[R] Help Interpreting Linear Mixed Model
Joshua Dixon
joshuamichaeldixon at gmail.com
Mon Apr 27 22:30:39 CEST 2015
Apologies for my ignorance!
*Thierry* - thank you for the reading. I'll look into those ASAP!
*John* - The data set I have is quite large, when using the dput() command
I'm unsure if it actually fits the whole output into the console. I can't
scroll up far enough to see the actual command. I can paste what is there
if that may help? The bottom line:
Names = c("Id", "Level", "AgeGr", "Position", "Height", "Weight", "BMI",
"YoYo"), class = "data.frame", row.names = c(NA, -9689L))
*Michael *- Essentially, I'm looking for differences between "YoYo" outcome
for "Positions", "Levels" and accounting for repeated measures using "Id"
as a random factor. So I was able to figure out points 2 and 3.
I've searched for definitions of "Scaled residuals", "Random effects", "Fixed
effects", "Correlation of Fixed Effects". However, I'm confused at the
different interpretations I've found. Or quite possibly, I'm just
confused... What should I be looking out for in these variables?
I've tried to take my analysis smaller, and just look at specifics, to make
it simpler. Such as, comparing YoYo (outcome score) for a Premier_League
(Level), 22 (AgeGr) F (Position) with a Premier_League (Level), 22 (AgeGr)
M (Position). How do I convert these into a factors for analysis?
Simple question maybe, but it's not when you can't find the answer!
Thank you,
Josh
On Mon, Apr 27, 2015 at 4:10 PM, Michael Dewey <lists at dewey.myzen.co.uk>
wrote:
> 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]]
>>>>>
>>>>> ______________________________________________
>>>>> 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!
>>
>> ______________________________________________
>> 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.
>>
>>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>
[[alternative HTML version deleted]]
More information about the R-help
mailing list