[R] Help Interpreting Linear Mixed Model
Joshua Dixon
joshuamichaeldixon at gmail.com
Tue Apr 28 22:25:15 CEST 2015
*John* - Lot's of missing data for height unfortunately. Which is needed
for BMI calculation.
How would I look compare very specific parts of the data, i.e. comparing
YoYo outcomes between "F" and "M" position that are both in the
PREMIER_LEAGUE Level?
Still can't figure it out!
Josh
On Tue, Apr 28, 2015 at 2:39 AM, John Kane <jrkrideau at inbox.com> wrote:
>
> Looks great. How come so many NA's in Height and BMI? Just no data
> available?
>
> str(dat1)
> 'data.frame': 100 obs. of 8 variables:
> $ Id : int 7451 148 10393 10200 1961 10428 10541 10012 9895 10626
> ...
> $ Level : Factor w/ 5 levels "CHAMPIONSHIP",..: 1 1 1 1 1 1 1 1 1 1 ...
> $ AgeGr : int 14 16 10 10 13 10 10 10 10 10 ...
> $ Position: Factor w/ 4 levels "D","F","GK","M": 4 1 1 2 3 3 2 3 1 1 ...
> $ Height : int NA NA NA NA NA NA NA NA NA NA ...
> $ Weight : num 63 64 36 46 67 40 25 30 36 33 ...
> $ BMI : num NA NA NA NA NA NA NA NA NA NA ...
> $ YoYo : int 80 80 160 160 160 160 160 160 160 160 ...
>
> John Kane
> Kingston ON Canada
>
> -----Original Message-----
> From: joshuamichaeldixon at gmail.com
> Sent: Mon, 27 Apr 2015 23:35:13 +0100
> To: jrkrideau at inbox.com
> Subject: Re: [R] Help Interpreting Linear Mixed Model
>
> Thanks John!
>
> This ok?
>
> > dput(head(data, 100))
>
> structure(list(Id = c(7451L, 148L, 10393L, 10200L, 1961L, 10428L,
>
> 10541L, 10012L, 9895L, 10626L, 1151L, 8775L, 10083L, 6217L, 90L,
>
> 10168L, 10291L, 8549L, 3451L, 10003L, 5907L, 10136L, 6182L, 6315L,
>
> 10015L, 9956L, 2040L, 4710L, 10747L, 6787L, 1222L, 10757L, 2892L,
>
> 117L, 10328L, 10503L, 768L, 2979L, 1961L, 10520L, 10498L, 3018L,
>
> 10335L, 2448L, 9027L, 362L, 8499L, 10603L, 9489L, 2124L, 707L,
>
> 8501L, 4908L, 9905L, 3000L, 2819L, 9973L, 10550L, 9921L, 10639L,
>
> 8771L, 10121L, 32L, 9935L, 9299L, 3246L, 682L, 10325L, 6741L,
>
> 3295L, 5270L, 727L, 8500L, 50L, 4705L, 3018L, 787L, 2953L, 1391L,
>
> 3682L, 7974L, 5023L, 652L, 727L, 679L, 10212L, 9488L, 9987L,
>
> 10039L, 5025L, 250L, 2539L, 787L, 3000L, 1151L, 8946L, 6177L,
>
> 3296L, 250L, 498L), Level = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
>
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label =
> c("CHAMPIONSHIP",
>
> "CONFERENCE", "LEAGUE_ONE", "LEAGUE_TWO", "PREMIER_LEAGUE"), class =
> "factor"),
>
> AgeGr = c(14L, 16L, 10L, 10L, 13L, 10L, 10L, 10L, 10L, 10L,
>
> 14L, 10L, 10L, 10L, 12L, 10L, 10L, 12L, 10L, 10L, 10L, 10L,
>
> 12L, 10L, 10L, 10L, 10L, 10L, 10L, 15L, 10L, 10L, 10L, 12L,
>
> 10L, 10L, 13L, 10L, 13L, 11L, 11L, 13L, 12L, 11L, 12L, 14L,
>
> 13L, 13L, 13L, 13L, 12L, 11L, 15L, 11L, 14L, 13L, 11L, 11L,
>
> 11L, 12L, 14L, 12L, 13L, 11L, 13L, 15L, 11L, 13L, 13L, 13L,
>
> 14L, 13L, 13L, 12L, 13L, 13L, 13L, 14L, 12L, 14L, 13L, 13L,
>
> 13L, 13L, 13L, 12L, 13L, 14L, 13L, 14L, 13L, 14L, 13L, 14L,
>
> 14L, 13L, 14L, 13L, 13L, 13L), Position = structure(c(4L,
>
> 1L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 4L, 3L, 2L, 3L,
>
> 4L, 3L, 4L, 2L, 4L, 2L, 3L, 1L, 1L, 2L, 4L, 4L, 2L, 4L, 4L,
>
> 2L, 1L, 4L, 1L, 1L, 2L, 4L, 3L, 1L, 4L, 1L, 2L, 3L, 3L, 1L,
>
> 1L, 3L, 1L, 3L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 1L,
>
> 2L, 2L, 2L, 4L, 4L, 2L, 4L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 1L,
>
> 2L, 2L, 4L, 1L, 1L, 1L, 2L, 4L, 1L, 3L, 4L, 4L, 4L, 4L, 2L,
>
> 2L, 2L, 1L, 1L, 4L, 1L, 4L, 2L, 2L), .Label = c("D", "F",
>
> "GK", "M"), class = "factor"), Height = c(NA, NA, NA, NA,
>
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 151L, NA,
>
> 154L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 156L, NA,
>
> 147L, NA, NA, NA, NA, NA, 138L, 172L, NA, NA, 150L, NA, NA,
>
> NA, NA, NA, NA, NA, 140L, 153L, NA, NA, NA, NA, NA, NA, NA,
>
> 158L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 156L), Weight = c(63,
>
> 64, 36, 46, 67, 40, 25, 30, 36, 33, 61, 31, 29, 34, 47, 38,
>
> 32, 44, 32, 32, 30, 34, 51, 34, 28, 27, 33, 31, 28, 44, 37,
>
> 46, 26, 42, 32, 32, 43, 31, 72, 27, 30, 55, 53, 50, 51, 55,
>
> 48.6, 49, 48, 64, 35, 32, 55, 32, 50, 61, 42, 33, 37, 45,
>
> 45, 50, 36, 33, 49, 59, 42, 43, 35.1, 66.9, 52, 47, 40, 38,
>
> 45, 53, 44, 54, 39, 62, 33, 53.8, 42, 46, 39, 48, 39, 54,
>
> 40, 42.4, 50, 48, 46, 52, 58, 40, 46, 51, 54, 42), BMI = c(NA,
>
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>
> 21.2, NA, 20.24, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>
> NA, 18.49, NA, 16.66, NA, NA, NA, NA, NA, 18.57, 22.61, NA,
>
> NA, 17.77, NA, NA, NA, NA, NA, NA, NA, 16.84, 22.86, NA,
>
> NA, NA, NA, NA, NA, NA, 16.9, NA, NA, NA, NA, NA, NA, NA,
>
> NA, NA, 17.26), YoYo = c(80L, 80L, 160L, 160L, 160L, 160L,
>
> 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L,
>
> 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L,
>
> 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L,
>
> 160L, 160L, 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L,
>
> 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L,
>
> 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L,
>
> 200L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
>
> 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
>
> 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
>
> 240L, 240L, 240L, 240L)), .Names = c("Id", "Level", "AgeGr",
>
> "Position", "Height", "Weight", "BMI", "YoYo"), row.names = c(NA,
>
> 100L), class = "data.frame")
>
> On Mon, Apr 27, 2015 at 10:43 PM, John Kane <jrkrideau at inbox.com> wrote:
>
> Hi Josh,
>
> Just a sample is usually fine. As long as it cover a representative
> (must be time for dinner---I was going to type reprehensibe) sample of the
> data then something like dput(head(mydata, 100) ) works well.
>
> Kingston ON Canada
>
> -----Original Message-----
> From: joshuamichaeldixon at gmail.com
>
> Sent: Mon, 27 Apr 2015 21:30:39 +0100
> To: lists at dewey.myzen.co.uk
> Subject: Re: [R] Help Interpreting Linear Mixed Model
>
> Apologies for my ignorance!
>
> Thierry - thank you for the reading. I'll look into those ASAP!
>
> John - The data set I have is quite large, when using the dput() command
> I'm unsure if it actually fits the whole output into the console. I can't
> scroll up far enough to see the actual command. I can paste what is there
> if that may help? The bottom line:
>
> Names = c("Id", "Level", "AgeGr", "Position", "Height", "Weight", "BMI",
> "YoYo"), class = "data.frame", row.names = c(NA, -9689L))
>
> Michael - Essentially, I'm looking for differences between "YoYo" outcome
> for "Positions", "Levels" and accounting for repeated measures using "Id"
> as a random factor. So I was able to figure out points 2 and 3.
>
> I've searched for definitions of "Scaled residuals", "Random
> effects", "Fixed effects", "Correlation of Fixed Effects". However, I'm
> confused at the different interpretations I've found. Or quite possibly,
> I'm just confused... What should I be looking out for in these variables?
>
> I've tried to take my analysis smaller, and just look at specifics, to
> make it simpler. Such as, comparing YoYo (outcome score) for a
> Premier_League (Level), 22 (AgeGr) F (Position) with a Premier_League
> (Level), 22 (AgeGr) M (Position). How do I convert these into a factors
> for analysis?
>
> Simple question maybe, but it's not when you can't find the answer!
>
> Thank you,
>
> Josh
>
> On Mon, Apr 27, 2015 at 4:10 PM, Michael Dewey <lists at dewey.myzen.co.uk>
> wrote:
>
> Dear Joshua
>
> It would also help if you told us what your scientific question was. At
> the moment we know what R commands you used and have seen the head of your
> dataset but not why you are doing it.
>
> I would summarise what you have given us as
>
> 1 - most ID only occur once
> 2 - goal keepers do worse than outfield players
> 3 - older people (presumably in fact age is in years as a continuous
> variable) do better
>
> On 27/04/2015 12:42, John Kane wrote:
>
> John Kane
> Kingston ON Canada
>
> -----Original Message-----
> From: joshuamichaeldixon at gmail.com
> Sent: Mon, 27 Apr 2015 08:54:51 +0100
> To: thierry.onkelinx at inbo.be
> Subject: Re: [R] Help Interpreting Linear Mixed Model
>
> Hello Thierry,
>
> No, this isn't homework. Not that young unfortunately.
>
> A few years ago a friend of mine and her daughter were neck-in-neck on
> who got their Ph.D first. What's this "not that young" business?
>
> BTW, a better way to supply sample data is to use the dput() command.
>
> Do a dput(mydata), copy the results into the email and you have supplied
> us with an exact copy of your data.
>
> It is possible for many reasons that I will not read in your data, as
> you supplied it, in the format you have it in. This can lead to real
> confusion.
>
> 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)
>
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>
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