例如,假设你有一个字符串变量“string_var”,其中包含“male”和“female”两个值,你可以使用以下命令将其转换为数值变量: ```stata egen numeric_var = group(string_var) ``` 这将创建一个新的名为“numeric_var”的变量,并将“string_var”的值根据分类进行编码。 以上是一些常见的处理字符串变量的方法,...
5.滞后命令后time variable not setr(111); . xtset id 会计期间 string variables not allowed in varlist; id is a string variable r(109); 变量属性 egen iid=group(id) egen year=group( 会计期间 ) xtset iid year 时间变量中有重复 duplicates drop id year, force 6.差分gen DLX=DL.x前先sort...
Stata一共有两种变量numeric variables、string variablesnumeric variables：指数字变量，这种变量所有的observ...
3、a create or change variables生成变量的窗口data create or change variables create new variable基本命令stata有四个基本的生成和修改变量的命令:gen、egen、replace和recode . gen和egen分别是generate和extended generate的缩写,它们用于生成新变量 . replace和recode用来改变现存变量的属性或数值 . replace需要与ge...
Warning: If you have more than 67,784 unique values of the string variables that you are encoding, encode will complain. If that is the case, then you can use . egen nb = group(b) which will generate a numeric variable nb that does not have value labels. ...
How would this be extended to identifying groups that differ on at least one of two or more variables? One way would be to useegen. For example,egen, group()could be used to group values according to one or more variables, and then the same method could be used on the resulting variab...
S458761 MAP: Stata module to map string variables using an external dictionary file byDaniel Alves Fernandes S458760 GHSURV: Stata module for the estimation of survival model using repeated cross-sectional data byFernando Rios-Avila S458759 IVREG_SS: Stata module to compute confidence intervals, st...
: Convert string variables to numeric variables and vice versa. : Encode string into numeric and vice versa. encode 主要用于将数据进行编码、分组,将连续性变量转换成分组变量。 它可以和另外一组命令一起记忆: encode , decode , recode 和 autocode() ...
(p20)'" if corr == 1 local ++col } * Grouped correlation of variables generate group_corr = corrname1 + " - " + corrname2 compress * Sort the plot sort corr, stable generate rank_corr = _n labmask rank_corr, values(group_corr) * Plotting * Run the commands ahead in one go ...
groupID(statefip) /// subexpVar(sub_exp) * Summarize by sub_exp:sum stack_weight if treat == 0 & event_time == 0 得到权重后,我们可以利用堆叠数据集来跑堆叠回归,得到总的ACA处理效应: // Estimate the stacked regression // Create dummy variables for event-time ...