Given the model:
Y = b0 + b1 x1 + b2 x2 + b3 x3
what command in SAS will allow me to see the confidence interval for Y?
proc reg; model y=x1 x2 x3;
see online for output details.
Related
I'm on windows, and I'll work only with windows.
I have a question about opening big files (PTX).
On each line, I will have the coordinates of a points X Y Z I {R G B} ({R, G, B} are not forced to be present).
Since my files are huge (sometimes > 100Go), I would like to read them fastly using memory map (I never did that before), or at least read chunck of memory instead of reading it line by line.
My question is : if I read chunck of memory using
ifstream bigFile("mybigfile.dat");
constexpr size_t bufferSize = 1024 * 1024;
unique_ptr<char[]> buffer(new char[bufferSize]);
while (bigFile)
{
bigFile.read(buffer.get(), bufferSize);
// process data in buffer
}
for example, is there a way to be sure that my buffer won't stop in the middle of a line?
For example, my files is
x1 y1 z1 i1 r1 g1 b1
x2 y2 z2 i2 r2 g2 b2
x3 y3 z3 i3 r3 g3 b3
x4 y4 z4 i4 r4 g4 b4
x5 y5 z5 i5 r5 g5 b5
and I want to create a std::vector<Point>. So I read a buffer size of this file, put it in the buffer, and then I take data from buffer to create my points. But how can I be sure that the buffer won't stop at r3?
If the buffer contains x1 y1 z1 i1 r1 g1 b1 x2 y2 z2 i2 r2 g2 b2 x3 y3 z3 i3 r3 I can't create a point using only x3, y3, z3, i3, r3. I would need g3 and b3 too.
Is there a way to take care of that? I hope that it is understandable, English isn't my native language and I'm not sure I explained it well...
I'm a little bit confused about a result that I got after a coefficients reduction on a constraint of a linear programming problem.
The problem is:
maximize z = x1 + x2 + x3 + x4 + x5 + x6
subject to: 6*x1 + 3*x2 - 5*x3 + 2*x4 + 7*x5 - 4*x6 <= 15
where:
1<=x1<=2 continuos
1<=x2<=2 continuos
1<=x3<=2 continuos
1<=x4<=2 continuos
1<=x5<=2 continuos
1<=x6<=2 continuos
After the coefficients reduction the contraints will be:
subject to: 3*x1 + 3*x2 - 3*x3 + 2*x4 + 3*x5 - 3*x6 <= 8
as stated in the Applied Integer Programming book (Der-San Chen - Robert G.Batson - Yu Dang) at page 96 (there is a little error at page 97. The x1 coefficient is 3 not 1).
After that I've tried to submit the problem to ampl with and without the coefficients reduction. But I got two different results:
[without coefficients reduction]
CPLEX 12.6.1.0: optimal integer solution; objective 11.57142857
display x;
x1 2
x2 2
x3 2
x4 2
x5 1.57
x6 2
[with coefficients reduction]
CPLEX 12.6.1.0: optimal integer solution; objective 11.33333333
display x;
x1 2
x2 2
x3 2
x4 2
x5 1.33
x6 2
why? can the solution be considered correct anyway even if the result for x5 is a little different?
I've used three different solver (minos, gurobi, cplex) but they output the same results on the problem.
If you are referring to the technique in 4.4.3, then it's clear what's the problem here.
Suppose we are given a constraint of the form
a1*y1+ a2*y2 + ... + ai*yi < b
where yi = 0 or 1
You are not allowed to use this technique, as your coefficients are continuous ( in [1,2]) and not binary as needed here!
Here are my data. Data are structured like so: id x1 x2 x3 y.
I used proc mixed to analyze it, but now want to determine regression coefficients and I don't know how to do it. I'm only a beginner with sas. From the results I see that x1, x2, x3 and x1x2x3 are the significant effects, but how to determine the coefficients alpha, beta, gamma, delta, theta:
y = theta + alpha*x1 + beta*x2 + gamma*x3 + delta*x1*x2*x3
This is my code:
ods graphics on;
proc mixed data=test;
class x1 x2 x3;
model y = x1 | x2 | x3 / solution residual;
random id;
run;
ods graphics off;
EDIT 1: Here is a part of the table Solutions for Fixed Effects:
Since x1 has two levels, there are two rows for it in the table. Do I get the effect of x1 by summing these two values: -109.07 for the first row and 0 for the second, or should I do something else? Note that this is 2^k design. The effect of x1 should be computed as half the difference between the average values for y when x1 is high (20) and when it is low (10).
Based on your model, x1, x2, x3 should be treated as continuous variables, then you should be able to get the coefficients in your model.
proc mixed data=test;
model y=x1 x2 x3 x1*x2*x3/ solution residual;
random id/s;
run;
However, based on your code and the values of x1, x2 and x3, it would be better to treat them as categorical variable as what you did, then the Estimate in your table actually is the mean difference between whatever two levels. The link below may help you understand your results.
http://support.sas.com/kb/38/384.htmlexplanation of estimation of coefficients
The solution option should generate your estimates.You need to include it on the model and random statements. You should see two tables, Solution for Fixed Effects and Solution for Random Effects that hold the estimates.
proc mixed data=test;
class x1 x2 x3;
model y = x1 | x2 | x3 / solution residual;
random id / s;
run;
The Random Coefficients example in the documentation is close to your question.
https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect034.htm
I have a situation where I want to associate a fixed cost in my optimization function if a summation is even. That is, if
(x1 + x2 + x3)%2 = 0
Is there a way this can be modeled in CPLEX? All the variables are binary, so really, I just want to express x1 + x2 + x3 = 0 OR x1 + x2 + x3 = 2
Yes, you can do this by introducing a new binary variable. (Note that we are modifying the underlying formulation, not tinkering with CPLEX per se for the modulo.)
Your constraints are
x1 + x2 + x3 = 0 OR 2
Let's introduce a new binary variable Y and rewrite the constraint.
Combined Constraint: x1 + x2 + x3 = 0(1-Y) + 2Y
This works because if Y is 0, one of the choices gets selected, and if Y=1 the other choice gets selected.
When simplified:
x1+x2+x3-2Y = 0
x_i, Y binary
Addendum
In your specific case, the constraint got simplified because one of the rhs terms was 0. Instead, more generally, if you had b1 or b2 as the two rhs choices,
the constraint would become
x1 + x2 + x3 = b1(Y) + b2(1-Y).
If you had inequalities in your constraint (<=), you'd use the Big-M trick, and then introduce a new binary variable, thereby making the model choose one of the constraints.
Hope that helps.
My colleague and I are running exactly the same SAS PROC LOGISTIC, but with different input files.
SAS models ooX = 1 when I do it, and ooX = 0 when he does it.
We've checked record counts and FREQ counts for the main variables. They are the same.
Type 3 analysis of effects are the same. MLE estimates are the same, except for the intercept.
Does SAS require input to be sorted a certain way?
PROC LOGISTIC data = TTTT;
class ooX Y1 Y2 Y3 Y4;
model ooX = Y1 Y2 Y3 q1 q2 q3;
RUN;
If your data are not sorted you can specify the order of your outcome variable right after calling PROC LOGISTIC.
I don't have the data, but assuming that ooX is a binary outcome variable with levels 0 and 1, the model will default to modeling ooX = 0 unless you specify that you want it in descending order.
PROC LOGISTIC data = TTTT descending; /* will model ooX = 1 */
class ooX Y1 Y2 Y3 Y4; /* Not sure if it makes sense to have your outcome in the class statement */
model ooX = Y1 Y2 Y3 q1 q2 q3;
RUN;
As explained in SAS manual (http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_logistic_sect030.htm)
For binary response data with event and nonevent categories, if your event category has a higher Ordered Value, then by default the nonevent is modeled.