{"id":363,"date":"2017-05-05T19:45:40","date_gmt":"2017-05-05T19:45:40","guid":{"rendered":"http:\/\/sites.warnercnr.colostate.edu\/gwhite\/?page_id=363"},"modified":"2017-05-05T19:45:40","modified_gmt":"2017-05-05T19:45:40","slug":"chat","status":"publish","type":"page","link":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/chat\/","title":{"rendered":"CHat"},"content":{"rendered":"

Contents<\/a> – Index<\/a><\/span><\/p>\n

\n

CHat<\/i><\/b><\/span><\/p>\n

Enter a new value for the quasi-likelihood parameter (c<\/i>-hat).\u00a0 Typically, this value is 1.0000, meaning that the data are not over-dispersed, i.e., no extrabinomial variation.\u00a0 However, you may want to correct for overdispersion by increasing the value.\u00a0 An estimator of c<\/i>-hat is the <\/span>deviance<\/a><\/span> divided by its degrees of freedom, although this estimator generally is biased high for finite sample sizes.<\/span><\/p>\n

The value for c<\/i> cannot be <1, because there is no biologically reasonable model that would generate underdispersed data.<\/span><\/p>\n

The value of c-hat is used to compute the QAIC<\/a> (quasi-AIC) for a model with k<\/i> parameters by the following formula:<\/span><\/p>\n

QAIC = -2log Likelihood\/c-hat + 2K<\/i><\/span><\/p>\n

and the QAIC<\/a> by the formula:<\/span><\/p>\n

QAICc = -2log Likelihood\/c-hat + 2K<\/i> + 2K<\/i>(K<\/i> + 1)\/(n<\/i>-ess – K<\/i> – 1)<\/span><\/p>\n

where n<\/i>-ess is the effective sample size.<\/span><\/p>\n

Values of c-hat > 1 can also be used to compute profile likelihod confidense intervals<\/a>.<\/span><\/p>\n

Methods in MARK to estimate c are the <\/span>median chat<\/a><\/span> and the <\/span>Fletcher chat<\/a><\/span> estimators.<\/span><\/p>\n

More details on c-hat are provided on the WWW page <\/span>http:\/\/warnercnr.colostate.edu\/~gwhite\/fw663\/Mark.html<\/a><\/span>.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Contents – Index CHat Enter a new value for the quasi-likelihood parameter (c-hat).\u00a0 Typically, this value is 1.0000, meaning that the data are not over-dispersed, i.e., no extrabinomial variation.\u00a0 However, you may want to correct for overdispersion by increasing the …<\/p>\n

CHat<\/span> Read More »<\/a><\/p>\n","protected":false},"author":117,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-363","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/363","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/users\/117"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/comments?post=363"}],"version-history":[{"count":1,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/363\/revisions"}],"predecessor-version":[{"id":364,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/363\/revisions\/364"}],"wp:attachment":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/media?parent=363"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}