{"id":339,"date":"2017-05-05T19:19:00","date_gmt":"2017-05-05T19:19:00","guid":{"rendered":"http:\/\/sites.warnercnr.colostate.edu\/gwhite\/?page_id=339"},"modified":"2017-05-05T19:19:00","modified_gmt":"2017-05-05T19:19:00","slug":"mixture-distributions","status":"publish","type":"page","link":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/mixture-distributions\/","title":{"rendered":"Mixture Distributions"},"content":{"rendered":"

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

\n

Mixture Distributions<\/b><\/span><\/p>\n

Mixture distributions are used to model individual heterogeneity of capture probabilities (<\/span>Pledger 2000<\/a><\/span>, <\/span>Norris and Pollock 1996<\/a><\/span>) in the closed captures<\/a> data type.\u00a0 To change the number of mixture distributions, you have to change data type<\/a>, available under the PIM menu choice. Mixture models have also been added for the occupancy data type<\/a>, and the robust design occupancy data types<\/a>.\u00a0 In addition, <\/span>mixture models for open populations<\/a><\/span> are available for the Cormack-Jolly-Seber data type, the <\/span>Pradel data type<\/a><\/span>, the <\/span>Link-Barker<\/a><\/span> data type, and the Seber parameterization of dead recoveries data type (only the survival rate is included in the mixtures, <\/span>Pledger and Schwarz 2002<\/a><\/span>)..<\/span><\/p>\n

Mixture models consist of a pi parameter that gives the probability of a mixture, with the sum of the pi values equaling 1.\u00a0 In MARK, 1 less pi value is included in the model, so that the last pi value is obtained by substraction.\u00a0 When you specify >2 mixtures, the multinomial logit link function (mlogit link function)<\/a> on pi is useful to maintain this constraint.<\/span><\/p>\n

The PIM for the parameter that is being modeled as a mixture has separate rows for each mixture.\u00a0 So, as an example, for the <\/span>Pradel<\/a><\/span> mixture model<\/a><\/span>, the p<\/i> parameter is modeled as a mixture.\u00a0 Each row of the p PIM corresponds to a mixture.\u00a0 With 2 mixtures, there are 2 rows of p parameters.\u00a0 The same structure is used for the <\/span>closed capture models<\/a><\/span> with <\/span>mixtures<\/a><\/span>.<\/span><\/p>\n

Mixture models can have multiple modes, so care with their optimization should be taken.\u00a0 The simulated annealing algorithm<\/a> included in MARK is a good method to avoid local maxima.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Contents – Index Mixture Distributions Mixture distributions are used to model individual heterogeneity of capture probabilities …<\/p>\n

Mixture Distributions<\/span> Read more \u2192<\/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-339","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/339","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=339"}],"version-history":[{"count":1,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/339\/revisions"}],"predecessor-version":[{"id":340,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/339\/revisions\/340"}],"wp:attachment":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/media?parent=339"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}