I looked up the. Traditional SAS monospace output. To create a profile. Do i need to create a library to run mixed models? Did you get any error message? See Using Style Elements in PROC REPORT for details. Use the CATALOG procedure or the Explorer window to copy the profile to.

Part 1 of this document mmixed be found at. Because I was particularly interested in the analysis of variance, in Part 1 I approached the problem of mixed models first by looking at the use of the repeated statement in Proc Mixed. Remember that our main problem in any repeated measures analysis is to handle the fact that when we have several observations from the same subject, our error terms are often going to be correlated.

This is true whether the covariances fit the compound symmetry structure or we treat them as unstructured or autoregressive. But there is another way to get at this problem. Look at the completely fictitious data shown below. Except for the specific values, these look like the pattern we have seen before. I generated them by simply setting up data for each subject that had a different slope. For Subject 1 the scores had a very steep optiions, whereas for Subject 4 the line was almost ,ixed.

In other words there was variance to the slopes. Had all of the slopes been equal the lines parallel the off-diagonal correlations would have been equal except for error, and the variance of the slopes would have been 0. But when the slopes were unequal their variance was greater than 0 and the times would be differentially correlated. As I pointed out earlier, compound symmetry is associated directly with a model in which lines representing changes for subjects over time are parallel.

That means that when we assume compound symmetry, as we do in a standard repeated measures design, we are assuming that pattern heda subjects. Their intercepts may differ, but not their slopes. One way to look at the analysis of mixed models is to fiddle with the expected pattern of the correlations, as we did with the repeated command.

Another way is to look at the variances in the slopes, which we will do with the random command. With the appropriate selection of options the results opyions be the same. We will start first with optipns simplest approach. We will assume that subjects differ on average hfx managed forex account. This is really equivalent to our basic repeated measures ANOVA where we have a term for Subjects, reflecting subject differences, but where our assumption of compound symmetry forces us to treat the data by best time frame for forex trading computers that however subjects differ overall, they all have the same slope.

I am using the same missing data set that we used in the first part of the document for purposes of comparison. Here we will replace the Repeated command with the Random command. The "int" on the random statement tells the model to fit a different intercept for each subject, but to assume that the slopes are constant across subjects. I am requesting a covariance structure with compound proc mixed output options head. By only specifying "int" as random we have not allowed the slopes to differ, and thus we have forced compound symmetry.

We would have virtually the same output even if we specified that the covariance structure be "unstructured. Here I am venturing into territory that I know less well, but I think that I am correct in what follows. Remember that when we optiions compound symmetry we are specifying a pattern that results proc mixed output options head subjects showing parallel trends over time. So when we replace our repeated statement with a random statement and specify that "int" is the only random component, we are doing essentially what the repeated statement did.

We are not allowing for different slopes. But in the next analysis I am going to allow slopes to differ by entering "time" in the random statement along with "int. The commands for this analysis follow. We suddenly have a variable called "timecont. That is fine, but for the random variable I want time to be continuous.

So I have just made a copy **proc mixed output options head** the original "time," called it "timecont," and not put it in the class statement. The commands that follow assume that you have *proc mixed output options head* earlier commands to establish the data file etc. Notice that the pattern of results is similar to what we found in the earlier analyses compared with both the analysis using the repeated command and the analysis without time as a random effect.

However we only have 35 df for error for each test. The AIC for the earlier analysis in Part 1 of this opions using AR 1 as the covariance structure had an AIC of My preference would be to stay with the AR1 structure on the repeated command. That looks to me to be the best fitting model and one that makes logical sense. There is one more approach recommended by Guerin and Stroop They suggest that when we are allowing a model that has an AR 1 or UN covariance structure, we combine the random and repeated commands in the same run.

According to Littell et al. In other words cell Notice that that cell ophions is The effect for Group 1 is the difference between the mean of the last time in the first group cell 14 and the intercept, which equals That is the treatment effect for group 1 given in the solutions section of the table. Because there is only 1 df for groups, we don't have a treatment effect for group 2, though we can calculate it as For the effect of Time 1, we hear the deviation of the cell for Time 1 for the last group group 2 from the intercept, which equals For Times 2 and 3 we oroc subtract With 3 df for Time, we don't have an effect for Time 4, but again we can obtain it by subtraction as 0 - For the interaction effects we take cell means minus row and column effects plus the intercept.

So for Time11 we have Similarly kptions the other interaction effects. I should probably pay a great deal more attention to these treatment effects, but I will not do so here. If they were expressed in terms of deviations from the grand mean, rather than with respect to cell 24 I could get more excited about them. If SAS outout up its design matrix differently they would come prlc that way.

But they don't here. I know that most statisticians will come down on my head for making such a statement, and perhaps I am being sloppy, but I think that I get more information from looking at cell means and F statistics. Well after this page was originally written and I thought that I had everything all figured out well, I didn't really think that, but I hopedI discovered that life is not as simple as we would like it to be. The classic book in the field is Littell et al.

They have written about SAS in numerous books, and some of them worked on the development proc mixed output options head Proc Mixed. However others who know far more statistics than I will ever learn, and who have used SAS for years, have had great difficulty in deciding on the appropriate ways of writing the syntax. They spent 27 pages trying to decide on the correct analysis and ended up arguing that perhaps there is a better way than using mixed models anyway.

Now they did have somewhat of a special problem because they were proc mixed output options head an analysis of covariance because missing data was dependent, in part, on baseline measures. However other forms of analyses will allow a variable to be both a dependent variable and a covariate. If you try this with SPSS you will be allowed to enter Time1 as a covariate, but the solution is exactly the same as if you had not.

I haven't yet tried this is R or S-Plus. This points out that all of the answers are not out there. If John Overall can't figure it out, how are you and I supposed to? That last paragraph might suggest that I should just eliminate this whole ohtput, but that is much too extreme. Proc Mixed is not going to go away, and we have to get used to it.

All that I suggest is a bit of caution. But if you do want to consider alternatives, look at the Overall et al. Then look at other work that this group has done. But I can't leave this without bringing in one more complication. In other words, specifying time on the class variable turns time into a factor with 4 levels. It is very close to, but not exactly the same as, a test of linear and quadratic components.

For quadratic you would need to include time2 and its interaction with group. The 1 df is understandable because you have one degree mixedd freedom for each contrast. I am going to stick with my approach, at least for now. They are not the same, though they are very close. I don't know why they aren't the same, but I suspect that it has to do with the fact that Proc GLM uses a least squares solution while Proc Proc mixed output options head uses REML. Notice the different degrees of freedom for error, and remember that "mean" is equivalent to "timecont" and "group" is equivalent to the interaction.

To describe what EM does in a very few sentences, it basically uses the means and standard deviations of the existing observations to make estimates of the missing values. Having those estimates changes the mean and standard deviation of the data, and those new means and standard deviations are used as parameter estimates to make new predictions for the originally missing values. Those, in turn, change the means and variances and a new set of estimated values is created.

This process goes on iteratively until it stabilizes. For a discussion of NORM, see MissingDataNorm. I repeated this several times to get an estimate of the variability in the results. The resulting F s for three replications are shown below, along with the results of using Proc Mixed on the missing data with an autoregressive covariance structure and simply using the standard ANOVA with all subjects having any missing data deleted.

I will freely admit that I don't know exactly how to evaluate these results, but they are outpt least in line with each other except for the last column when uses casewise deletion. I find them encouraging.

## Finding the best covariance matrix in Proc Mixed

User's Guide, Second Edition PROC MIXED Contrasted with Other SAS Procedures; Response Variable Options ; Model Options ; OUTPUT Statement;. It omits these PROC REPORT statement options In order to produce PROC REPORT output that is option in the PROC REPORT statement to force PROC REPORT to. Do I need to output the transformed data and use that new data file in the proc mixed model statement, The macro you mentioned sounds a bit over my head.