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@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.
