I am learning to proc mixed. So my following question is hypothetical and any help is appreciated:
Supposed that I am comparing three age different un-equal groups of runners who ran with different shoe types and would like to see if they are significantly different in terms of their speed.
Then, my data can have the following variables: subj_id, group name, shoe type, speed of running.
proc mixed data=temp;
class subj_id group_name shoe_type;
model speed_of_running= group_name shoe_type group_name*shoe_type;
random subj_id;
run;
I have the following questions:
Thanks
Pr>|t| indicates if the statistical test for the hypothesis that estimate for that LSMEAN is equal to zero. The standard interpretation is that when this value is less than 0.05, then the estimate is not equal to zero. For group AM, the LSMEAN is 5.5424, which according to this statistical test, it is significantly different than 0.
To compare group A to group B, use
lsmeans group_name/lines;
I see in the documentation, there is no lines option in PROC MIXED, but there is one in PROC GLIMMIX, which for this design and analysis ought to give the same results as PROC MIXED.
You didn't really explain the design of your study, and I would need to know that to determine if your code is correct. So please explain the design of your study, so we can understand how subjects get assigned to groups and shoe_types and how often they run once assigned.
Specifically, does each subject_id run and is timed a single time? Or does each subject_id run and is timed more than once? Does each subject_id wear different shoe types throughout the study? I assume each subject can only appear in one group.
With regards to your last two bullet items, the LSMEANS statement will compute the means for you and do statistical testing, so that you can compare groups and compare shoe types.
Thank you PaigeMiller
Let's consider that i have 35 subjects in the three unequal groups. the subject_id variable goes from 1 to 35. All subjects run 5 minutes with each of the shoe types while i measure their speed. For instance, it can be running bare feet, sport shoe and dress shoe. the order of shoe type for each subject is randomly assigned. so, technically, each subject can only appear in one group but is tested three times for the three conditions of shoe type.
PS:can you also tell me how the code would have been different if
a- each test was repeated three times. i.e. each subject belongs to one of the three age groups but runs 9 times (three times with each shoe).
b- can you also tell my how the code would have been different if the subject appeared in more than one group. For instance if my test groups instead of being different in age, they were the same subject by were were tested before and after having breakfast, or were tested morning, afternoon and night)
It seems to me then that you want this RANDOM statement
random subj_id(group_name);
since subject_id is nested within group_name.
So when i use lsmeans, waht does the p value referes to?
So if i have gorups A, B and C and i include a statement lsmean(group_name), i get something like the following:
Effect Group_name Estimate Error DF t Value Pr > |t|
Group_name A .......
Is the p value refering to the difference in mean of A as compared to all other groups?
thanks
Please show us the actual code used, and the actual table produced by that code.
proc mixed data=temp;
class subj GROUP ShoeType ;
model SPEED= GROUP ShoeType GROUP*ShoeType ;
random subj (GROUP);
lsmeans GROUP;
run;
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Kdisart YA 10:25 Monday, January 27, 2020 22
The Mixed Procedure
Model Information
Data Set WORK.TEMP
Dependent Variable SPEED
Covariance Structure Variance Components
Estimation Method REML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Containment
Class Level Information
Class Levels Values
subj 26 3 4 5 7 8 9 10 11 12 13 15 16
17 19 20 21 22 23 24 25 26 27
29 30 31 32
GROUP 3 AM OA YA
ShoeType 3 1 2 3
Dimensions
Covariance Parameters 2
Columns in X 16
Columns in Z 26
Subjects 1
Max Obs per Subject 234
Number of Observations
Number of Observations Read 234
Number of Observations Used 234
Number of Observations Not Used 0
Iteration History
Iteration Evaluations -2 Res Log Like Criterion
0 1 1001.35302689
1 1 954.65672525 0.00000000
Convergence criteria met.
Covariance Parameter
Estimates
Cov Parm Estimate
subj(GROUP) 1.5245
Residual 3.0052
Fit Statistics
-2 Res Log Likelihood 954.7
AIC (Smaller is Better) 958.7
AICC (Smaller is Better) 958.7
BIC (Smaller is Better) 961.2
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
GROUP 2 23 1.09 0.3540
ShoeType 2 202 13.11 <.0001
GROUP*ShoeType 4 202 2.78 0.0280
Least Squares Means
Standard
Effect GROUP Estimate Error DF t Value Pr > |t|
GROUP AM 5.5424 0.5565 23 9.96 <.0001
GROUP OA 5.4719 0.4311 23 12.69 <.0001
GROUP YA 4.6901 0.4311 23 10.88 <.0001
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
I also receive the following when I type lsmeans GROUP*ShoeType
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Least Squares Means
Standard
Effect GROUP ShoeType Estimate Error DF t Value Pr > |t|
GROUP*ShoeType AM 1 4.7795 0.6489 202 7.37 <.0001
GROUP*ShoeType AM 2 5.9108 0.6489 202 9.11 <.0001
GROUP*ShoeType AM 3 5.9370 0.6489 202 9.15 <.0001
GROUP*ShoeType OA 1 5.3605 0.5026 202 10.67 <.0001
GROUP*ShoeType OA 2 4.8596 0.5026 202 9.67 <.0001
GROUP*ShoeType OA 3 6.1958 0.5026 202 12.33 <.0001
GROUP*ShoeType YA 1 3.6963 0.5026 202 7.35 <.0001
GROUP*ShoeType YA 2 4.3853 0.5026 202 8.72 <.0001
GROUP*ShoeType YA 3 5.9887 0.5026 202 11.92 <.0001
Pr>|t| indicates if the statistical test for the hypothesis that estimate for that LSMEAN is equal to zero. The standard interpretation is that when this value is less than 0.05, then the estimate is not equal to zero. For group AM, the LSMEAN is 5.5424, which according to this statistical test, it is significantly different than 0.
To compare group A to group B, use
lsmeans group_name/lines;
I see in the documentation, there is no lines option in PROC MIXED, but there is one in PROC GLIMMIX, which for this design and analysis ought to give the same results as PROC MIXED.
Thank you for your response. so, if variable "YOA" refers to the three groups that are called AM, OA and YA and variable "cond" refer to the condition (e.g. shoe type) and include conditions 1, 2 and 3. when i include lsmeans YOA*cond/lines; in my codes, i see the following:
T Grouping for YOA*cond Least Squares Means (Alpha=0.05)
LS-means with the same letter are not significantly different.
YOA | cond | Estimate | |||
OA | 3 | 15.9907 | A | ||
A | |||||
OA | 1 | 14.4178 | B | A | |
B | A | ||||
OA | 2 | 13.7219 | B | A | |
B | A | ||||
AM | 2 | 13.2346 | B | A | |
B | A | ||||
AM | 3 | 12.613 | B | A | |
B | A | ||||
AM | 1 | 11.5453 | B | A | C |
B | C | ||||
YA | 3 | 10.5709 | B | C | |
C | |||||
YA | 1 | 7.4639 | C | ||
C | |||||
YA | 2 | 7.0647 | C |
does this mean that under condition 3 (i.e. the same shoe type), OA and AM were not different? also, AM conditions 1 and 2 were significantly different? In other words, which column do I have to look at?
thanks
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