Dear Angelo
I suggest you do an online search for marginality which may help to
explain the relationship between main effects and interactions. As I
said in my original email this is a complicated subject which we are not
going to retype for you.
If you are doing this as a student I suggest you sue your university for
failing to train you appropriately and if it is part of your employment
I suggest you find a better employer.
On 21/07/2015 10:04, angelo.arc...@virgilio.it wrote:
Dear Bert,
thank you for your feedback. Can you please provide some references
online so I can improve "my ignorance"?
Anyways, please notice that it is not true that I do not know statistics
and regressions at all, and I am strongly
convinced that my question can be of interest for some one else in the
future.
This is what forums serve for, isn't it? This is why people help each
other, isn't it?
Moreover, don't you think that I would not have asked to this R forum if
I had the possibility to ask or pay a statician?
Don't you think I have done already my best to study and learn before
posting this message? Trust me, I have read different
online tutorials on lme and lmer, and I am confident that I have got the
basic concepts. Still I have not found the answer
to solve my problem, so if you know the answer can you please give me
some suggestions that can help me?
I do not have a book where to learn and unfortunately I have to analyze
the results soon. Any help? Any online reference to-the-point
that can help me in solving this problem?
Thank you in advance
Best regards
Angelo
----Messaggio originale----
Da: bgunter.4...@gmail.com
Data: 21-lug-2015 3.45
A: "angelo.arc...@virgilio.it"<angelo.arc...@virgilio.it>
Cc: <li...@dewey.myzen.co.uk>, <r-help@r-project.org>
Ogg: Re: [R] R: Re: Differences in output of lme() when introducing
interactions
I believe Michael's point is that you need to STOP asking such
questions and START either learning some statistics or work with
someone who already knows some. You should not be doing such analyses
on your own given your present state of statistical ignorance.
Cheers,
Bert
Bert Gunter
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
-- Clifford Stoll
On Mon, Jul 20, 2015 at 5:45 PM, angelo.arc...@virgilio.it
<angelo.arc...@virgilio.it> wrote:
> Dear Michael,
> thanks for your answer. Despite it answers to my initial
question, it does not help me in finding the solution to my problem
unfortunately.
>
> Could you please tell me which analysis of the two models should
I trust then?
> My goal is to know whether participants’ choices
> of the dependent variable are linearly related to their own
weight, height, shoe size and
> the combination of those effects.
> Would the analysis of model 2 be more
> correct than that of model 1? Which of the two analysis should I
trust according to my goal?
> What is your recommendation?
>
>
> Thanks in advance
>
> Angelo
>
>
>
>
>
> ----Messaggio originale----
> Da: li...@dewey.myzen.co.uk
> Data: 20-lug-2015 17.56
> A: "angelo.arc...@virgilio.it"<angelo.arc...@virgilio.it>,
<r-help@r-project.org>
> Ogg: Re: [R] Differences in output of lme() when introducing
interactions
>
> In-line
>
> On 20/07/2015 15:10, angelo.arc...@virgilio.it wrote:
>> Dear List Members,
>>
>>
>>
>> I am searching for correlations between a dependent variable and a
>> factor or a combination of factors in a repeated measure design.
So I
>> use lme() function in R. However, I am getting very different
results
>> depending on whether I add on the lme formula various factors
compared
>> to when only one is present. If a factor is found to be significant,
>> shouldn't remain significant also when more factors are
introduced in
>> the model?
>>
>
> The short answer is 'No'.
>
> The long answer is contained in any good book on statistics which you
> really need to have by your side as the long answer is too long to
> include in an email.
>
>>
>> I give an example of the outputs I get using the two models. In
the first model I use one single factor:
>>
>> library(nlme)
>> summary(lme(Mode ~ Weight, data = Gravel_ds, random = ~1 | Subject))
>> Linear mixed-effects model fit by REML
>> Data: Gravel_ds
>> AIC BIC logLik
>> 2119.28 2130.154 -1055.64
>>
>> Random effects:
>> Formula: ~1 | Subject
>> (Intercept) Residual
>> StdDev: 1952.495 2496.424
>>
>> Fixed effects: Mode ~ Weight
>> Value Std.Error DF t-value p-value
>> (Intercept) 10308.966 2319.0711 95 4.445299 0.000
>> Weight -99.036 32.3094 17 -3.065233 0.007
>> Correlation:
>> (Intr)
>> Weight -0.976
>>
>> Standardized Within-Group Residuals:
>> Min Q1 Med Q3 Max
>> -1.74326719 -0.41379593 -0.06508451 0.39578734 2.27406649
>>
>> Number of Observations: 114
>> Number of Groups: 19
>>
>>
>> As you can see the p-value for factor Weight is significant.
>> This is the second model, in which I add various factors for
searching their correlations:
>>
>> library(nlme)
>> summary(lme(Mode ~ Weight*Height*Shoe_Size*BMI, data =
Gravel_ds, random = ~1 | Subject))
>> Linear mixed-effects model fit by REML
>> Data: Gravel_ds
>> AIC BIC logLik
>> 1975.165 2021.694 -969.5825
>>
>> Random effects:
>> Formula: ~1 | Subject
>> (Intercept) Residual
>> StdDev: 1.127993 2494.826
>>
>> Fixed effects: Mode ~ Weight * Height * Shoe_Size * BMI
>> Value Std.Error DF t-value
p-value
>> (Intercept) 5115955 10546313 95 0.4850941
0.6287
>> Weight -13651237 6939242 3 -1.9672518
0.1438
>> Height -18678 53202 3 -0.3510740
0.7487
>> Shoe_Size 93427 213737 3 0.4371115
0.6916
>> BMI -13011088 7148969 3 -1.8199949
0.1663
>> Weight:Height 28128 14191 3 1.9820883
0.1418
>> Weight:Shoe_Size 351453 186304 3 1.8864467
0.1557
>> Height:Shoe_Size -783 1073 3 -0.7298797
0.5183
>> Weight:BMI 19475 11425 3 1.7045450
0.1868
>> Height:BMI 226512 118364 3 1.9136867
0.1516
>> Shoe_Size:BMI 329377 190294 3 1.7308827
0.1819
>> Weight:Height:Shoe_Size -706 371 3 -1.9014817
0.1534
>> Weight:Height:BMI -109 63 3 -1.7258742
0.1828
>> Weight:Shoe_Size:BMI -273 201 3 -1.3596421
0.2671
>> Height:Shoe_Size:BMI -5858 3200 3 -1.8306771
0.1646
>> Weight:Height:Shoe_Size:BMI 2 1 3 1.3891782
0.2589
>> Correlation:
>> (Intr) Weight Height Sho_Sz BMI
Wght:H Wg:S_S Hg:S_S Wg:BMI Hg:BMI S_S:BM Wg:H:S_S W:H:BM W:S_S: H:S_S:
>> Weight -0.895
>> Height -0.996 0.869
>> Shoe_Size -0.930 0.694 0.933
>> BMI -0.911 0.998 0.887 0.720
>> Weight:Height 0.894 -1.000 -0.867 -0.692 -0.997
>> Weight:Shoe_Size 0.898 -0.997 -0.873 -0.700 -0.999
0.995
>> Height:Shoe_Size 0.890 -0.612 -0.904 -0.991 -0.641
0.609 0.619
>> Weight:BMI 0.911 -0.976 -0.887 -0.715 -0.972
0.980 0.965 0.637
>> Height:BMI 0.900 -1.000 -0.875 -0.703 -0.999
0.999 0.999 0.622 0.973
>> Shoe_Size:BMI 0.912 -0.992 -0.889 -0.726 -0.997
0.988 0.998 0.649 0.958 0.995
>> Weight:Height:Shoe_Size -0.901 0.999 0.876 0.704 1.000
-0.997 -1.000 -0.623 -0.971 -1.000 -0.997
>> Weight:Height:BMI -0.908 0.978 0.886 0.704 0.974
-0.982 -0.968 -0.627 -0.999 -0.975 -0.961 0.973
>> Weight:Shoe_Size:BMI -0.949 0.941 0.928 0.818 0.940
-0.946 -0.927 -0.751 -0.980 -0.938 -0.924 0.935 0.974
>> Height:Shoe_Size:BMI -0.901 0.995 0.878 0.707 0.998
-0.992 -1.000 -0.627 -0.960 -0.997 -0.999 0.999 0.964 0.923
>> Weight:Height:Shoe_Size:BMI 0.952 -0.948 -0.933 -0.812 -0.947
0.953 0.935 0.747 0.985 0.946 0.932 -0.943 -0.980 -0.999 -0.931
>>
>> Standardized Within-Group Residuals:
>> Min Q1 Med Q3 Max
>> -2.03523736 -0.47889716 -0.02149143 0.41118126 2.20012158
>>
>> Number of Observations: 114
>> Number of Groups: 19
>>
>>
>> This time the p-value associated to Weight is not significant
anymore. Why? Which analysis should I trust?
>>
>>
>> In addition, while in the first output the field "value" (which
>> should give me the slope) is -99.036 in the second output it is
>> -13651237. Why they are so different? The one in the first
output is the
>> one that seems definitively more reasonable to me.
>> I would very grateful if someone could give me an answer
>>
>>
>> Thanks in advance
>>
>>
>> Angelo
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Michael
http://www.dewey.myzen.co.uk/home.html
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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