如何拟合SAS中使用不同参数的线性回归模型

Question

我有一个包含三个独立变量的线性模型。我有下面的最后一个模型。我想使用increase_po和decrease_po变量重新估计回归模型中的y值。

Dependent Variable Estimate increase_po decrease_po 
Rate  Rate_lag1 0.54  0.60   0.49 
Rate  UN   0.07  0.08   0.06 
Rate  SQ   0.03  0.03   0.02 

我想要做的就是写一个DO循环生产的六个可能的组合：

comb1 comb2 comb3 comb4 comb5 comb6 
0.60 0.49 0.08 0.06 0.03 0.02 
0.07 0.07 0.54 0.54 0.54 0.54 
0.03 0.03 0.03 0.03 0.07 0.07 

我想用这些参数来重新拟合模型，并得到估计ÿ值。

fitted y1= b0 + 0.60*Rate_lag1 + 0.07*UN + 0.03*SQ (only Rate_lag1 change parameter) 

fitted y2= b0 + 0.49*Rate_lag1 + 0.07*UN + 0.03*SQ (only Rate_lag1 change parameter) 

fitted y3= b0 + 0.54*Rate_lag1 + 0.08*UN + 0.03*SQ (only UN change parameter) 

....................

因此，如果不使用宏循环是很难甚至是不可能的。

Answer 1

我看到你的其他线程，但想在这里发布。

如果我理解正确： 您已经为您的数据拟合了3个模型，并且每个模型使用相同的三个独立变量/预测变量。 您已经显示了与3个模型的每个预测变量相对应的beta参数。 您想从3个原始模型开始创建6个新模型，并且一次只更改一个β参数。

下面是我认为会做你想做的一些SAS代码。

但是，您的3个原始模型中的测试版估计都非常相似！所以我不知道这个练习揭示了什么是“最佳”模型。

祝你好运！

data have; 
    infile cards; 
    input Dependent $ Variable $ Estimate increase_po decrease_po; 
    cards; 
Rate  Rate_lag1 0.54  0.60   0.49 
Rate  UN   0.07  0.08   0.06 
Rate  SQ   0.03  0.03   0.02 
; 
run; 

*** TRANSPOSE SO EACH PREDICTOR VARIABLE IS A COLUMN AND EACH MODEL IS A ROW ***; 
*** NOTE: RATE_LAG1 CHANGES TO RATE_LAG IN THE TRANSPOSE ***; 
proc transpose data=have out=have_transpose; 
    id variable; 
    var Estimate increase_po decrease_po; 
run; 

*** CREATE VARIABLE FOR MODEL NUMBERS 1-3 ***; 
data have_transpose; 
    set have_transpose; 
    modelnum=_N_; 
run;  

proc print data=have_transpose; 
run; 

*** PUT EACH COLUMN INTO A SEPARATE DATASET AND KEEP ORIGINAL MODEL NUMBER IN EACH DATASET ***; 
data col1(keep=rate_lag modelnum rename=(modelnum=rate_lag_num)) 
     col2(keep=un modelnum rename=(modelnum=un_num)) 
     col3(keep=sq modelnum rename=(modelnum=sq_num)) 
    ; 
    set have_transpose; 
run; 

*** USE SQL TO DO A MANY-TO-MANY MERGE FOR ALL THREE DATASETS ***; 
*** THE CREATED DATASET WILL CONTAIN ALL POSSIBLE COMBINATIONS OF PARAMETER ESTIMATES FROM ALL MODELS ***; 
*** IN THIS CASE THERE WILL BE 3x3x3 = 27 RECORDS ***; 
proc sql; 
    create table col123 as 
    select * 
    from col1, col2, col3 
; 
quit; 

data almost; 
    set col123; 
    *** FOR EACH POSSIBLE COMBINATION, COUNT HOW MANY PARAMETERS ARE UNCHANGED FROM MODEL 1 ***; 
    flag = (rate_lag_num=1) + (un_num=1) + (sq_num=1); 
run; 

proc print data=almost; 
run; 

*** WANT TO ONLY KEEP MODELS WHERE TWO PARAMETERS ARE UNCHANGED ESTIMATES (WHERE FLAG=2) ***; 
data want; 
    set almost; 
    if flag=2; 
    keep rate_lag un sq ; 
run; 

*** THIS DATASET CONTAINS 6 RECORDS ***; 
proc print data=want; 
run; 


*** FROM HERE YOU CAN USE SQL TO DO A MANY-TO-MANY MERGE WITH YOUR 6 NEW MODELS AND DATASET WITH THE PREDICTOR VARIABLES ***; 
*** AND THEN CREATE YOUR NEW ESTIMATES FOR EACH MODEL ***; 
*** SOMETHING LIKE THIS BELOW ***; 
/* 

*** RENAME VARIABLES AND CREATE NEW MODEL NUMBER ***; 
data betas; 
    set want; 
    new_model_num = _N_; 
    rename rate_lag=rate_lag_beta un=un_beta sq=sq_beta ; 
run; 

proc sql; 
    create table all as 
    select * 
    from betas, ORIGINAL_DATA; *** CHANGE "ORIGINAL_DATA" TO YOUR DATA SET NAME ***; 
run; 

data new_model; 
    set all; 
    fitted = b0 + (Rate_lag_beta * Rate_lag1) + (un_beta * un) + (sq_beta * sq); 
run; 
*/ 

*** NOTE: IN YOUR EXAMPLE, YOU ASSUME THE SAME B0 FOR ALL MODELS 
*** HOWEVER, YOUR THREE STARTER MODELS MAY HAVE DIFFERENT B0 ESTIMATES ***; 
*** SO YOU WILL HAVE TO THINK ABOUT THE BEST WAY TO HANDLE THAT ***;