Select 50%, 25% of the rows randomly and mark them

[Cruise]

Dear SAS experts:

I'd like to create 100 indicator variables which mark 0 through 100% of the rows in a random fashion. For example, MARK50 in the mock data would mark 50% of the rows selected randomly while MARK25 variable would mark 25% of the rows in a random selection. Desirable dataset has 100 indicator dummy variables marking 0-100% of the rows in random.  

This is my attempt to manually conduct a simulation analysis in my previous post.  If I could just create mark 0, 15, 50, 75 and 100 then I would come up with a skeleton of the final desirable plot I want. I need this prelim information int he meantime.

https://communities.sas.com/t5/SAS-Programming/Simulate-parameter-estimates-of-the-model-for-missing...

Thank you so much for your precious time!

PS: I'm familiar with proc surveyselect. But I want to create indicator variables within the dataset rather than creating subsets of random samples.

DATA HAVE;
INPUT ID RANDOM MARK50 MARK25;
CARDS;
1  1  1
2  0  0
3  1  0
4  0  1
5  1  0
6  0  0
7  1  0
8  0  1
9  1  0
10 0  0
11 1  0
12 0  1
;
RUN;

[PaigeMiller]

DATA HAVE;
    INPUT ID;
    rand=rand('uniform');
    array mark mark1-mark99;
    do i=1 to dim(mark);
        if rand<(i/100) then mark(i)=1;
	else mark(i)=0;
    end;
CARDS;
1  1  1
2  0  0
3  1  0
4  0  1
5  1  0
6  0  0
7  1  0
8  0  1
9  1  0
10 0  0
11 1  0
12 0  1
;
RUN;

[Cruise]

@PaigeMiller 

Thanks a lot. I tried your approach on 1 thru 100 row dataset. How to tie mark1 totals to 1., so on., mark100 totals to 100. Possible? 

DATA HAVE;
    INPUT ID;
    rand=rand('uniform');
    array mark mark1-mark99;
    do i=1 to dim(mark);
        if rand<(i/100) then mark(i)=1;
	else mark(i)=0;
    end;
CARDS;
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;
RUN;

proc summary data=have;
var mark1-mark100;
output out=totals sum=;
run;
proc transpose data=totals;
run;
proc print; run; 

[PaigeMiller]

How to tie mark1 totals to 1., so on., mark100 totals to 100. Possible? 

I don't know what this means.

[Cruise]

@PaigeMiller correct me If I'm wrong. I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

[Reeza]

Wouldn't this just be an array?

data want;
set have;

call streaminit(300) ; *to allow for replication;
array pct(100) pct1-pct100;

do i=1 to 100;
pct(i) = rand('bernoulli', i/100);
end;

run;

---

@Cruise wrote:

Dear SAS experts:

I'd like to create 100 indicator variables which mark 0 through 100% of the rows in a random fashion. For example, MARK50 in the mock data would mark 50% of the rows selected randomly while MARK25 variable would mark 25% of the rows in a random selection. Desirable dataset has 100 indicator dummy variables marking 0-100% of the rows in random.  

This is my attempt to manually conduct a simulation analysis in my previous post.  If I could just create mark 0, 15, 50, 75 and 100 then I would come up with a skeleton of the final desirable plot I want. I need this prelim information int he meantime.

https://communities.sas.com/t5/SAS-Programming/Simulate-parameter-estimates-of-the-model-for-missing...

Thank you so much for your precious time!

PS: I'm familiar with proc surveyselect. But I want to create indicator variables within the dataset rather than creating subsets of random samples.

DATA HAVE;
INPUT ID RANDOM MARK50 MARK25;
CARDS;
1  1  1
2  0  0
3  1  0
4  0  1
5  1  0
6  0  0
7  1  0
8  0  1
9  1  0
10 0  0
11 1  0
12 0  1
;
RUN;

---

[PaigeMiller · Accepted solution]

DATA HAVE;
    INPUT ID;
    rand=rand('uniform');
CARDS;
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;
RUN;
proc rank data=have out=ranked;
    var rand;
	ranks ranks;
run;
data want;
	set ranked;
	array mark mark1-mark99;
	do i=1 to dim(mark);
	    if ranks<=i then mark(i)=1; 
		else mark(i)=0;
	end;
run;

[Cruise]

@PaigeMiller 

Your last approach is what I wanted. mark1 flags 1 row, mark2 flags 2 rows et.c.

my actual data has 20K rows and i will use these marks as an indicator of the extent of a missing, thus, replaced data in my manual simulation analysis. mark50, would indicate that the 50% of the variable of interest was missing,therefore, replaced by a proxy value (July1 for the month/day).

[PaigeMiller]

---

@Reeza wrote:

Wouldn't this just be an array?

data want;
set have;

call streaminit(300) ; *to allow for replication;
array pct(100) pct1-pct100;

do i=1 to 100;
pct(i) = rand('bernoulli', i/100);
end;

run;

---

---

It's still not 100% clear to me, but it seems that the question from @Cruise  requires only one value of 1 and 99 values of zero in column 1; and exactly 2 values of 1 and 98 values of zero in column 2; and so on. So, this use of the Bernoulli random variable does not produce that result.

[Reeza]

@PaigeMiller  given the last reponse of this:

I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

This would create those variables. 

PCT1 would have 1% with values of 1.

PCT50 would have 50% of values with 1, ie 50% selected.

[Reeza]

I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

This would create those variables. 

PCT1 would have 1% with values of 1.

PCT50 would have 50% of values with 1, ie 50% selected.

[PaigeMiller]

---

@Reeza wrote:

@PaigeMiller  given the last reponse of this:

I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

This would create those variables. 

PCT1 would have 1% with values of 1.

PCT50 would have 50% of values with 1, ie 50% selected.

---

data want;
set have;

call streaminit(300) ; *to allow for replication;
array pct(100) pct1-pct100;

do i=1 to 100;
pct(i) = rand('bernoulli', i/100);
end;

run;
proc summary data=want;
    var pct1-pct100;
	output out=stats sum=;
run;

If it produced the result as I now understand it, PROC SUMMARY should produce a sum of 1 for pct1, and 2 for pct 2 and so on, and this code does not produce that result.

[PaigeMiller]

I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

This would create those variables. 

PCT1 would have 1% with values of 1.

PCT50 would have 50% of values with 1, ie 50% selected.

[Reeza]

---

@PaigeMiller wrote:

---

@Reeza wrote:

@PaigeMiller  given the last reponse of this:

I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

This would create those variables. 

PCT1 would have 1% with values of 1.

PCT50 would have 50% of values with 1, ie 50% selected.

---

data want;
set have;

call streaminit(300) ; *to allow for replication;
array pct(100) pct1-pct100;

do i=1 to 100;
pct(i) = rand('bernoulli', i/100);
end;

run;
proc summary data=want;
    var pct1-pct100;
	output out=stats sum=;
run;

If it produced the result as I now understand it, PROC SUMMARY should produce a sum of 1 for pct1, and 2 for pct 2 and so on, and this code does not produce that result.

---

You would need to look at the mean, not the sum because the number of observations would affect the results. When done in that case, it most definitely does produce a series of approximately 1-100%.

[Reeza]

I thought if i have 100 observations and randomly selecting them 100 times. Total number of rows selected randomly aiming to cover the 50% of the dataset of 100 rows then I would end up selecting 50 rows. Right? therefore, mark50 would match up to the 50 rows selected in total. That is how I mean by matching markN to the N and the N% of the data selection in random on the 100 rows of data. Make sense?

This would create those variables. 

PCT1 would have 1% with values of 1.

PCT50 would have 50% of values with 1, ie 50% selected.

[Cruise]

I see what you're saying. I'll use both your and paige miller's approaches on my actual data for a comparison purpose. I'll report back in here.

[Reeza]

Doesn't matter which one you use, just wanted to ensure that I wasn't missing something. Both work and allow you to choose samples of a specific size though. You should review David L. Cassell's paper on don't be loopy - it covers simulations in SAS both using an approach that you're trying and survey select.

[Cruise]

@Reeza @PaigeMiller 

I ended up using  Reeza's approach since this took a percentile of the rows. Paige's approach marked N 1-100 rows in total from 27,440 rows.