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Posted 07-12-2014 10:20 PM
(764 views)

Hi everyone,

I used excel's solver for this problem. It works. But now I need to use SAS. I am kind of new to SAS. So, any help would be appreciated. And Here is my problem.

I have 3 matrices(or data set).

1. and 3. are Constant. 2. matrix changes with an index value(I need to find this value by changing until sum of percentage error of matrix 2 and matrix 3 becomes less than 0.0001. Here are the example matrices.

1. Matrix Z values of normal dist.(example)

t1 | t2 | t3 | t4 | t5 |
---|---|---|---|---|

-1.34 | 0.45 | -1.22 | 0.11 | 0.99 |

-1.1 | 0.2 | 0.3 | 0.23 | 0.99 |

... | ... | ... | ... | ... |

... | ... | ... | ... | ... |

2. Matrix( Depends on First Matrix and Index Value).

At the beginning second matrix is empty.

We calculate 2. Matrix Cell[1,1] = probit(1. matrix[1,1] - Index Value)

If sum of squared per. error is not small enough. We should apply new index and This matrix will change again.

t1 | t2 | t3 | t4 | t5 |
---|---|---|---|---|

. | . | . | . | . |

. | . | . | . | . |

... | ... | ... | ... | ... |

... | ... | ... | ... | ... |

3. Matrix is always constant as the 1. matrix

t1 | t2 | t3 | t4 | t5 |
---|---|---|---|---|

0.89 | 0.45 | 0.22 | 0.11 | 0 |

0.2 | 0.7 | 0.5 | 0.23 | 0.1 |

... | ... | ... | ... | ... |

... | ... | ... | ... | ... |

And Then We calculate percentage error between 2. and 3. matrix for every cell. ( ( 3.Matrix[1,1] - 2. Matrix[1,1])/ 3Matrix[1,1]) ^2 then sum all of them.

So Sum of these numbers should be less than 0.0001 and I should do this by changing index value. If sum of percentage errors are small enough. Then that is the index value that i am looking for.

Even simple examples would help. Maybe I can try to apply them by myself. There are some examples online. I couldnt find way to apply to this situation. Thanks in advance to everyone.

2 REPLIES 2

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You are operating a Matrix , you'd better post it at

and there is a problem. probit(p) should be 0<p<1 . but in your sample data they can't be true in sometime .

Anyway, here is some data step code you can refer to , But I suggest you post it at IML forum.

data one; input t1-t5; cards; -1.34 0.45 -1.22 0.11 0.99 -1.1 0.2 0.3 0.23 0.99 ; run; %let index=0.5; data two(keep=_t:); set one; array one{*} t1-t5; array two{*} _t1-_t5; do i=1 to dim(one); two{i}=probit(one{i}-&index ); end; run; data three; input t1-t5; cards; 0.89 0.45 0.22 0.11 0 0.2 0.7 0.5 0.23 0.1 ; run; data error(keep=e:); merge three two; array three{*} t1-t5; array two{*} _t1-_t5; array error{*} e1-e5; do i=1 to dim(error); error{i}= (divide((three{i}-two{i}),three{i}))**2; end; run;

Xia Keshan

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Here's one way to use PROC OPTMODEL to solve this optimization problem with one decision variable:

proc optmodel;

set ISET;

set JSET = 1..5;

num one {ISET, JSET};

num three {ISET, JSET};

read data one into ISET=[_N_] {j in JSET} <one[_N_,j]=col('t'||j)>;

read data three into [_N_] {j in JSET} <three[_N_,j]=col('t'||j)>;

var Index;

impvar Two {i in ISET, j in JSET} = probit(one[i,j] - Index);

min TotalError = sum {i in ISET, j in JSET} ((three[i,j]-Two[i,j])/three[i,j])^2;

solve;

print Two;

print Index;

quit;

But you need to clean up the data before you can use PROBIT.

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