Hello Josh, One is never too old to study ;-)
Your question seems quite broad. You might be better off to read some books on mixed models (e.g. Pinheiro & Bates (2000) or Zuur et al (2009)) or try to find a local statistician. Email is not a suitable medium to teach statistics. Note that r-sig-mixed-models is a more suitable list for _specific_ questions on mixed models. 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 9:54 GMT+02:00 Joshua Dixon <joshuamichaeldi...@gmail.com>: > Hello Thierry, > > No, this isn't homework. Not that young unfortunately. > > 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) >> >> [[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. > > > [[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.
