) counting the sample-category data assignment across all DP nodes. Number of rows is the number of posterior samples, and number of columns is the number of data categories.
comp_dp_counts
List of matrices (one for each DP) counting sample-component assignment (aggregating across data categories). Number of rows is the number of posterior samples, and number of columns is the number of components.
comp_categ_distn
List with elements "mean" and "cred.int", containing matrices with the mean (and lower/upper 95% credibility interval) distribution over data categories for each component. Number of rows is the number of components, and number of columns is the number of data categories. Rows sum to 1.
comp_dp_distn
List with elements "mean" and "cred.int", containing matrices with the mean (and lower/upper 95% credibility interval) distribution over components for each DP. Number of rows is the number of DPs, and number of columns is the number of components. Rows sum to 1.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.