我没怎么懂您说的“好比你用edu1-edu7及health1-health5做虚拟变量回归，那你只要在回归时不加”加入我生成了state1-state8,原来是用state1做基准组，在分析多元回归的时候，xi.i state 去掉了state1，但是我需要用state1来说明对被解释变量的影响，请问如何做呢？
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 five factor-variable operators:
Operator Description
----------------------------------------------------------------------------------
i. unary operator to specify indicators
c. unary operator to treat as continuous
o. unary operator to omit a variable or indicator
# 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
Video examples
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.
Thus, 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.
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