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    <title>topic proc mixed repeated statemen to proc glmmix in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/proc-mixed-repeated-statemen-to-proc-glmmix/m-p/489083#M25413</link>
    <description>&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;data TMT;
input trat rep c1 c2 c3 c4 c5 c6 c7 c8 c9;
y=c1; conteo=1; output;
y=c2; conteo=2; output;
y=c3; conteo=3; output;
y=c4; conteo=4; output;
y=c5; conteo=5; output;
y=c6; conteo=6; output;
y=c7; conteo=7; output;
y=c8; conteo=8; output;
y=c9; conteo=9; output;
drop c1-c9;
datalines;
1	1	57.9	71.4	87.5	74.3	95.6	92.0	94.1	85.7	94.5
1	2	42.9	69.2	90.5	80.0	98.0	96.7	95.9	94.9	93.6
1	3	37.5	71.4	88.9	54.5	100.0	93.8	88.9	100.0	87.5
1	4	30.8	92.3	85.2	82.8	92.5	94.9	88.4	95.5	92.7
2	1	76.0	47.1	80.6	64.7	97.4	85.4	89.5	86.8	91.4
2	2	55.6	31.6	82.4	78.9	96.4	96.8	76.5	90.0	85.7
2	3	33.3	100.0	100.0	80.0	100.0	100.0	94.4	90.5	88.1
2	4	100.0	100.0	100.0	100.0	94.4	100.0	100.0	100.0	75.0
3	1	66.7	3.3	66.7	100.0	94.4	95.2	83.3	94.1	80.6
3	2	75.0	18.2	100.0	93.5	83.0	94.6	82.8	91.2	88.5
3	3	66.7	54.5	84.6	94.4	73.9	95.0	90.5	90.9	95.2
3	4	44.4	25.0	50.0	100.0	100.0	100.0	90.0	100.0	71.4
4	1	57.9	65.0	73.9	52.4	87.0	100.0	74.2	91.7	81.0
4	2	42.9	50.0	50.0	83.3	100.0	100.0	87.5	100.0	100.0
4	3	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0
4	4	50.0	70.4	94.1	56.4	100.0	95.1	84.4	79.1	92.5
;
proc mixed data=TMT;
class trat rep conteo;
model y = trat conteo trat*conteo;
repeated conteo / type= CS sub= rep r rcorr;
random rep; 
LSMEANS trat conteo trat*conteo/ pdiff adjust=tukey;
run;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;Hi!&amp;nbsp;&lt;/P&gt;&lt;P&gt;Hello&lt;BR /&gt;I did this program within a model of repeated measures, using CS as a covatianza structure (it was the only one that did not give me an error).&lt;BR /&gt;The procedure runs normally, but I want the literals to appear as in the lsmeans like glm; I tried to run in glmmix but send me error.&lt;/P&gt;</description>
    <pubDate>Wed, 22 Aug 2018 22:41:02 GMT</pubDate>
    <dc:creator>jescam05</dc:creator>
    <dc:date>2018-08-22T22:41:02Z</dc:date>
    <item>
      <title>proc mixed repeated statemen to proc glmmix</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/proc-mixed-repeated-statemen-to-proc-glmmix/m-p/489083#M25413</link>
      <description>&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;data TMT;
input trat rep c1 c2 c3 c4 c5 c6 c7 c8 c9;
y=c1; conteo=1; output;
y=c2; conteo=2; output;
y=c3; conteo=3; output;
y=c4; conteo=4; output;
y=c5; conteo=5; output;
y=c6; conteo=6; output;
y=c7; conteo=7; output;
y=c8; conteo=8; output;
y=c9; conteo=9; output;
drop c1-c9;
datalines;
1	1	57.9	71.4	87.5	74.3	95.6	92.0	94.1	85.7	94.5
1	2	42.9	69.2	90.5	80.0	98.0	96.7	95.9	94.9	93.6
1	3	37.5	71.4	88.9	54.5	100.0	93.8	88.9	100.0	87.5
1	4	30.8	92.3	85.2	82.8	92.5	94.9	88.4	95.5	92.7
2	1	76.0	47.1	80.6	64.7	97.4	85.4	89.5	86.8	91.4
2	2	55.6	31.6	82.4	78.9	96.4	96.8	76.5	90.0	85.7
2	3	33.3	100.0	100.0	80.0	100.0	100.0	94.4	90.5	88.1
2	4	100.0	100.0	100.0	100.0	94.4	100.0	100.0	100.0	75.0
3	1	66.7	3.3	66.7	100.0	94.4	95.2	83.3	94.1	80.6
3	2	75.0	18.2	100.0	93.5	83.0	94.6	82.8	91.2	88.5
3	3	66.7	54.5	84.6	94.4	73.9	95.0	90.5	90.9	95.2
3	4	44.4	25.0	50.0	100.0	100.0	100.0	90.0	100.0	71.4
4	1	57.9	65.0	73.9	52.4	87.0	100.0	74.2	91.7	81.0
4	2	42.9	50.0	50.0	83.3	100.0	100.0	87.5	100.0	100.0
4	3	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0
4	4	50.0	70.4	94.1	56.4	100.0	95.1	84.4	79.1	92.5
;
proc mixed data=TMT;
class trat rep conteo;
model y = trat conteo trat*conteo;
repeated conteo / type= CS sub= rep r rcorr;
random rep; 
LSMEANS trat conteo trat*conteo/ pdiff adjust=tukey;
run;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;Hi!&amp;nbsp;&lt;/P&gt;&lt;P&gt;Hello&lt;BR /&gt;I did this program within a model of repeated measures, using CS as a covatianza structure (it was the only one that did not give me an error).&lt;BR /&gt;The procedure runs normally, but I want the literals to appear as in the lsmeans like glm; I tried to run in glmmix but send me error.&lt;/P&gt;</description>
      <pubDate>Wed, 22 Aug 2018 22:41:02 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/proc-mixed-repeated-statemen-to-proc-glmmix/m-p/489083#M25413</guid>
      <dc:creator>jescam05</dc:creator>
      <dc:date>2018-08-22T22:41:02Z</dc:date>
    </item>
    <item>
      <title>Re: proc mixed repeated statemen to proc glmmix</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/proc-mixed-repeated-statemen-to-proc-glmmix/m-p/489311#M25417</link>
      <description>&lt;P&gt;In this thread&amp;nbsp;&lt;A href="https://communities.sas.com/t5/SAS-Statistical-Procedures/Proc-mixed/m-p/489027#M25407" target="_self"&gt;https://communities.sas.com/t5/SAS-Statistical-Procedures/Proc-mixed/m-p/489027#M25407&lt;/A&gt;&amp;nbsp;I noted that you cannot include "random rep;" with type=un. You cannot include it with type=cs either, or with most of the other commonly used types. You can &lt;EM&gt;optionally&lt;/EM&gt; include it with type=ar(1), to generate "AR(1) + RE". Refer to the paper by Littell et al. that I linked in the other thread, as well as&amp;nbsp;&lt;A href="https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/prodBK_59882_en.html" target="_self"&gt;https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/prodBK_59882_en.html&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I&amp;nbsp;am not entirely sure what you mean by "&lt;SPAN&gt;the literals to appear as in the lsmeans like glm". I'm guessing you mean letter assignments, which you can get using the LINES option on the LSMEANS statement.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;Tukey-adjusted pairwise comparisons among &lt;EM&gt;main effects means&lt;/EM&gt; are fine. But I do not recommend using Tukey-adjusted comparisons among &lt;EM&gt;interaction means&lt;/EM&gt;; they are too conservative because they control for &lt;EM&gt;all&lt;/EM&gt; pairwise comparisons (of which there are 630, for 4 x 9 = 36 means) whereas you are typically interested in only a subset of comparisons (198, if I've computed correctly). Consider SLICE and SLICEDIFF. &lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;In general, I believe pairwise comparisons are a poor approach to interpreting an interaction (especially when factors have many levels, for example 4 x 9), so I generally test what I consider to be contextually useful hypotheses with CONTRAST or ESTIMATE statements.&amp;nbsp;&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;If you post your GLIMMIX code, the Community might be able to make suggestions.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Thu, 23 Aug 2018 15:32:33 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/proc-mixed-repeated-statemen-to-proc-glmmix/m-p/489311#M25417</guid>
      <dc:creator>sld</dc:creator>
      <dc:date>2018-08-23T15:32:33Z</dc:date>
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