Hi ballardw,
Thank you for your reply. Yes, I guess that "not working" is awful vague. What I meant was that the LINES option is not producing any output in addition to output produced by other options in the LSMEANS statement. There is no list of means in descending order with lines connecting means that are not statistically significant at alpha=0.10.
There are no error messages or unexpected comments in the SAS log. The SAS log does produce a warning message that "ADJUST=T implies no adjustment for simultaneous inference.", but that message is expected.
The output from the program is what I would expect from the SAS code except that the LINES option does not produce the list of means annotated with vertical lines.
I have copied a simplified version of the complete SAS program below, along with the SAS log and output. I have also attached to this message the same SAS program, log and output copied as text into a .SAS file. The PROC GLM in this code uses a BY variable "attr" that has 5 values in the data. I copied the output from only the first value of the BY variable: "attr=1". The data used in this example are embedded in the SAS program below.
*SAS PROGRAM;
options formdlim='-';
data two;
* INFILE datain1 dsd dlm="," firstobs=3;
input id $ sample $ @;
do attr= 1 to 5 by 1;
input score @ ;
output;
end;
datalines;
10001B A540 8 8 7 5 4
10001B B303 8 8 7 7 6
10001B C855 9 8 9 9 8
10001B D163 8 8 7 7 8
10001B E692 7 8 6 8 7
10002K B303 6 4 2 1 1
10002K A540 5 5 4 5 6
10002K D163 5 5 7 7 7
10002K C855 6 5 2 5 5
10002K E692 8 3 6 2 8
10003E C855 8 8 7 8 8
10003E D163 7 7 8 8 8
10003E A540 8 8 9 9 9
10003E B303 8 8 8 8 8
10003E E692 8 7 3 2 2
10004P D163 3 4 7 7 8
10004P C855 8 9 8 9 9
10004P B303 6 5 5 5 9
10004P A540 7 6 7 8 9
10004P E692 2 4 6 6 8
;
run;
proc sort data=two;
by attr;
proc glm data=two;
by attr;
class sample;
model score = sample / ss3;
lsmeans sample / stderr pdiff=all adjust=t alpha=0.10 lines out=lsmeans;
lsmeans sample / stderr pdiff alpha=0.10 lines;
lsmeans sample / stderr pdiff adjust=t alpha=0.10 lines;
title;
run;
quit;
*SAS LOG;
1793 options formdlim='-';
1794
1795 data two;
1796 * INFILE datain1 dsd dlm="," firstobs=3;
1797 input id $ sample $ @;
1798 do attr= 1 to 5 by 1;
1799 input score @ ;
1800 output;
1801 end;
1802 datalines;
NOTE: The data set WORK.TWO has 100 observations and 4 variables.
NOTE: DATA statement used (Total process time):
real time 0.00 seconds
cpu time 0.00 seconds
1823 ;
1824 run;
1825
1826 proc sort data=two;
1827 by attr;
1828
NOTE: There were 100 observations read from the data set WORK.TWO.
NOTE: The data set WORK.TWO has 100 observations and 4 variables.
NOTE: PROCEDURE SORT used (Total process time):
real time 0.00 seconds
cpu time 0.00 seconds
1829 proc glm data=two;
1830 by attr;
1831 class sample;
1832 model score = sample / ss3;
1833 lsmeans sample / stderr pdiff=all adjust=t alpha=0.10 lines out=lsmeans;
1834 lsmeans sample / stderr pdiff alpha=0.10 lines;
1835 lsmeans sample / stderr pdiff adjust=t alpha=0.10 lines;
1836 title;
1837 run;
NOTE: Interactivity disabled with BY processing.
WARNING: ADJUST=T implies no adjustment for simultaneous inference.
NOTE: The above message was for the following BY group:
attr=1
WARNING: ADJUST=T implies no adjustment for simultaneous inference.
NOTE: The above message was for the following BY group:
attr=2
WARNING: ADJUST=T implies no adjustment for simultaneous inference.
NOTE: The above message was for the following BY group:
attr=3
WARNING: ADJUST=T implies no adjustment for simultaneous inference.
NOTE: The above message was for the following BY group:
attr=4
WARNING: ADJUST=T implies no adjustment for simultaneous inference.
NOTE: The above message was for the following BY group:
attr=5
NOTE: The data set WORK.LSMEANS has 25 observations and 5 variables.
NOTE: PROCEDURE GLM used (Total process time):
real time 5.50 seconds
cpu time 2.31 seconds
1838
1839 quit;
*SAS OUTPUT;
attr=1
The GLM Procedure
Class Level Information
Class Levels Values
sample 5 A540 B303 C855 D163 E692
Number of Observations Read 20
Number of Observations Used 20
--------------------------------------------------------------------------------------------------------------------------
attr=1
The GLM Procedure
Dependent Variable: score
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 9.50000000 2.37500000 0.66 0.6313
Error 15 54.25000000 3.61666667
Corrected Total 19 63.75000000
R-Square Coeff Var Root MSE score Mean
0.149020 28.17413 1.901754 6.750000
Source DF Type III SS Mean Square F Value Pr > F
sample 4 9.50000000 2.37500000 0.66 0.6313
--------------------------------------------------------------------------------------------------------------------------
attr=1
The GLM Procedure
Least Squares Means
Standard LSMEAN
sample score LSMEAN Error Pr > |t| Number
A540 7.00000000 0.95087679 <.0001 1
B303 7.00000000 0.95087679 <.0001 2
C855 7.75000000 0.95087679 <.0001 3
D163 5.75000000 0.95087679 <.0001 4
E692 6.25000000 0.95087679 <.0001 5
Least Squares Means for effect sample
Pr > |t| for H0: LSMean(i)=LSMean(j)
Dependent Variable: score
i/j 1 2 3 4 5
1 1.0000 0.5853 0.3673 0.5853
2 1.0000 0.5853 0.3673 0.5853
3 0.5853 0.5853 0.1577 0.2822
4 0.3673 0.3673 0.1577 0.7152
5 0.5853 0.5853 0.2822 0.7152
NOTE: To ensure overall protection level, only probabilities associated with pre-planned comparisons should be used.
--------------------------------------------------------------------------------------------------------------------------
attr=1
The GLM Procedure
Least Squares Means
Standard LSMEAN
sample score LSMEAN Error Pr > |t| Number
A540 7.00000000 0.95087679 <.0001 1
B303 7.00000000 0.95087679 <.0001 2
C855 7.75000000 0.95087679 <.0001 3
D163 5.75000000 0.95087679 <.0001 4
E692 6.25000000 0.95087679 <.0001 5
Least Squares Means for effect sample
Pr > |t| for H0: LSMean(i)=LSMean(j)
Dependent Variable: score
i/j 1 2 3 4 5
1 1.0000 0.5853 0.3673 0.5853
2 1.0000 0.5853 0.3673 0.5853
3 0.5853 0.5853 0.1577 0.2822
4 0.3673 0.3673 0.1577 0.7152
5 0.5853 0.5853 0.2822 0.7152
NOTE: To ensure overall protection level, only probabilities associated with pre-planned comparisons should be used.
--------------------------------------------------------------------------------------------------------------------------
attr=1
The GLM Procedure
Least Squares Means
Standard LSMEAN
sample score LSMEAN Error Pr > |t| Number
A540 7.00000000 0.95087679 <.0001 1
B303 7.00000000 0.95087679 <.0001 2
C855 7.75000000 0.95087679 <.0001 3
D163 5.75000000 0.95087679 <.0001 4
E692 6.25000000 0.95087679 <.0001 5
Least Squares Means for effect sample
Pr > |t| for H0: LSMean(i)=LSMean(j)
Dependent Variable: score
i/j 1 2 3 4 5
1 1.0000 0.5853 0.3673 0.5853
2 1.0000 0.5853 0.3673 0.5853
3 0.5853 0.5853 0.1577 0.2822
4 0.3673 0.3673 0.1577 0.7152
5 0.5853 0.5853 0.2822 0.7152
NOTE: To ensure overall protection level, only probabilities associated with pre-planned comparisons should be used.
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