Searching across hundreds of databases
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
qvality - Nonparametric estimation of posterior error probabilities (RRID:SCR_004309)Copy Citation Copied
URL: http://noble.gs.washington.edu/proj/qvality/
Proper Citation: qvality - Nonparametric estimation of posterior error probabilities (RRID:SCR_004309)
Description: qvality estimates q-values and posterior error probabilities directly from score distributions. The method can be accessed via a web interface or downloaded as stand-alone software (C++ source code and binaries are available under MIT license). The qvality web server allows you to use qvality to compute posterior error probability and q-values for your data. There are two input modes: *Input the empirical score distribution and a corresponding null score distribution. The two inputs do not have to contain the same numbers of scores. *Input only the empirical p-value distribution. In this case, you must use p-values rather than raw scores. In either mode, the output is the same: a three-column file in which the first column contains sorted observed scores, the second column contains estimated q-values, and the third column contains estimated posterior error probabilities. Qvality is a C++ program for estimating two types of standard statistical confidence measures: the q-value, which is an analog of the p-value that incorporates multiple testing correction, and the posterior error probability (PEP, also known as the local false discovery rate), which corresponds to the probability that a given observation is drawn from the null distribution. In computing q-values, qvality employs a standard bootstrap procedure to estimate the prior probability of a score being from the null distribution; for PEP estimation, qvality relies upon non-parametric logistic regression. Relative to other tools for estimating statistical confidence measures, qvality is unique in its ability to estimate both types of scores directly from a null distribution, without requiring the user to calculate p-values.
Synonyms: qvality
Resource Type: source code, computation service resource, software resource, binary executable
Defining Citation: PMID:19193729
Expand Allhas parent organization |
We found {{ ctrl2.mentions.total_count }} mentions in open access literature.
We have not found any literature mentions for this resource.
We are searching literature mentions for this resource.
Most recent articles:
{{ mention._source.dc.creators[0].familyName }} {{ mention._source.dc.creators[0].initials }}, et al. ({{ mention._source.dc.publicationYear }}) {{ mention._source.dc.title }} {{ mention._source.dc.publishers[0].name }}, {{ mention._source.dc.publishers[0].volume }}({{ mention._source.dc.publishers[0].issue }}), {{ mention._source.dc.publishers[0].pagination }}. (PMID:{{ mention._id.replace('PMID:', '') }})
Check
for all resource mentions.
A list of researchers who have used the resource and an author search tool
A list of researchers who have used the resource and an author search tool. This is available for resources that have literature mentions.
No rating or validation information has been found for qvality - Nonparametric estimation of posterior error probabilities.
No alerts have been found for qvality - Nonparametric estimation of posterior error probabilities.
Source: SciCrunch Registry
Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
