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: joshuamichaeldi...@gmail.com
Sent: Mon, 27 Apr 2015 08:54:51 +0100
To: thierry.onkel...@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.onkel...@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 <joshuamichaeldi...@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)
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