summaryBVS
(Package: BVS) :
Calculates Posterior Summaries for BVS Methods
This function calculates the global and marginal Bayes Factors that give the strength of evidence of there being an association in the overall set of variants of interest, the individual genes of interest (if specified) and the individual variants of interest.
● Data Source:
CranContrib
● Keywords: Posterior Summaries
● Alias: summaryBVS
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sampleBVS
(Package: BVS) :
Sampling Algorithm for Bayesian Variant Selection Methods
This function performs a basic MH Sampling algorithm to sample models from the model space when enumeration is not possible. For informative marginal inclusion probabilities the algorithm also performs a basic MCMC algorithm to sample the effects of the predictor-level covariates (alpha).
● Data Source:
CranContrib
● Keywords: model search
● Alias: sampleBVS
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RareResults
(Package: BVS) :
Example Summary From 100K iterations of sampleBVS with Rare Data
Summary from 100K iterations of sampleBVS with the Rare variant data set RareData using summaryBVS. This was ran with rare=TRUE to correspond to the BRI analysis of Quintana and Conti (2011) and with a burnin of 1000 iterations.
● Data Source:
CranContrib
● Keywords: sample summary output
● Alias: RareResults
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RareBVS.out
(Package: BVS) :
Example Output From 100K iterations of sampleBVS with Rare Data
Output from 100K iterations of sampleBVS with the Rare variant data set RareData. This was ran with rare=TRUE to correspond to the BRI analysis of Quintana and Conti (2011).
● Data Source:
CranContrib
● Keywords: sample output
● Alias: RareBVS.out
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0 images
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plotBVS
(Package: BVS) :
Image Plots for top Variant and Region Inclusions
This function allows the user to create image plots of the top variants and top Regions (any user specified set of variants such as pathways or genes) included in the top models. Variants and Regions are ordered based on marginal BF and regional BF which are plotted on the right axis. The width of the inclusion blocks are proportional to the posterior model probability that the variant or region is included in.
● Data Source:
CranContrib
● Keywords: image plot
● Alias: plotBVS
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6 images
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Informresults.NI
(Package: BVS) :
Example Summary From 100K iterations of sampleBVS with Informative Data
Summary from 100K iterations of sampleBVS with the informative study-based data set InformData using summaryBVS. This was ran with inform=FALSE so that the analysis corresponds to the basic Bayesian model uncertainty framework where we assume that the effects of the predictor-level covariates are 0 (alpha=0).
● Data Source:
CranContrib
● Keywords: sample summary output
● Alias: Informresults.NI
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0 images
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Informresults.I
(Package: BVS) :
Example Summary From 100K iterations of sampleBVS with Informative Data
Summary from 100K iterations of sampleBVS with the informative study-based data set InformData using summaryBVS. This was ran with inform=TRUE and gene based predictor-level covariates so that the analysis follows iBMU framework described in Quintana and Conti (submitted) where we sample that the effects of the predictor-level covariates.
● Data Source:
CranContrib
● Keywords: sample summary output
● Alias: Informresults.I
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0 images
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InformBVS.NI.out
(Package: BVS) :
Example Output From 100K iterations of sampleBVS with Informative Data
Output from 100K iterations of sampleBVS with the informative study-based data set InformData. This was ran with inform=FALSE so that the analysis corresponds to the basic Bayesian model uncertainty framework where we assume that the effects of the predictor-level covariates are 0 (alpha=0).
● Data Source:
CranContrib
● Keywords: sample output
● Alias: InformBVS.NI.out
●
0 images
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InformBVS.I.out
(Package: BVS) :
Example Output From 100K iterations of sampleBVS with Informative Data
Output from 100K iterations of sampleBVS with the informative study-based data set InformData. This was ran with inform=TRUE and gene based predictor-level covariates so that the analysis follows iBMU framework described in Quintana and Conti (submitted) where we sample that the effects of the predictor-level covariates.
● Data Source:
CranContrib
● Keywords: sample output
● Alias: InformBVS.I.out
●
0 images
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hapBVS
(Package: BVS) :
Function to estimate and report a set of haplotypes given a subset of variants
This function takes a subset of variants and estimates a set of haplotypes. Only haplotypes with a frequency greater than min.Hap.freq are reported.
● Data Source:
CranContrib
● Keywords: haplotypes
● Alias: hapBVS
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