Description Usage Arguments Methods (by generic) Slots
hdpSampleChain class for posterior samples off one MCMC chain
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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ## S4 method for signature 'hdpSampleChain'
as.list(x, ...)
## S4 method for signature 'hdpSampleChain'
sampling_seed(x, ...)
## S4 method for signature 'hdpSampleChain'
hdp_settings(x, ...)
## S4 method for signature 'hdpSampleChain'
final_hdpState(x, ...)
## S4 method for signature 'hdpSampleChain'
lik(x, ...)
## S4 method for signature 'hdpSampleChain'
numcluster(x, ...)
## S4 method for signature 'hdpSampleChain'
cp_values(x, ...)
## S4 method for signature 'hdpSampleChain'
clust_categ_counts(x, ...)
## S4 method for signature 'hdpSampleChain'
clust_dp_counts(x, ...)
## S4 method for signature 'hdpSampleChain'
numcomp(x)
## S4 method for signature 'hdpSampleChain'
prop.ex(x)
## S4 method for signature 'hdpSampleChain'
comp_cos_merge(x)
## S4 method for signature 'hdpSampleChain'
comp_categ_counts(x)
## S4 method for signature 'hdpSampleChain'
comp_dp_counts(x)
## S4 method for signature 'hdpSampleChain'
comp_categ_distn(x)
## S4 method for signature 'hdpSampleChain'
comp_dp_distn(x)
|
x |
Object of class hdpSampleChain |
... |
unused |
as.list
: Convert to list class
sampling_seed
: Get random seed used by hdp_posterior
hdp_settings
: Get settings of posterior sampling chain
final_hdpState
: Get hdpState object from the end of the posterior sampling chain
lik
: Get likelihood of data given model over all iterations
numcluster
: Get the number of clusters for each posterior sample
cp_values
: Get matrix of concentration parameter values for each posterior sample
clust_categ_counts
: Get category vs cluster counts for each posterior sample
clust_dp_counts
: Get dp node vs cluster counts for each posterior sample
numcomp
: Get number of extracted components for hdpSampleChain
prop.ex
: Get proportion of dataset explained (on average) for hdpSampleChain
comp_cos_merge
: Get cos.merge setting for hdpSampleChain
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
seed
Random seed used by hdp_posterior
settings
Settings of the posterior sampling chain: burnin, n (number of samples collected), space (iters between samples), cpiter (con param moves between iters)
hdp
hdpState object after the final iteration
lik
Likelihood of data given model at each iteration
numcluster
Number of raw data clusters in each posterior sample
cp_values
Matrix of concentration parameter values (one column for each parameter) in each posterior sample (rows).
clust_categ_counts
List of matrices (one from each posterior sample) counting the category-cluster data assignment across all DP nodes. Number of rows is the number of categories (constant), and number of columns is the number of clusters in that posterior sample (variable).
clust_dp_counts
List of matrices (one from each posterior sample) counting within-DP cluster assignment (aggregating across data categories). Number of rows is the number of DPs (constant), and number of columns is the number of clusters in that posterior sample (variable).
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.
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