{"id":2244,"date":"2022-05-06T13:47:40","date_gmt":"2022-05-06T12:47:40","guid":{"rendered":"https:\/\/www.microdata.no\/?p=2244"},"modified":"2022-06-15T08:46:44","modified_gmt":"2022-06-15T07:46:44","slug":"new-variants-of-marginal-effects-available-for-logistical-regressions","status":"publish","type":"post","link":"https:\/\/www.microdata.no\/en\/new-variants-of-marginal-effects-available-for-logistical-regressions\/","title":{"rendered":"New variants of marginal effects available for logistical regressions"},"content":{"rendered":"\n<p><strong><em>It is now possible to calculate marginal effects for logistic regression analyzes in other ways than the default value &#8220;overall&#8221;, using the new option &#8220;mfx_at ()&#8221;.<\/em><\/strong><\/p>\n\n\n\n<!--more-->\n\n\n\n<p>Logistic regression analyzes run by the <code>logit<\/code>, <code>probit<\/code> and <code>mlogit<\/code> commands provide logistic coefficient estimates by default. However, many people prefer to look at marginal effects, as these are easier to interpret. For this, the option <code>mfx()<\/code> is used, where you can select the variants <code>dydx<\/code>, <code>dyex<\/code>, <code>eydx<\/code>, and <code>eyex<\/code>. More info about these variants can be found in <a href=\"https:\/\/www.microdata.no\/wp-content\/uploads\/2022\/04\/brukermanual-en-1.pdf\">the user manual chapter 5.6.2<\/a> or by using the command <code>help logit<\/code> (or one of the other logistical commands).<\/p>\n\n\n\n<p>By using the new option <code>mfx_at()<\/code>, you can override the default measure. The following variants are available:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><code>mfx_at(overall)<\/code> (mean of the marginal effects measured over all x values) (default measure if this option is not used)<\/li><li><code>mfx_at(mean)<\/code> (marginal effect measured at mean value of x)<\/li><li><code>mfx_at(median)<\/code> (marginal effect measured at median value of x)<\/li><li><code>mfx_at(zero)<\/code> (marginal effect measured at 0-value of x)<\/li><\/ul>\n\n\n\n<p>The <code>mfx_at()<\/code> option is usually used in combination with <code>mfx()<\/code>, for example:<\/p>\n\n\n<div id=\"rose-block_6275174511e17\" class=\"rose-code codeblock-wrapper\">\n<pre tabindex=\"0\" class=\"codeblock\"><code>logit high_income male married age high_wealth, mfx(dydx) mfx_at(mean)<\/code><\/pre>\n<\/div>\n\n\n<p>However, you may also just use <code>mfx_at()<\/code>. Thus, the standard variant <code>mfx(dydx)<\/code> is used.<\/p>\n\n\n\n<p>The following alternative regression expressions will present the same marginal effect values:<\/p>\n\n\n<div id=\"rose-block_6275181611e18\" class=\"rose-code codeblock-wrapper\">\n<pre tabindex=\"0\" class=\"codeblock\"><code>logit high_income male married age high_wealth, mfx(dydx) mfx_at(overall)\r\nlogit high_income male married age high_wealth, mfx_at(overall)\r\nlogit high_income male married age high_wealth, mfx(dydx)<\/code><\/pre>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>It is now possible to calculate marginal effects for logistic regression analyzes in other ways than the default value &#8220;overall&#8221;, using the new option &#8220;mfx_at ()&#8221;.<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[2],"tags":[],"class_list":["post-2244","post","type-post","status-publish","format-standard","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - 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