[R] Help Interpreting Linear Mixed Model

Mon Apr 27 13:42:36 CEST 2015

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]]
>>> 
>>> ______________________________________________
>>> 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.
>> 
> 
> 	[[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.

____________________________________________________________
FREE ONLINE PHOTOSHARING - Share your photos online with your friends and family!
Visit http://www.inbox.com/photosharing to find out more!