This can be particularly important when you are generating scale variables, or variables that will be combining values of multiple variables. You can also use egen to generate a variable to examine missing data on each row. That is, its value is constant within clusters but varies across clusters. This creates a variable that is equal to the cluster mean for each cluster.
#Stata egen code#
If the id variable isn’t sorted, then you can change the code to: by id, sort: egen groupweight=mean(weight) (Remember that id is the cluster id variable.) by id: egen groupweight=mean(weight) We can combine egen mean with by to compute cluster means. This creates a new variable that is equal to the grand mean for weight (it is a constant across all variables) list in 1/15, clean
We can create a grand mean (say, for creating centered variables) of weight using egen.
#Stata egen download#
We can download the the pig data from the mixed help. To create a mean with egen we use the following syntax: egen nameofnewvariable=mean(nameofoldvariable) The egen function is used to create new variables. Combining egen mean with by processing in Stata makes this a breeze, even when cluster sizes differ. The egen mean function makes creating means easy. Consequently, I often need to create cluster-level means and grand means for graphing and modeling. View the entire collection of UVA Library StatLab articles.I work a lot with clustered data, including group psychotherapy data (people clustered in groups), individual psychotherapy data (people clustered within therapists), and longitudinal data (observations clustered within people). Pop_c float %9.0g popcl Categorized population Marriage long %12.0gc Number of marriages > label data "1980 Census data by state: v2" That is, if fcn is not one of the functions above, gegen outvar fcn (varlist) if in, by (byvars) would be the same as. * Now the three categories are presented as low, medium and high Last, when gegen calls a function that is not implemented internally by gtools, it will hash the by variables and call egen with by set to an id based on the hash.
* Then we attach the value label popcl to the variable pop_c > label define popcl 1 "low" 2 "medium" 3 "high" Let’s label them as low, medium and high. * Remember we categorized pop_c into three categories: 1,2 and 3
Poplt5 long %12.0gc Pop, label variable pop0_17 "Pop, label variable pop_c "Categorized population" Here we create another new variable called pop_c2 then do the recode in the same manner as we did for pop_c. We can use the -recode- command to recode variables as well. Then we create a new variable called pop_c and transform the original variable pop into three categories. Here we create the youth population variable again, but this time we make it into thousands and replace the one we just created. replace-: replace contents of existing variables > order state state2 region pop poplt5 pop0_17 * Summary statistics for the three variables Poplt5 long %12.0gc Pop, generate pop0_17 = poplt5 + pop5_17 State2 str2 %-2s Two-letter state abbreviation Variable name type format label variable label Contains data from /Applications/Stata/ado/base/c/census.dta
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