[R] Help Interpreting Linear Mixed Model

Michael Dewey lists at dewey.myzen.co.uk
Mon Apr 27 17:10:15 CEST 2015 

Dear Joshua

It would also help if you told us what your scientific question was. At 
the moment we know what R commands you used and have seen the head of 
your dataset but not why you are doing it.

I would summarise what you have given us as

1 - most ID only occur once
2 - goal keepers do worse than outfield players
3 - older people (presumably in fact age is in years as a continuous 
variable) do better

On 27/04/2015 12:42, John Kane wrote:
>
>
> John Kane
> Kingston ON Canada
>
>
>> -----Original Message-----
>> From: joshuamichaeldixon at gmail.com
>> Sent: Mon, 27 Apr 2015 08:54:51 +0100
>> To: thierry.onkelinx at inbo.be
>> Subject: Re: [R] Help Interpreting Linear Mixed Model
>>
>> Hello Thierry,
>>
>> No, this isn't homework. Not that young unfortunately.
>>
>
> A few years ago a friend of mine and her daughter were neck-in-neck on who got their Ph.D first. What's this "not that young" business?
>
> BTW, a better way to supply sample data is to use the dput() command.
>
> Do a dput(mydata), copy the results into the email and you have supplied us with an exact copy of your data.
>
> It is possible for many reasons that I will not read in your data, as you supplied it, in the format you have it in.  This can lead to real confusion.
>
>
>
>
>
>> Josh
>>
>>> On 27 Apr 2015, at 08:06, Thierry Onkelinx <thierry.onkelinx at inbo.be>
>>> wrote:
>>>
>>> Dear Josh,
>>>
>>> Is this homework? Because the list has a no homework policy.
>>>
>>> Best regards,
>>>
>>> ir. Thierry Onkelinx
>>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>>> and Forest
>>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>>> Kliniekstraat 25
>>> 1070 Anderlecht
>>> Belgium
>>>
>>> To call in the statistician after the experiment is done may be no more
>>> than asking him to perform a post-mortem examination: he may be able to
>>> say what the experiment died of. ~ Sir Ronald Aylmer Fisher
>>> The plural of anecdote is not data. ~ Roger Brinner
>>> The combination of some data and an aching desire for an answer does not
>>> ensure that a reasonable answer can be extracted from a given body of
>>> data. ~ John Tukey
>>>
>>> 2015-04-27 2:26 GMT+02:00 Joshua Dixon <joshuamichaeldixon at gmail.com>:
>>>> Hello!
>>>>
>>>> Very new to R (10 days), and I've run the linear mixed model, below.
>>>> Attempting to interpret what it means...  What do I need to look for?
>>>> Residuals, correlations of fixed effects?!
>>>>
>>>> How would I look at very specific interactions, such as PREMIER_LEAGUE
>>>> (Level) 18 (AgeGr) GK (Position) mean difference to CHAMPIONSHIP 18
>>>> GK?
>>>>
>>>> For reference my data set looks like this:
>>>>
>>>> Id Level AgeGr   Position Height Weight BMI YoYo
>>>> 7451 CHAMPIONSHIP 14 M NA 63 NA 80
>>>> 148 PREMIER_LEAGUE 16 D NA 64 NA 80
>>>> 10393 CONFERENCE 10 D NA 36 NA 160
>>>> 10200 CHAMPIONSHIP 10 F NA 46 NA 160
>>>> 1961 LEAGUE_TWO 13 GK NA 67 NA 160
>>>> 10428 CHAMPIONSHIP 10 GK NA 40 NA 160
>>>> 10541 LEAGUE_ONE 10 F NA 25 NA 160
>>>> 10012 CHAMPIONSHIP 10 GK NA 30 NA 160
>>>> 9895 CHAMPIONSHIP 10 D NA 36 NA 160
>>>>
>>>>
>>>> Many thanks in advance for time and help.  Really appreciate it.
>>>>
>>>> Josh
>>>>
>>>>
>>>>> summary(lmer(YoYo~AgeGr+Position+(1|Id)))
>>>> Linear mixed model fit by REML ['lmerMod']
>>>> Formula: YoYo ~ AgeGr + Position + (1 | Id)
>>>>
>>>> REML criterion at convergence: 125712.2
>>>>
>>>> Scaled residuals:
>>>>      Min      1Q  Median      3Q     Max
>>>> -3.4407 -0.5288 -0.0874  0.4531  4.8242
>>>>
>>>> Random effects:
>>>>   Groups   Name        Variance Std.Dev.
>>>>   Id       (Intercept) 15300    123.7
>>>>   Residual             16530    128.6
>>>> Number of obs: 9609, groups:  Id, 6071
>>>>
>>>> Fixed effects:
>>>>               Estimate Std. Error t value
>>>> (Intercept) -521.6985    16.8392  -30.98
>>>> AgeGr         62.6786     0.9783   64.07
>>>> PositionD    139.4682     7.8568   17.75
>>>> PositionM    141.2227     7.7072   18.32
>>>> PositionF    135.1241     8.1911   16.50
>>>>
>>>> Correlation of Fixed Effects:
>>>>            (Intr) AgeGr  PostnD PostnM
>>>> AgeGr     -0.910
>>>> PositionD -0.359 -0.009
>>>> PositionM -0.375  0.001  0.810
>>>> PositionF -0.349 -0.003  0.756  0.782
>>>>> model=lmer(YoYo~AgeGr+Position+(1|Id))
>>>>> summary(glht(model,linfct=mcp(Position="Tukey")))
>>>>
>>>>   Simultaneous Tests for General Linear Hypotheses
>>>>
>>>> Multiple Comparisons of Means: Tukey Contrasts
>>>>
>>>>
>>>> Fit: lmer(formula = YoYo ~ AgeGr + Position + (1 | Id))
>>>>
>>>> Linear Hypotheses:
>>>>              Estimate Std. Error z value Pr(>|z|)
>>>> D - GK == 0  139.468      7.857  17.751   <1e-04 ***
>>>> M - GK == 0  141.223      7.707  18.323   <1e-04 ***
>>>> F - GK == 0  135.124      8.191  16.496   <1e-04 ***
>>>> M - D == 0     1.754      4.799   0.366    0.983
>>>> F - D == 0    -4.344      5.616  -0.774    0.862
>>>> F - M == 0    -6.099      5.267  -1.158    0.645
>>>> ---
>>>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>>> (Adjusted p values reported -- single-step method)
>>>>
>>>>          [[alternative HTML version deleted]]
>>>>
>>>> ______________________________________________
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>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> 	[[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at 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.
>
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html

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