{"id":2900,"date":"2012-07-31T05:45:00","date_gmt":"2012-07-31T05:45:00","guid":{"rendered":"http:\/\/cloudcomputing.sys-con.com\/node\/2305541"},"modified":"2012-07-31T05:45:00","modified_gmt":"2012-07-31T05:45:00","slug":"big-data-generalized-linear-models-with-revolution-r-enterprise","status":"publish","type":"post","link":"https:\/\/icloud.pe\/blog\/big-data-generalized-linear-models-with-revolution-r-enterprise\/","title":{"rendered":"Big Data Generalized Linear Models with Revolution R Enterprise"},"content":{"rendered":"<p>R&#8217;s glm function for generalized linear modeling is very powerful and flexible: it supports all of the standard model types (binomial\/logistic, Gamma, Poisson, etc.) and in fact you can fit any distribution in the exponential family (with the family argument). But if you want to use it on a data set with millions or rows, and especially with more than a couple of dozen variables (or even just a few categorical variables with many levels), this is a big computational task that quickly grows in time as the data gets larger, or even exhaust the available memory. The rxGlm function&#8230;<\/p>\n<p>            David Smith<\/p>\n<p><a href=\"http:\/\/cloudcomputing.sys-con.com\/node\/2305541\" >read more<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R&#8217;s glm function for generalized linear modeling is very powerful and flexible: it supports all of the standard model types (binomial\/logistic, Gamma, Poisson, etc.) and in fact you can fit any distribution in the exponential family (with the family ar&#8230;<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-2900","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/2900","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/comments?post=2900"}],"version-history":[{"count":0,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/2900\/revisions"}],"wp:attachment":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/media?parent=2900"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/categories?post=2900"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/tags?post=2900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}