{"id":1636,"date":"2023-07-25T13:39:35","date_gmt":"2023-07-25T13:39:35","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bb%d1%96%d1%81%d0%be%d0%b2%d0%b0-%d0%b4%d1%96%d0%bb%d1%8f%d0%bd%d0%ba%d0%b0-%d0%b2-%d1%80\/"},"modified":"2023-07-25T13:39:35","modified_gmt":"2023-07-25T13:39:35","slug":"%d0%bb%d1%96%d1%81%d0%be%d0%b2%d0%b0-%d0%b4%d1%96%d0%bb%d1%8f%d0%bd%d0%ba%d0%b0-%d0%b2-%d1%80","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bb%d1%96%d1%81%d0%be%d0%b2%d0%b0-%d0%b4%d1%96%d0%bb%d1%8f%d0%bd%d0%ba%d0%b0-%d0%b2-%d1%80\/","title":{"rendered":"\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043b\u0456\u0441\u043e\u0432\u0443 \u0434\u0456\u043b\u044f\u043d\u043a\u0443 \u0432 \u0440"},"content":{"rendered":"

<\/p>\n

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\u041b\u0456\u0441\u043e\u0432\u0430 \u0434\u0456\u043b\u044f\u043d\u043a\u0430<\/strong> (\u0456\u043d\u043e\u0434\u0456 \u0457\u0457 \u043d\u0430\u0437\u0438\u0432\u0430\u044e\u0442\u044c \u00ab\u0431\u043b\u043e\u0431\u0431\u043e\u0433\u0440\u0430\u043c\u043e\u044e\u00bb) \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0432 \u043c\u0435\u0442\u0430-\u0430\u043d\u0430\u043b\u0456\u0437\u0456 \u0434\u043b\u044f \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0456\u0432 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u044c \u043d\u0430 \u043e\u0434\u043d\u0456\u0439 \u0434\u0456\u043b\u044f\u043d\u0446\u0456.<\/span> <\/p>\n

\"\"<\/p>\n

\u0447\u0430\u0439<\/span><\/p>\n

\u0426\u0435\u0439 \u0442\u0438\u043f \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0438 \u0437\u0430\u0431\u0435\u0437\u043f\u0435\u0447\u0443\u0454 \u0437\u0440\u0443\u0447\u043d\u0438\u0439 \u0441\u043f\u043e\u0441\u0456\u0431 \u043f\u0435\u0440\u0435\u0433\u043b\u044f\u0434\u0443 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0456\u0432 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u044c \u043e\u0434\u043d\u043e\u0447\u0430\u0441\u043d\u043e.<\/span><\/p>\n

\u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u043b\u044f\u043d\u043a\u0443 \u043b\u0456\u0441\u0443 \u0432 R.<\/span><\/p>\n

\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043b\u0456\u0441\u043e\u0432\u0430 \u0434\u0456\u043b\u044f\u043d\u043a\u0430 \u0432 \u0440<\/strong><\/span><\/h3>\n

\u0429\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043b\u0456\u0441\u043e\u0432\u0443 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u0432 R, \u043d\u0430\u043c \u0441\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u0438\u0445, \u0449\u043e \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0440\u043e\u0437\u043c\u0456\u0440 \u0435\u0444\u0435\u043a\u0442\u0443 (\u0430\u0431\u043e \u0456\u043d\u0448\u0435 \u0446\u0456\u043a\u0430\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f) \u0456 \u0432\u0435\u0440\u0445\u043d\u0456\u0439 \u0456 \u043d\u0438\u0436\u043d\u0456\u0439 \u0434\u043e\u0432\u0456\u0440\u0447\u0456 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0438 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044f:<\/span><\/p>\n

 #create data<\/span>\ndf <- data. frame<\/span> (study=c('S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7'),\n                 index=1:7,\n                 effect=c(-.4, -.25, -.1, .1, .15, .2, .3),\n                 lower=c(-.43, -.29, -.17, -.02, .04, .17, .27),\n                 upper=c(-.37, -.21, -.03, .22, .24, .23, .33))\n\n#view data<\/span>\nhead(df)\n\n  study index effect lower upper\n1 S1 1 -0.40 -0.43 -0.37\n2 S2 2 -0.25 -0.29 -0.21\n3 S3 3 -0.10 -0.17 -0.03\n4 S4 4 0.10 -0.02 0.22\n5 S5 5 0.15 0.04 0.24\n6 S6 6 0.20 0.17 0.23\n7 S7 7 0.30 0.27 0.33<\/strong><\/pre>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u043f\u0430\u043a\u0435\u0442\u0430 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0434\u0430\u043d\u0438\u0445 ggplot2, \u0449\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u043b\u0456\u0441\u043e\u0432\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a:<\/span> <\/p>\n
 #load ggplot2\n<\/span>library<\/span> (ggplot2)\n\n#create forest plot<\/span>\nggplot(data=df, aes<\/span> (y=index, x=effect, xmin=lower, xmax=upper)) +\n  geom_point() + \n  geom_errorbarh(height= .1<\/span> ) +\n  scale_y_continuous(name = \"\", breaks=1: nrow<\/span> (df), labels=df$study)<\/strong> <\/pre>\n
\"\"<\/p>\n
 \u041d\u0430 \u043e\u0441\u0456 X \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454\u0442\u044c\u0441\u044f \u0440\u043e\u0437\u043c\u0456\u0440 \u0435\u0444\u0435\u043a\u0442\u0443 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044f, \u0430 \u043d\u0430 \u043e\u0441\u0456 Y \u2013 \u043d\u0430\u0437\u0432\u0430 \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u0422\u043e\u0447\u043a\u0438 \u043d\u0430 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u044e\u0442\u044c \u0440\u043e\u0437\u043c\u0456\u0440 \u0435\u0444\u0435\u043a\u0442\u0443 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044f, \u0430 \u0441\u043c\u0443\u0433\u0438 \u043f\u043e\u043c\u0438\u043b\u043e\u043a \u043f\u043e\u043a\u0430\u0437\u0443\u044e\u0442\u044c \u043c\u0435\u0436\u0456 \u0434\u043e\u0432\u0456\u0440\u0447\u043e\u0433\u043e \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0443.<\/span><\/p>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0434\u043e\u0434\u0430\u0442\u0438 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043e\u043a, \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u043c\u0456\u0442\u043a\u0438 \u043e\u0441\u0456 \u0442\u0430 \u0434\u043e\u0434\u0430\u0442\u0438 \u0432\u0435\u0440\u0442\u0438\u043a\u0430\u043b\u044c\u043d\u0443 \u043b\u0456\u043d\u0456\u044e \u0437 \u043d\u0443\u043b\u044c\u043e\u0432\u0438\u043c \u0440\u043e\u0437\u043c\u0456\u0440\u043e\u043c \u0435\u0444\u0435\u043a\u0442\u0443, \u0449\u043e\u0431 \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u043a\u0440\u0430\u0449\u043e\u044e:<\/span> <\/p>\n
 #load ggplot2\n<\/span>library<\/span> (ggplot2)\n\n#create forest plot<\/span>\nggplot(data=df, aes<\/span> (y=index, x=effect, xmin=lower, xmax=upper)) +\n  geom_point() + \n  geom_errorbarh(height= .1<\/span> ) +\n  scale_y_continuous(breaks=1: nrow<\/span> (df), labels=df$study) +\n  labs(title=' Effect Size by Study<\/span> ', x=' Effect Size<\/span> ', y = ' Study<\/span> ') +\n  geom_vline(xintercept=0, color=' black<\/span> ', linetype=' dashed<\/span> ', alpha= .5<\/span> ) +\n  theme_minimal()<\/strong> <\/pre>\n
\"\u041b\u0456\u0441\u043e\u0432\u0430<\/p>\n
 \u0421\u043c\u0456\u043b\u0438\u0432\u043e \u0437\u043c\u0456\u043d\u044e\u0439\u0442\u0435 \u0442\u0435\u043c\u0443 \u0441\u044e\u0436\u0435\u0442\u0443, \u0449\u043e\u0431 \u0432\u0456\u043d \u0432\u0438\u0433\u043b\u044f\u0434\u0430\u0432 \u0442\u0430\u043a, \u044f\u043a \u0432\u0430\u043c \u0445\u043e\u0447\u0435\u0442\u044c\u0441\u044f. \u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u043c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 theme_classic()<\/strong> \u0434\u043b\u044f \u0449\u0435 \u0431\u0456\u043b\u044c\u0448 \u043a\u043b\u0430\u0441\u0438\u0447\u043d\u043e\u0433\u043e \u0432\u0438\u0433\u043b\u044f\u0434\u0443:<\/span> <\/p>\n
 #load ggplot2\n<\/span>library<\/span> (ggplot2)\n\n#create forest plot<\/span>\nggplot(data=df, aes<\/span> (y=index, x=effect, xmin=lower, xmax=upper)) +\n  geom_point() + \n  geom_errorbarh(height= .1<\/span> ) +\n  scale_y_continuous(breaks=1: nrow<\/span> (df), labels=df$study) +\n  labs(title=' Effect Size by Study<\/span> ', x=' Effect Size<\/span> ', y = ' Study<\/span> ') +\n  geom_vline(xintercept=0, color=' black<\/span> ', linetype=' dashed<\/span> ', alpha= .5<\/span> ) +\n  theme_classic()<\/strong> <\/pre>\n
\"\"<\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n
 \u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u043b\u044f\u043d\u043a\u0443 \u043b\u0456\u0441\u0443 \u0432 Excel<\/a>
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\u041b\u0456\u0441\u043e\u0432\u0430 \u0434\u0456\u043b\u044f\u043d\u043a\u0430 (\u0456\u043d\u043e\u0434\u0456 \u0457\u0457 \u043d\u0430\u0437\u0438\u0432\u0430\u044e\u0442\u044c \u00ab\u0431\u043b\u043e\u0431\u0431\u043e\u0433\u0440\u0430\u043c\u043e\u044e\u00bb) \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0432 \u043c\u0435\u0442\u0430-\u0430\u043d\u0430\u043b\u0456\u0437\u0456 \u0434\u043b\u044f \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0456\u0432 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u044c \u043d\u0430 \u043e\u0434\u043d\u0456\u0439 \u0434\u0456\u043b\u044f\u043d\u0446\u0456. \u0447\u0430\u0439 \u0426\u0435\u0439 \u0442\u0438\u043f \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0438 \u0437\u0430\u0431\u0435\u0437\u043f\u0435\u0447\u0443\u0454 \u0437\u0440\u0443\u0447\u043d\u0438\u0439 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