# [R] Help Interpreting Linear Mixed Model

Joshua Dixon joshuamichaeldixon at gmail.com
Mon Apr 27 02:26:24 CEST 2015

```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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