[R] Help Interpreting Linear Mixed Model

[Thierry Onkelinx]

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 op 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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>
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