Simulation using SAS

[GehadElsayed123]

i want to simulate ,  the in-control performance of the Phase I T2 chart is evaluated when the missing values are estimated by each of
the four handling methods; MSS, EM, RG and SRG. We consider  the historical sample m (m=20), of size
n=1, five values for the percent of missing value k% (k% = 1%,), three values for the number of quality
characteristics p (2, 3 and 5) and four values for the correlation coefficient r(r = 0, ,). Similar conclusions from other
simulations (not shown here) are obtained when different values of p and r are considered. The value of h that produces an overall
probability of a false alarm a = 0.05, when full data is assumed, was computed using 20,000 simulation runs. Table I presents these
values of h according to the number of variables p and the number of samples m. Notice that these control limits do not depend on
the variance–covariance structure since they are calculated based on full data sets.

i do this code but i don't complete it , there are some errors 

so i do not know how i solve it . 

[pau13rown]

can you be more specific about the problem you're having. Have you read Rick Wicklin?

[PaigeMiller]

there are some errors

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Show us the SASLOG with the errors. Click on the {i} icon and paste the relevant parts of the SASLOG into there. 

[GehadElsayed123]

i want to simulate ,  the in-control performance of the Phase I T2 chart is evaluated when the missing values are estimated by each of
the four handling methods; MSS, EM, RG and SRG. We consider  the historical sample m (m=20), of size
n=1, five values for the percent of missing value k% (k% = 1%,), three values for the number of quality
characteristics p (2, 3 and 5) and four values for the correlation coefficient r(r = 0, ,). Similar conclusions from other
simulations (not shown here) are obtained when different values of p and r are considered. The value of h that produces an overall
probability of a false alarm a = 0.05, when full data is assumed, was computed using 20,000 simulation runs. Table I presents these
values of h according to the number of variables p and the number of samples m. Notice that these control limits do not depend on
the variance–covariance structure since they are calculated based on full data sets.

i do this code but i don't complete it , there are some errors 

so i do not know how i solve it . 

[FreelanceReinh]

@GehadElsayed123:

Why don't you continue the thread that you started six days ago with the identical text and (up to blank lines) identical attachment in the appropriate forum for SAS/IML? Respected experts asked you questions there and are awaiting your answers.

I'm sorry, I can't help you with this, although I like doing simulations with SAS. But I haven't been using SAS/IML for a long time and your request is unclear. For example, your (copied-and-pasted?) text mentions "five values for the percent of missing value", but specifies only one ("1%"). Same with the "four values for the correlation coefficient". The reference to "Table I" is useless as there is no such table shown in your post, nor in the attachment.

[Rick_SAS]

Many times the key to a successful simulation study is knowing how to generate one simulated sample before you try to simulate 10,000 samples. In your case, before you loop over a grid of parameters, know how to do each simulation separately. For example

Handling method=MSS  (?what is this?)

Percent of missing value = 1%

quality characteristics p=3

and correlation coefficient r = 0.5

Only after you can successfully generate 1 sample for these conditions, should you attempt 20,000.

If you get stuck, post the code along with a verbal explanation of what the "handling method" is and what you mean by "quality characteristics".

[GehadElsayed123]

i want to simulate ,  the in-control performance of the Phase I T2 chart is evaluated when the missing values are estimated by each of
the four handling methods; MS=mean substitution , EM=the EM algorithm , RG =regression imputation and SRG=stochastic regression imputation . We consider  the historical sample m (m=20), of size
n=1, one value for the percent of missing value k% (k% = 1%,), one value for the number of quality
characteristics p (2, ) and one value for the correlation coefficient r(r = 0, ,). 
 The value of h= ucl  that produces an overall  probability of a false alarm a = 0.05, when full data is assumed, was computed using 20,000 simulation runs. Table I presents these
values of h according to the number of variables p and the number of samples m. Notice that these control limits do not depend on
the variance–covariance structure since they are calculated based on full data sets.

i do this code but i don't complete it , there are some errors , i want to know ,how to solve theses errors ?      

so i do not know how i solve it . 

i want to simulate ,  the in-control performance of the Phase I T2 chart is evaluated when the missing values are estimated by each of
the four handling methods; MSS, EM, RG and SRG. We consider  the historical sample m (m=20), of size
n=1, five values for the percent of missing value k% (k% = 1%,), three values for the number of quality
characteristics p (2, 3 and 5) and four values for the correlation coefficient r(r = 0, ,). Similar conclusions from other
simulations (not shown here) are obtained when different values of p and r are considered. The value of h that produces an overall
probability of a false alarm a = 0.05, when full data is assumed, was computed using 20,000 simulation runs. Table I presents these
values of h according to the number of variables p and the number of samples m. Notice that these control limits do not depend on
the variance–covariance structure since they are calculated based on full data sets.

i do this code but i don't complete it , there are some errors 

so i do not know how i solve it .