# 如何一次定义与多个虚拟变量的交互项_Stata12做回归如何建立交互项

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## Stata12做回归如何建立交互项

 建立新变量 gen z=x*y
 Title     [U] 11.4.3 Factor variables Description     Factor variables are extensions of varlists of existing variables.  When a command allows factor variables, in     addition to typing variable names from your data, you can type factor variables, which might look like         i.varname         i.varname#i.varname         i.varname#i.varname#i.varname         i.varname##i.varname         i.varname##i.varname##i.varname     Factor variables create indicator variables from categorical variables, interactions of indicators of categorical     variables, interactions of categorical and continuous variables, and interactions of continuous variables     (polynomials).  They are allowed with most estimation and postestimation commands, along with a few other     commands.     There are four factor-variable operators:          Operator  Description          ————————————————————————————————————-          i.        unary operator to specify indicators          c.        unary operator to treat as continuous          #         binary operator to specify interactions          ##        binary operator to specify factorial interactions          ————————————————————————————————————-     The indicators and interactions created by factor-variable operators are referred to as virtual variables.  They     act like variables in varlists but do not exist in the dataset.     Categorical variables to which factor-variable operators are applied must contain nonnegative integers with values     in the range 0 to 32,740, inclusive.     Factor variables may be combined with the L. and F. time-series operators. Remarks     Remarks are presented under the following headings:         Basic examples         Base levels         Selecting levels         Applying operators to a group of variables Basic examples     Here are some examples of use of the operators:          Factor                      specification     Result          ————————————————————————————————————-          i.group           indicators for levels of group          i.group#i.sex     indicators for each combination of levels of group and sex, a two-way interaction          group#sex         same as i.group#i.sex          group#sex#arm     indicators for each combination of levels of group, sex, and arm, a three-way interaction          group##sex        same as i.group i.sex group#sex          group##sex##arm   same as i.group i.sex i.arm group#sex group#arm sex#arm group#sex#arm          sex#c.age         two variables — age for males and 0 elsewhere, and age for females and 0 elsewhere; if age                              is also in the model, one of the two virtual variables will be treated as a base          sex##c.age        same as i.sex age sex#c.age          c.age             same as age          c.age#c.age       age squared          c.age#c.age#c.age age cubed          ————————————————————————————————————- Base levels     You can specify the base level of a factor variable by using the ib. operator.  The syntax is            Base                     operator(*)    Description            ———————————————————————————————————–            ib#.           use # as base, #=value of variable            ib(##).        use the #th ordered value as base (**)            ib(first).     use smallest value as base (the default)            ib(last).      use largest value as base            ib(freq).      use most frequent value as base            ibn.           no base level            ———————————————————————————————————–             (*) The i may be omitted.  For instance, you may type ib2.group or b2.group.            (**) For example, ib(#2). means to use the second value as the base.     If you want to use group==3 as the base in a regression, you can type,         . regress y  i.sex ib3.group     You can also permanently set the base levels of categorical variables by using the fvset command.

anova wage children married children#married c.education

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