Date: Tue, 5 Jan 2010 14:05:56 +0100
Reply-To: Antonio ML <amllistas@gmail.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Antonio ML <amllistas@gmail.com>
Subject: Re: Mixed design? 2 ways MANOVA? or what? and how...
In-Reply-To: <7814E7B977FF4EB5AF0E126315F56127@MJHP>
Content-Type: multipart/alternative;
Thank you very much!. But please one more question.
Why you don't use REPEATED for treatment?
and, why the CSR (compound simetry, with corr) structure matrix? instead
of VC (variance components)
I mean:
This
/REPEATED = trat | SUBJECT(pat) COVTYPE(DIAG) .
instead of this:
/RANDOM INTERCEPT treatment | SUBJECT(patient) COVTYPE(CSR).
So it would be like this:
MIXED
var1 BY trat
/CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
PCONVERGE(0.000001, ABSOLUTE)
/FIXED = trat | SSTYPE(3)
/METHOD = REML
/PRINT = SOLUTION TESTCOV
/REPEATED = trat | SUBJECT(pat) COVTYPE(DIAG) .
I'm really don't understand this deeply so please forgive me if the
question is silly.
*Thank you *again
MaxJasper escribió:
> Running:
>
>
> MIXED
> v9 BY treatment
> /CRITERIA = CIN(95) MXITER(1000) MXSTEP(50) SCORING(1)
> SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
> PCONVERGE(0.000001, ABSOLUTE)
> /FIXED = treatment | SSTYPE(3)
> /METHOD = REML
> /PRINT = SOLUTION TESTCOV
> /RANDOM INTERCEPT treatment | SUBJECT(patient) COVTYPE(CSR)
> /EMMEANS = TABLES(OVERALL) .
>
> Results:
>
> ****
>
> **Type III Tests of Fixed Effects(a)**
>
> Source
>
>
>
> Numerator df
>
>
>
> Denominator df
>
>
>
> F
>
>
>
> Sig.
>
> Intercept
>
>
>
> 1
>
>
>
> 6.030
>
>
>
> 90.409
>
>
>
> .000
>
> treatment
>
>
>
> 4
>
>
>
> 23.115
>
>
>
> 4.379
>
>
>
> .009
>
> *a Dependent Variable: v9.*
>
> * *
>
> ****
>
> **Estimates of Fixed Effects(b)**
>
> Parameter
>
>
>
> Estimate
>
>
>
> Std. Error
>
>
>
> df
>
>
>
> t
>
>
>
> Sig.
>
>
>
> 95% Confidence Interval
>
> Upper Bound
>
>
>
> Lower Bound
>
> Intercept
>
>
>
> 35.220000
>
>
>
> 6.177033
>
>
>
> 17.262
>
>
>
> 5.702
>
>
>
> .000
>
>
>
> 22.202668
>
>
>
> 48.237332
>
> [treatment=1]
>
>
>
> 21.608571
>
>
>
> 6.641639
>
>
>
> 23.035
>
>
>
> 3.253
>
>
>
> .003
>
>
>
> 7.870464
>
>
>
> 35.346678
>
> [treatment=2]
>
>
>
> 5.330439
>
>
>
> 6.962174
>
>
>
> 23.247
>
>
>
> .766
>
>
>
> .452
>
>
>
> -9.063451
>
>
>
> 19.724329
>
> [treatment=3]
>
>
>
> -1.384286
>
>
>
> 6.641639
>
>
>
> 23.035
>
>
>
> -.208
>
>
>
> .837
>
>
>
> -15.122393
>
>
>
> 12.353821
>
> [treatment=4]
>
>
>
> 14.572857
>
>
>
> 6.641639
>
>
>
> 23.035
>
>
>
> 2.194
>
>
>
> .039
>
>
>
> .834750
>
>
>
> 28.310964
>
> [treatment=5]
>
>
>
> 0(a)
>
>
>
> 0
>
>
>
> .
>
>
>
> .
>
>
>
> .
>
>
>
> .
>
>
>
> .
>
> *a This parameter is set to zero because it is redundant.*
>
> *b Dependent Variable: v9.*
>
> * *
>
> ****
>
> **Estimates of Covariance Parameters(a)**
>
> Parameter
>
>
>
> Estimate
>
>
>
> Std. Error
>
>
>
> Wald Z
>
>
>
> Sig.
>
>
>
> 95% Confidence Interval
>
> Lower Bound
>
>
>
> Upper Bound
>
> Residual
>
>
>
> 56.369642
>
>
>
> 233566.456629
>
>
>
> .000
>
>
>
> 1.000
>
>
>
> .000000
>
>
>
> .
>
> Intercept + treatment [subject = patient]
>
>
>
> CSR diagonal
>
>
>
> 101.690200
>
>
>
> 175174.842062
>
>
>
> .001
>
>
>
> 1.000
>
>
>
> .000000
>
>
>
> .
>
> CSR rho
>
>
>
> .036090
>
>
>
> 636.381380
>
>
>
> .000
>
>
>
> 1.000
>
>
>
> -1.000000
>
>
>
> 1.000000
>
> *a Dependent Variable: v9.*
>
> * *
>
> *Variable V9 Conclusion:*
>
> *Taking into account variations between patients, treatment=1 & 4 have
> significat (p<0.05) effect on patients relative to treatment=5, and
> that there is no sig different effect betwen treatment=2,3 and
> treatement=5.*
>
> *Above procedure can be repeated for V1.....V9.*
>
> * *
>
> *If V1...V9 are assumed to be dependent, then a Canonical Correlation
> analysis can be performed using MANOVA (not available in menus).*
>
> -----Original Message-----
> *From:* SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] *On
> Behalf Of *Antonio ML
> *Sent:* Monday, January 04, 2010 4:27
> *To:* SPSSX-L@LISTSERV.UGA.EDU
> *Subject:* Mixed design? 2 ways MANOVA? or what? and how...
>
> Sure I didn't explain myself well.. sorry. And sorry for long time
> I took to answer but my whole family had pass the A flu this
> Christmas!
>
> I'll try to explain my problem again 'cos I still don't get it.
>
>
> I only have 7 patients (we'll may get some more but not much
> more), and we measure 9 different variables (independents things
> like height, weight,..) under 5 treatments (5 different drugs).
> It's like in 5 different conditions (treatments) we measure the
> same 9 things to the same 7 patients, what could look like 5
> repleted measures (?). The problem is like we only have this 7
> patients (!) and, because of the nature of the research is very
> hard and expensive to find more patients with this
> characteristics, so we use the same 7 for the 5 experiments and we
> measure the same things (variables v1 to v9) every time. This will
> guide our future research.
>
> The data look like this: (tab separated, decimal separator a dot '.')
>
> caseid trat pat var1 var2 var3 var4 var5
> var6 var7 var8 var9
> 1 1 1 2 4 0 2 0 2 23 15 52
> 2 1 2 4 10 4 5 0 2 35 5 43
> 3 1 3 5.4 12.4 1.4 11 0 0 2 0 67.8
> 4 1 4 4 3 2 0 4 4 10 34 39
> 5 1 5 1 1 0 0 0 1 5 19 73
> 6 1 6 4 3 0 4 1 5 14 10 59
> 7 1 7 0 3 0 0 0 2 5 26 64
> 8 2 1 2.9 2.2 5.1 14.5 0 2.2 16.6
> 5 31.17
> 9 2 2 1 1 23.3 16.5 1.9 1 9.7 1.9
> 42.7
> 10 2 3 23.7 17.5 11.34 23.6 0.7 0 0
> 0 23.16
> 11 2 4 3.6 5.4 0.9 21.4 1.8 7.1 22.3
> 18.75 18.75
> 12 2 5 0 0.7 3.4 0.7 9.6 6.2 4.8
> 44.5 69.9
> 13 2 6
> 14 2 7 0 1.27 1.27 2.5 0.6 7.6 4.43
> 31.6 50.73
> 15 3 1 2.7 2.7 10.8 13.5 0 3.6 15.3
> 2.7 48.7
> 16 3 2 46.8 1.8 30.3 3.7 0 0 0.9
> 0 16.5
> 17 3 3 7.4 9.26 20.4 20.4 0 1.8 11.1
> 0 29.64
> 18 3 4 15 7 28 7 20 2 6 3 12
> 19 3 5 21.3 8.73 6.67 7.77 3.87 0.97
> 2.9 5.82 41.97
> 20 3 6 19.3 11.7 3.88 3.88 1 1 6.8
> 5.9 46.54
> 21 3 7 1.9 5.7 0.9 3.8 4.7 5.7 7.5
> 28.3 41.5
> 22 4 1 4.2 10.86 0 0 0.84 0 0.84
> 5.06 78.2
> 23 4 2 31.1 37.9 0 0 0 0 0 0 31
> 24 4 3 14.8 37.3 0 5 0 0 3 0 34.48
> 25 4 4 3 20 2 19 1 11 6 6 32
> 26 4 5 13 45 0 0 0 6 0 0 36
> 27 4 6 22.1 17.7 0 0.88 0.88 0 0
> 1.77 56.67
> 28 4 7 0 4.5 0 1.8 0 0.9 2.7 9.9 80.2
> 29 5 1 1.74 0.87 13.9 24.35 1.74 0.87
> 22.6 0.87 33.06
> 30 5 2 35.13 5.15 12 4.3 14.5 0.85
> 1.7 1.7 24.67
> 31 5 3 12.7 4.9 14.6 25.2 0 1 3.9
> 0 37.7
> 32 5 4 25.9 6.48 12.95 6.5 19.45 4.6
> 1.85 0.9 21.37
> 33 5 5 24.3 16.5 8.7 1.9 13.6 0.97
> 1.9 2.87 29.26
> 34 5 6 18.1 12.4 0 3.8 2.86 3.8 5.7
> 2.86 50.48
> 35 5 7 0 2.7 0 0.9 0 8 10.7 27.7 50
>
> What we'd like to analyze is differences between treatments for
> the 9 variables (var1,..vae9), but I guess we have to take in
> count the patient variable (dependencies). We'd like to conclude
> things like "/treatment 'x' seems to get higher scores in the
> variables var1, var 2,.... and treatment 'y' computes higher in
> var 8 and 9 than the rest of treatments/" for example.
>
>
> Gene, Thank you a lot for your help and comments they are really
> useful for me, especially all about mixed designs I think there is
> the clue.
> and happy new year!
>
> Gene Maguin escribió:
>> Bruce,
>>
>> Yes, it does. I assumed that v1 to v4 were the same variable purely on the
>> fact that Antonio said they all had the same range. Although it turns out
>> that they are the same, they could have the same range and not be the same.
>>
>> But suppose they were different variables, with or without different ranges.
>> Although GLM probably would be what I'd use, can the analysis be done in
>> mixed. My thought is yes because the residual covariance can be represented
>> as UNstructured and a measure or treatment by measure interaction would
>> indicate significant differences in means. If the ranges were different, the
>> means probably should differ and the measure main effect would be
>> uninteresting. But a treatment by measure interaction would not necessarily
>> be uninteresting.
>>
>> Gene Maguin
>>
>>
>>
>>>> It's not clear to me whether v1 to v4 represent 4 different variables
>>>>
>> (e.g.,
>> height, weight, resting heart rate, and diastolic blood pressure), or the
>> same variable measured on 4 occasions (or under 4 different conditions,
>> etc). Gene, does your MIXED syntax assume it's the same variable measured
>> repeatedly? Thanks for clarifying.
>>
>> Bruce
>>
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>>
>> Antonio.
>> Gracias, Efharisto poli, Grazie, Thank you, Tak,
>> Shukran, Hvala, Blagodaria, Bedankt, Danke schön,
>> Shukriya, Tesekuler, Merci, Spasiba, Obrigato,
>> Go raibh maith agat, Arigato, Dishklenle,
>> Dankon, Tashakkur
>>
>>
>
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