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]]
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 at 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 at 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]]
Hello Thierry, No, this isn't homework. Not that young unfortunately. Josh> On 27 Apr 2015, at 08:06, Thierry Onkelinx <thierry.onkelinx at 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 <joshuamichaeldixon at 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 at 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]]