{"id":13964,"date":"2018-04-20T04:45:21","date_gmt":"2018-04-20T04:45:21","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=13964"},"modified":"2021-12-03T10:35:27","modified_gmt":"2021-12-03T05:05:27","slug":"sas-mixed-model","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/","title":{"rendered":"SAS Mixed Model Procedures – PROC MIXED, PROC NLMIXED"},"content":{"rendered":"

In our previous article we have seen\u00a0Longitudinal Data Analysis Procedures<\/a><\/strong>, today we will discuss what is SAS mixed model. Moreover, we are going to explore procedures used in Mixed modeling in SAS\/STAT.<\/p>\n

In these SAS Mixed Model, we will focus on 6 different types of procedures:\u00a0PROC MIXED, PROC NLMIXED, PROC PHREG, PROC GLIMMIX, PROC VARCOMP, and ROC HPMIXED with examples & syntax. At last, we also learn SAS\u00a0mixed models with examples.
\nSo, let’s start with SAS mixed model.<\/p>\n

\"SAS<\/a>

SAS Mixed Model Procedures – PROC MIXED, PROC NLMIXED<\/p><\/div>\n

Introduction to SAS Mixed Model<\/h3>\n

The term\u00a0mixed model<\/em>\u00a0in SAS\/STAT refers to the use of both fixed and random effects in the same analysis. SAS mixed model are particularly useful in settings where\u00a0repeated measurements are made on the same\u00a0statistical units, or where measurements are made on clusters of related statistical units.<\/p>\n

Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures\u00a0ANOVA<\/strong><\/a>.<\/p>\n

In such SAS mixed modeling, the observations are not assumed to be independent. Random effects are fitted to the model accounting for additional sources of variation. A general linear mixed model looks like the one shown below.
\nAlso, Learn 6 Bayesian Analysis Procedures\u00a0<\/a><\/strong><\/p>\n

\"SAS\/STAT<\/a>

Sample – SAS Mixed Modeling<\/p><\/div>\n

Mixed Modelling Procedure in SAS\/STAT<\/h3>\n

SAS\/STAT uses the following 6 simple procedures to compute mixed models of a sample data. Each procedure has a different syntax and is used with different type of data in different contexts. Let us explore each one of these.<\/p>\n

a. PROC HPMIXED<\/h4>\n

The HPMIXED procedure uses a number of techniques to fit linear mixed models. PROC HPMIXED procedure enables you to specify a linear mixed model with variance component structure, to estimate the covariance parameters by restricted maximum likelihood.<\/p>\n

The HPMIXED procedure is similar to the PROC MIXED procedure and other SAS procedures for mixed modeling. SAS mixed model\u00a0supported by the HPMIXED procedure are a subset of the models that you can fit with the MIXED procedure.
\nLet’s learn 16 Important Features of\u00a0SAS\/STAT \u00a0<\/a><\/strong>
\nThe syntax of\u00a0PROC HPMIXED<\/strong>–<\/strong><\/p>\n

PROC HPMIXED\u00a0DATASET\r\nCLASS <variable>;\r\nMODEL response= effects <options>;<\/pre>\n
The PROC HPMIXED, MODEL statements are required
\nPROC HPMIXED Example-<\/strong><\/p>\n
proc hpmixed data=Sashelp.iris;\r\n\u00a0\u00a0 class Species;\r\n\u00a0\u00a0 model sepallength = Sepalwidth;\r\n\u00a0 run;<\/pre>\n
\u00a0<\/strong><\/p>\n
\"SAS<\/a>
SAS mixed model –\u00a0\u00a0PROC HPMIXED<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS mixed modeling –\u00a0PROC HPMIXED<\/p><\/div>\n
Read About Categorical Data Analysis Procedure <\/a><\/strong><\/p>\n
b. PROC NLMIXED<\/h4>\n
The PROC NLMIXED fits nonlinear mixed models\u2014that is, models in which both fixed and random effects enter nonlinearly. PROC NLMIXED enables you to specify a conditional distribution for your data (given the random effects) having either a standard form (normal, binomial, Poisson) or a general distribution.<\/p>\n
SAS PROC NLMIXED fits nonlinear mixed models by maximizing an approximation to the likelihood integrated over the random effects.
\nPROC\u00a0NLMIXED<\/strong>\u00a0Syntax-<\/strong><\/p>\n
PROC NLMIXED dataset <<\/pre>\n
OPTIONS>; MODEL model-specification; PARMS\u00a0parameters-and-starting-values;
\n\u00a0PROC<\/strong>\u00a0NLMIXED\u00a0Example-<\/strong><\/p>\n
data CARS;\r\n\u00a0 input NUMBER LENGTH X;\r\n\u00a0 datalines;\r\n1\u00a0 118\u00a0\u00a0 30\r\n1\u00a0 484\u00a0\u00a0 58\r\n1\u00a0 664\u00a0\u00a0 87\r\n2\u00a0 118\u00a0\u00a0 33\r\n2\u00a0 484\u00a0\u00a0 69\r\n2\u00a0 664\u00a0  111\r\n3\u00a0 118\u00a0\u00a0 30\r\n3\u00a0 484\u00a0\u00a0 51\r\n3\u00a0 664\u00a0\u00a0 75\r\n4\u00a0 118\u00a0\u00a0 32\r\n4\u00a0 484\u00a0\u00a0 62\r\n4\u00a0 664\u00a0  112\r\n4  1004\u00a0 167\r\n5\u00a0 118\u00a0\u00a0 30\r\n5\u00a0 484\u00a0\u00a0 49\r\n5\u00a0 664\u00a0\u00a0 81\r\n5 1004\u00a0  125\r\n;\r\nproc nlmixed data=CARS;\r\n\u00a0\u00a0 parms b1=220 b2=500 b3=310 s2u=100 s2e=60;\r\n\u00a0\u00a0 num = b1+u1;\r\n\u00a0\u00a0 ex\u00a0 = exp(-(day-b2)\/b3);\r\n\u00a0\u00a0 den = 1 + ex;\r\n\u00a0\u00a0 model X ~ normal(num\/den,s2e);\r\n\u00a0\u00a0 random u1 ~ normal(0,s2u) subject=NUMBER;\r\nrun;<\/pre>\n
In the above example, a nonlinear SAS mixed model has been created with different equations.
\nThe\u00a0PROC NLMIXED\u00a0statement invokes the procedure and inputs the data set. The\u00a0PARMS statement identifies the unknown parameters and their starting values. Here there are three fixed-effects parameters (b1,\u00a0b2,\u00a0b3) and two variance components (s2u,\u00a0s2e).
\nLet’s Explore Advantages & Disadvantages of\u00a0SAS\/STAT\u00a0<\/a><\/strong>
\nThe MIXED MODEL statement defines the dependent variable and its conditional distribution given the random effects. Here a normal (Gaussian) conditional distribution is specified.<\/p>\n
The\u00a0RANDOM statement defines the single random effect to be\u00a0u1, and specifies that it follow a normal distribution with mean 0 and variance\u00a0s2u. The\u00a0SUBJECT=\u00a0argument in the\u00a0RANDOM\u00a0statement defines a variable indicating when the random effect obtains new realizations.\u00a0<\/strong><\/p>\n
\"SAS\/STAT<\/a>
SAS mixed model –\u00a0PROC NLMIXED<\/p><\/div>\n
\"Statistical<\/a>
Statistical Mixed Modeling –\u00a0PROC NLMIXED<\/p><\/div>\n
\"Mixed<\/a>
Mixed Models in SAS\/STAT –\u00a0PROC NLMIXED<\/p><\/div>\n
Read About Group Sequential Design and Analysis\u00a0<\/a><\/strong><\/p>\n
c. PROC VARCOMP<\/h4>\n
The VARCOMP procedure in SAS\/STAT fits general linear models that have random effects. PROC VARCOMP estimates the contribution of each of the random effects to the variance of the dependent variable. You can specify four general methods of estimation\u00a0in the PROC VARCOMP statement by using the\u00a0METHOD=option. Let us see how it works-
\nPROC VARCOMP\u00a0Syntax-<\/strong><\/p>\n
PROC VARCOMP dataset OPTIONS;\r\nCLASS <VARIABLES>;\r\n\u00a0\u00a0\u00a0\u00a0 MODEL dependent\u00a0=\u00a0<fixed-effects> <\/ options>;<\/pre>\n
The\u00a0PROC MIXED and\u00a0MODEL statements are required, and the\u00a0MODEL\u00a0statement must appear after the\u00a0CLASS statement if a\u00a0CLASS statement is included.
\nPROC VARCOMP<\/strong>\u00a0Example-<\/strong><\/p>\n
proc varcomp method=ml data=SASHELP.CARS;\r\n\u00a0\u00a0 class MAKE\u00a0 ORIGIN;\r\n\u00a0\u00a0 model MPG_HIGHWAY=ORIGIN;\r\nrun;<\/pre>\n
The MIXED MODEL statement first specifies the response (dependent) variable MPG_highway. The explanatory (independent) variables are then listed after the equal (=) sign.<\/p>\n
\"SAS\/STAT<\/a>
SAS mixed model –\u00a0PROC VARCOMP<\/p><\/div>\n
d. PROC GLIMMIX<\/h4>\n
We have already discussed this procedure in detail in the Longitudinal data analysis<\/a> <\/strong>tutorial. You can refer to the same for the complete tutorial.<\/p>\n
e. PROC MIXED<\/h4>\n
We have already discussed\u00a0this procedure in detail in the Longitudinal data analysis<\/a> <\/strong>tutorial with PROC MIXED example.<\/p>\n
f. PROC PHREG<\/h4>\n
We have already discussed this procedure in detail. You can refer to the following link for the complete tutorial<\/a><\/strong>.
\nThis was all\u00a0about SAS Mixed Model Tutorial. Hope you like our explanation<\/b>.<\/p>\n
Conclusion<\/h3>\n
Hence, we have discussed the complete description of SAS mixed model. Along with this we also learned the 6 different procedures for mixed modeling in SAS\/STAT. Moreover, we looked at the syntax and examples of PROC MIXED, PROC NLMIXED, PROC PHREG, PROC GLIMMIX, PROC VARCOMP, and PROC HPMIXED and how they can be used.<\/p>\n
Hope you all enjoyed it. Stay tuned for more interesting topics in SAS\/STAT, and for any doubts in SAS mixed model, post it in the comments section below.<\/p>\n
For reference\u00a0<\/a><\/strong><\/p>\n
In our previous article we have seen\u00a0Longitudinal Data Analysis Procedures, today we will discuss what is SAS mixed model. Moreover, we are going to explore procedures used in Mixed modeling in SAS\/STAT. In these...<\/p>\n","protected":false},"author":6,"featured_media":14097,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[10026,10036,10037,10055,10056,10062,10063,10069,10070,10133,12271,12349,13802,13803,14065,14067,14068,14070,14072,14082],"class_list":["post-13964","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-proc-glimmix-example","tag-proc-hpmixed","tag-proc-hpmixed-example","tag-proc-mixed","tag-proc-mixed-example","tag-proc-nlmixed","tag-proc-nlmixed-example","tag-proc-phreg","tag-proc-phreg-example","tag-proc-varcomp","tag-sas-stat-tutorial","tag-sasstat-mixed-modeling","tag-statistical-mixed-model","tag-statistical-mixed-modeling","tag-syntax-of-proc-glimmix","tag-syntax-of-proc-hpmixed","tag-syntax-of-proc-mixed","tag-syntax-of-proc-nlmixed","tag-syntax-of-proc-phreg","tag-syntax-of-proc-varcomp"],"yoast_head":"\nSAS Mixed Model Procedures - PROC MIXED, PROC NLMIXED - DataFlair<\/title>\n<meta name=\"description\" content=\"What is SAS mixed model,Mixed models Procedure: SAS\/STAT,PROC VARCOMP,PROC HPMIXED, PROC NLMIXED,PROC GLIMMIX,PROC PHREG, PROC MIXED with examples & Syntax\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SAS Mixed Model Procedures - PROC MIXED, PROC NLMIXED - DataFlair\" \/>\n<meta property=\"og:description\" content=\"What is SAS mixed model,Mixed models Procedure: SAS\/STAT,PROC VARCOMP,PROC HPMIXED, PROC NLMIXED,PROC GLIMMIX,PROC PHREG, PROC MIXED with examples & Syntax\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-04-20T04:45:21+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-12-03T05:05:27+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-Mixed-Models-01.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"SAS Mixed Model Procedures - PROC MIXED, PROC NLMIXED - DataFlair","description":"What is SAS mixed model,Mixed models Procedure: SAS\/STAT,PROC VARCOMP,PROC HPMIXED, PROC NLMIXED,PROC GLIMMIX,PROC PHREG, PROC MIXED with examples & Syntax","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/","og_locale":"en_US","og_type":"article","og_title":"SAS Mixed Model Procedures - PROC MIXED, PROC NLMIXED - DataFlair","og_description":"What is SAS mixed model,Mixed models Procedure: SAS\/STAT,PROC VARCOMP,PROC HPMIXED, PROC NLMIXED,PROC GLIMMIX,PROC PHREG, PROC MIXED with examples & Syntax","og_url":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-04-20T04:45:21+00:00","article_modified_time":"2021-12-03T05:05:27+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-Mixed-Models-01.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89"},"headline":"SAS Mixed Model Procedures – PROC MIXED, PROC NLMIXED","datePublished":"2018-04-20T04:45:21+00:00","dateModified":"2021-12-03T05:05:27+00:00","mainEntityOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/"},"wordCount":974,"commentCount":1,"publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-Mixed-Models-01.jpg","keywords":["PROC GLIMMIX Example","PROC HPMIXED","PROC HPMIXED Example","PROC MIXED","PROC MIXED Example","PROC NLMIXED","PROC NLMIXED Example","PROC PHREG","PROC PHREG Example","PROC VARCOMP","sas stat tutorial","SAS\/STAT Mixed Modeling","Statistical mixed Model","Statistical Mixed Modeling","Syntax of PROC GLIMMIX","Syntax of PROC HPMIXED","Syntax of PROC MIXED","Syntax of PROC NLMIXED","Syntax of PROC PHREG","Syntax of PROC VARCOMP"],"articleSection":["SAS - STAT Tutorials"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/","url":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/","name":"SAS Mixed Model Procedures - PROC MIXED, PROC NLMIXED - DataFlair","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/#website"},"primaryImageOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#primaryimage"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-Mixed-Models-01.jpg","datePublished":"2018-04-20T04:45:21+00:00","dateModified":"2021-12-03T05:05:27+00:00","description":"What is SAS mixed model,Mixed models Procedure: SAS\/STAT,PROC VARCOMP,PROC HPMIXED, PROC NLMIXED,PROC GLIMMIX,PROC PHREG, PROC MIXED with examples & Syntax","breadcrumb":{"@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/data-flair.training\/blogs\/sas-mixed-model\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#primaryimage","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-Mixed-Models-01.jpg","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-Mixed-Models-01.jpg","width":1200,"height":628,"caption":"SAS Mixed Model"},{"@type":"BreadcrumbList","@id":"https:\/\/data-flair.training\/blogs\/sas-mixed-model\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Blog Home","item":"https:\/\/data-flair.training\/blogs\/"},{"@type":"ListItem","position":2,"name":"Important features of java","item":"https:\/\/data-flair.training\/blogs\/tag\/important-features-of-java\/"},{"@type":"ListItem","position":3,"name":"SAS Mixed Model Procedures – PROC MIXED, PROC NLMIXED"}]},{"@type":"WebSite","@id":"https:\/\/data-flair.training\/blogs\/#website","url":"https:\/\/data-flair.training\/blogs\/","name":"DataFlair","description":"Learn Today. Lead Tomorrow.","publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/data-flair.training\/blogs\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/data-flair.training\/blogs\/#organization","name":"DataFlair","url":"https:\/\/data-flair.training\/blogs\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","width":106,"height":48,"caption":"DataFlair"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataFlairWS\/","https:\/\/x.com\/DataFlairWS","https:\/\/www.linkedin.com\/company\/dataflair-web-services-pvt-ltd\/","https:\/\/www.youtube.com\/user\/DataFlairWS"]},{"@type":"Person","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89","name":"DataFlair Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","caption":"DataFlair Team"},"description":"The DataFlair Team provides industry-driven content on programming, Java, Python, C++, DSA, AI, ML, data Science, Android, Flutter, MERN, Web Development, and technology. Our expert educators focus on delivering value-packed, easy-to-follow resources for tech enthusiasts and professionals.","url":"https:\/\/data-flair.training\/blogs\/author\/dfteam2\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/13964","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/comments?post=13964"}],"version-history":[{"count":5,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/13964\/revisions"}],"predecessor-version":[{"id":104676,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/13964\/revisions\/104676"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media\/14097"}],"wp:attachment":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media?parent=13964"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/categories?post=13964"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/tags?post=13964"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}