如何在SAS中使用不同參數擬合線性回歸模型

Question

我有一個包含三個自變量的線性模型。 我在下面有最終模型。 我想使用increas_po和reducing_po變量來重新估計回歸模型中的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

我想做的是編寫一個do循環以產生六個可能性組合：

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

我想使用這些參數中的每一個來重新擬合模型並獲得估計的y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，即使不使用宏和循環，也很難甚至不可能。

Answer 1

我看到了您的其他主題，但想在這里發布。

如果我理解正確：您已經為數據擬合了3個模型，並且每個模型都使用相同的三個獨立變量/預測變量。 您已經顯示了對應於3個模型的每個預測變量的beta參數。 您要通過從3個原始模型開始並一次僅更改一個beta參數來創建6個新模型。

以下是一些我想可以做您想要的SAS代碼。

但是，您的3個原始模型中的beta估算值都非常相似！ 因此，我不知道此練習可以揭示“最佳”模型方面的任何內容。

祝好運！

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 ***;