如何擬合SAS中使用不同參數的線性迴歸模型

Question

我有一個包含三個獨立變量的線性模型。我有下面的最後一個模型。我想使用increase_po和decrease_po變量重新估計迴歸模型中的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 

我想要做的就是寫一個DO循環生產的六個可能的組合：

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 

我想用這些參數來重新擬合模型，並得到估計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，如果不使用宏循環是很難甚至是不可能的。

Answer 1

我看到你的其他線程，但想在這裏發佈。

如果我理解正確： 您已經爲您的數據擬合了3個模型，並且每個模型使用相同的三個獨立變量/預測變量。 您已經顯示了與3個模型的每個預測變量相對應的beta參數。 您想從3個原始模型開始創建6個新模型，並且一次只更改一個β參數。

下面是我認爲會做你想做的一些SAS代碼。

但是，您的3個原始模型中的測試版估計都非常相似！所以我不知道這個練習揭示了什麼是「最佳」模型。

祝你好運！

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 ***;