OLS regression model

[uzma03505621]

Hi everyone, 

I am using proc reg for the analysis of my study data.

 Dependent variable= mcs score

independent variable= cat2 cat3 age income 

where cat2 and cat3 are both categorical variables. My reference group is category 1..I have created dummy variables.

My code is as follows:

ods graphics on;
proc reg data=dummyfinal plots(maxpoints=none);
model mcs42=cat2 cat3;
output out=new P=YHAT RSTUDENT=RESID L95M=LOW U95M=HIGH;
run;
quit;
ods graphics off;

after getting the predicted value (YHAT) of the dependent variable, I have to obtain the mean mcs scores across the 3 categories (cat1 cat2 cat3) along with the confidence intervals and do multiple comparison tests( eg: Tukey kramer).

can anyone please help me with the SAS codes that I should run to obtain the following results.

My results should look like this: 

means and SE
			MCS
			Mean (SE)	p value
cat1( reference group)			40.45 (0.94) ∗∗∗	<0.001
cat2			43.76 (0.71) ∗∗∗	<0.001
cat3			46.96 (0.78) ∗∗∗	<0.001

[PaigeMiller]

---

@uzma03505621 wrote:

Hi everyone, 

I am using proc reg for the analysis of my study data.

 Dependent variable= mcs score

independent variable= cat2 cat3 age income 

where cat2 and cat3 are both categorical variables. My reference group is category 1..I have created dummy variables.

My code is as follows:

ods graphics on;
proc reg data=dummyfinal plots(maxpoints=none);
model mcs42=cat2 cat3;
output out=new P=YHAT RSTUDENT=RESID L95M=LOW U95M=HIGH;
run;
quit;
ods graphics off;

after getting the predicted value (YHAT) of the dependent variable, I have to obtain the mean mcs scores across the 3 categories (cat1 cat2 cat3) along with the confidence intervals and do multiple comparison tests( eg: Tukey kramer).

can anyone please help me with the SAS codes that I should run to obtain the following results.

My results should look like this: 

means and SE
			MCS
			Mean (SE)	p value
cat1( reference group)			40.45 (0.94) ∗∗∗	<0.001
cat2			43.76 (0.71) ∗∗∗	<0.001
cat3			46.96 (0.78) ∗∗∗	<0.001

---

There are some things that really aren't clear, such as you say age and time are independent variables, but these are not in your model. In addition, you talk about a reference category of category1, even though you haven't put category1 into the model, and I assume these are three levels of a single variable.

So anyway, here is how to handle a categorical variable with 3 levels, which I have named CAT.

To get means in this case, you can use PROC GLM, and you don't have to create the dummy variables yourself.

proc glm data=dummyfinal;
class cat(ref='1');
model mcs42=cat;
means cat/t;
quit;

[uzma03505621]

I'm sorry for the typing error, the correct code is :

ods graphics on;
proc reg data=dummyfinal plots(maxpoints=none);
model mcs42=cat2 cat3 age income;
output out=new P=YHAT RSTUDENT=RESID L95M=LOW U95M=HIGH;
run;
quit;
ods graphics off;

My original variable is called category with three values (1,2,3). I created dummy variables as follows:
If category=2 then cat2=1 else 0
If category=3 then cat3=1 else 0
So when I run the proc reg program, category=1 will be used as reference by-default? ( please correct me if I am wrong)

Thanks for proc glm code, I have used this before. I am being told to use proc reg only, that's why I created dummy variables, I need help with codes to compare the new regression adjusted mcs MEANS (with confidence interval) between these 3 categories.

I appreciate your time and consideration.

Thank you.

[PaigeMiller]

---

@uzma03505621 wrote:
I'm sorry for the typing error, the correct code is :

ods graphics on;
proc reg data=dummyfinal plots(maxpoints=none);
model mcs42=cat2 cat3 age income;
output out=new P=YHAT RSTUDENT=RESID L95M=LOW U95M=HIGH;
run;
quit;
ods graphics off;

My original variable is called category with three values (1,2,3). I created dummy variables as follows:
If category=2 then cat2=1 else 0
If category=3 then cat3=1 else 0
So when I run the proc reg program, category=1 will be used as reference by-default? ( please correct me if I am wrong)

Thanks for proc glm code, I have used this before. I am being told to use proc reg only, that's why I created dummy variables, I need help with codes to compare the new regression adjusted mcs MEANS (with confidence interval) between these 3 categories.

I appreciate your time and consideration.

Thank you.

---

I don't know how to get the means that you are asking for using PROC REG only. As you can see, it's very easy to get the means from PROC GLM.

[uzma03505621]

I used dummy coding:

data dummyfinal;
set finalfile;
if category=2 then cat2=1; else cat2=0;
if category=3 then cat3=1; else cat3=0;

run;

Then I did proc reg (unadjusted model without income and age) as follows:

/*unadjusted model*/
ods graphics on;
proc reg data=dummyfinal plots(maxpoints=none);
model mcs42=cat2 cat3;
output out=new P=YHAT RSTUDENT=RESID L95M=LOW U95M=HIGH;
run;
quit;
ods graphics off;

My results look this like

Parameter Estimates
Variable	Label	DF	Parameter Estimate	Standard Error	t Value	Pr > |t|
Intercept	Intercept	1	41.48793	0.26104	158.93	<.0001
cat2		1	-4.81016	0.43795	-10.98	<.0001
cat3		1	11.55753	0.28816	40.11	<.0001

Now Yhat is my predicted dependent variable, I want to compare the statistically significant difference in the means of my predicted dependent variable across my three category (independent) variable.

So should I do ANOVA+post hoc of this predicted Yhat? or is there any other method to get an output as below:

Unadjusted means and SE
	PCS		MCS
	Mean (SE)	p value	Mean (SE)	p value
Category 1 (reference)	37.27 (0.96) ∗∗∗	<0.001	40.45 (0.94) ∗∗∗	<0.001
Category2	37.02 (0.97) ∗∗∗	<0.001	43.76 (0.71) ∗∗∗	<0.001
category3	38.38 (1.04) ∗	0.016	46.96 (0.78) ∗∗∗	<0.001

[PaigeMiller]

I think the real problem here is whoever told you that PROC REG has to be used, this is bad advice, when PROC GLM makes this simple.

Nevertheless, I still don't know how to do this with PROC REG, specifically I'm not sure how you get the CORRECT standard errors, and so I cannot advise further if PROC REG has to be used.