[R-sig-ME] lme posthoc constrasts for interaction effects group*time
Nicholas Lewin-Koh
nikko at hailmail.net
Fri Jun 18 18:27:17 CEST 2010
Hi,
Try
library(multcomp)
test<-glht(model,lin=mcp(group="Tukey"))
confint(test)
However, that just tests the marginal effect of group.
With interactions you want to test over time, probably a simultaneous
interval.
This will get you close:
library(contrast)
ctt<-contrast(model, a=list( day=c(0,3,7,21,49), group=c(Small Stroke",
"Large Stroke")),
b=list(day=c(0,3,7,21,49), group=c("Control", "Control")))
## Extract the contrast matrix
cmtrx <- ctt$X
## Fit the contrasts using glht
ttgl<-glht(model,lin=cmtrx)
confint(ttgl)
hope this helps.
And do read the vignette in multcomp :)
Nicholas
> Message: 2
> Date: Thu, 17 Jun 2010 14:51:21 +0200
> From: Chris Kleier <chris.kleier at gmail.com>
> To: R sig-mixed-models <r-sig-mixed-models at r-project.org>
> Subject: [R-sig-ME] lme posthoc constrasts for interaction effects
> group*time
> Message-ID:
> <AANLkTilMkK9HJZqXDZimDzKCswXElxw__dUVQpSnW8FQ at mail.gmail.com>
> Content-Type: text/plain
>
> Hi all,
>
> Our dataset is a repeated measures design including 3 groups (a)
> Controls,
> (b) Small stroke and (c) Large stroke. We want to know if the group
> behave
> different over time.
>
> We first fitted a lme with fixed = group * time (see below).
> Their was a significant interaction effect (see below as well; p < 0.05)
>
> In the next step we want to know which groups have significant
> interactions
> (Control - Stroke large, Control - Stroke small, Stroke small - Stroke
> large), and their p-values + CIs.
>
> A posthoc Tukey, I think is fine. However, I do not know how to setup the
> constrast matrix.
>
> These are the contrast names ( names( coef( model ) ) ):
> "(Intercept)", "groupStroke large", "groupStroke small", "time",
> "groupStroke large:time" "groupStroke small:time"
>
> I guess, the model has to be refitted to get a contrast for
> "groupControl:time" as well, but I do not know how.
>
> Any solution known? (Probabily something in the direction of: model.2 <-
> update( model, fixed = 0 + group * time ) )
>
> Thanks!
>
> Chris
>
>
>
>
>
> This is a print of the dataset:
>
> subject group value day
> 1 N03 Stroke small 0.47545500 0
> 2 N04 Stroke small 0.38866500 0
> 3 N19 Stroke small 0.64749800 0
> 4 N20 Stroke small 0.50468600 0
> 5 N22 Stroke small 0.55872300 0
> 6 N03 Stroke small 0.01979970 3
> 7 N04 Stroke small 0.05607250 3
> 8 N19 Stroke small -0.04506370 3
> 9 N20 Stroke small -0.33935400 3
> 10 N22 Stroke small 0.22052700 3
> 11 N03 Stroke small 0.29805300 7
> 12 N04 Stroke small -0.24908100 7
> 13 N19 Stroke small -0.02657290 7
> 14 N20 Stroke small -0.19483200 7
> 15 N22 Stroke small 0.50948000 7
> 16 N03 Stroke small 0.13056600 21
> 17 N04 Stroke small 0.33759500 21
> 18 N19 Stroke small 0.14929200 21
> 19 N20 Stroke small 0.25033100 21
> 20 N22 Stroke small 0.71456800 21
> 21 N03 Stroke small 0.39007100 49
> 22 N04 Stroke small 0.33254400 49
> 23 N19 Stroke small 0.27767600 49
> 24 N20 Stroke small 0.33467500 49
> 25 N22 Stroke small 0.52203500 49
> 26 N03 Stroke small 0.29851100 70
> 27 N04 Stroke small 0.50900200 70
> 28 N19 Stroke small 0.51781700 70
> 29 N20 Stroke small 0.69543700 70
> 30 N22 Stroke small 0.49337000 70
> 31 N01 Stroke large NA 0
> 32 N07 Stroke large 0.54930600 0
> 33 N09 Stroke large 0.59703600 0
> 34 N10 Stroke large 0.72751400 0
> 35 N17 Stroke large 0.63878400 0
> 36 N23 Stroke large 0.65749100 0
> 37 N24 Stroke large 0.60945900 0
> 38 N28 Stroke large NA 0
> 39 N01 Stroke large -0.31610900 3
> 40 N07 Stroke large -0.33615700 3
> 41 N09 Stroke large -0.24906400 3
> 42 N10 Stroke large 0.33868700 3
> 43 N17 Stroke large -0.31357300 3
> 44 N23 Stroke large -0.13162800 3
> 45 N24 Stroke large -0.45343700 3
> 46 N28 Stroke large -0.18206700 3
> 47 N01 Stroke large -0.05309070 7
> 48 N07 Stroke large -0.83318300 7
> 49 N09 Stroke large -0.49667100 7
> 50 N10 Stroke large -0.48083200 7
> 51 N17 Stroke large -0.28447000 7
> 52 N23 Stroke large 0.01842660 7
> 53 N24 Stroke large -0.50464400 7
> 54 N28 Stroke large 0.16456100 7
> 55 N01 Stroke large 0.24652600 21
> 56 N07 Stroke large -0.31566500 21
> 57 N09 Stroke large 0.39618900 21
> 58 N10 Stroke large -0.30702200 21
> 59 N17 Stroke large -0.28860100 21
> 60 N23 Stroke large -0.23851000 21
> 61 N24 Stroke large -0.00913697 21
> 62 N28 Stroke large 0.43462600 21
> 63 N01 Stroke large -0.20660100 49
> 64 N07 Stroke large -0.13044900 49
> 65 N09 Stroke large 0.24625000 49
> 66 N10 Stroke large -0.28206200 49
> 67 N17 Stroke large 0.29186400 49
> 68 N23 Stroke large -0.14246600 49
> 69 N24 Stroke large -0.11297700 49
> 70 N28 Stroke large 0.32027300 49
> 71 N01 Stroke large -0.00106427 70
> 72 N07 Stroke large -0.17264000 70
> 73 N09 Stroke large 0.26417500 70
> 74 N10 Stroke large -0.11202200 70
> 75 N17 Stroke large 0.16687700 70
> 76 N23 Stroke large -0.24308300 70
> 77 N24 Stroke large 0.00322026 70
> 78 N28 Stroke large 0.34928100 70
> 79 C01 Control NA 0
> 80 C02 Control NA 0
> 81 C03 Control NA 0
> 82 C04 Control NA 0
> 83 C05 Control NA 0
> 84 C06 Control NA 0
> 85 C07 Control NA 0
> 86 C09 Control NA 0
> 87 C10 Control NA 0
> 88 C32 Control NA 0
> 89 C01 Control 0.32387500 3
> 90 C02 Control 0.48334200 3
> 91 C03 Control 0.70698800 3
> 92 C04 Control 0.00572262 3
> 93 C05 Control 0.40416000 3
> 94 C06 Control 0.36343800 3
> 95 C07 Control 0.05064140 3
> 96 C09 Control 0.84621500 3
> 97 C10 Control 0.80675300 3
> 98 C32 Control NA 3
> 99 C01 Control 0.23174400 7
> 100 C02 Control 0.36959100 7
> 101 C03 Control 0.43762800 7
> 102 C04 Control 0.34187100 7
> 103 C05 Control 0.74288400 7
> 104 C06 Control 0.17692600 7
> 105 C07 Control 0.60067600 7
> 106 C09 Control 0.80314900 7
> 107 C10 Control 0.81695500 7
> 108 C32 Control 0.19029400 7
> 109 C01 Control 0.45062600 21
> 110 C02 Control 0.62884500 21
> 111 C03 Control 0.33693200 21
> 112 C04 Control 0.76098800 21
> 113 C05 Control 0.59837500 21
> 114 C06 Control 0.39328800 21
> 115 C07 Control 0.55099400 21
> 116 C09 Control 0.75411500 21
> 117 C10 Control 0.72495100 21
> 118 C32 Control 0.66503400 21
> 119 C01 Control 0.63903900 49
> 120 C02 Control NA 49
> 121 C03 Control 0.59655200 49
> 122 C04 Control 0.42958700 49
> 123 C05 Control 0.68408700 49
> 124 C06 Control 0.45482700 49
> 125 C07 Control 0.37217700 49
> 126 C09 Control 0.75770400 49
> 127 C10 Control 0.62059200 49
> 128 C32 Control 0.68320900 49
> 129 C01 Control 0.66179300 70
> 130 C02 Control 0.34183000 70
> 131 C03 Control 0.58543900 70
> 132 C04 Control 0.62621700 70
> 133 C05 Control 0.40610400 70
> 134 C06 Control 0.32596100 70
> 135 C07 Control 0.14847100 70
> 136 C09 Control 0.79177000 70
> 137 C10 Control 0.71970900 70
> 138 C32 Control 0.56702700 70
>
> This is our model:
>
> model <- lme( na.action = na.omit, data = data, fixed = value ~ group *
> time, random = ~1 | subject, correlation = corCAR1( form = ~time |
> subject )
> )
>
>
>
> anova( model )
>
> numDF denDF F-value p-value
> (Intercept) 1 87 59.08836 <.0001
> group 2 20 34.50706 <.0001
> time 1 87 19.45032 <.0001
> group:time 2 87 4.53603 0.0134
>
>
>
> intervals( model )
>
> Approximate 95% confidence intervals
>
> Fixed effects:
> lower est. upper
> (Intercept) 0.3677066515 0.4905816494 0.613456647
> groupStroke large -0.9027833601 -0.7106686760 -0.518553992
> groupStroke small -0.6705657199 -0.4490365094 -0.227507299
> time -0.0013629996 0.0009963115 0.003355623
> groupStroke large:time -0.0003754957 0.0031296769 0.006634850
> groupStroke small:time 0.0019261801 0.0059657552 0.010005330
> attr(,"label")
> [1] "Fixed effects:"
>
> Random Effects:
> Level: subject
> lower est. upper
> sd((Intercept)) 0.07377088 0.1227132 0.2041257
>
> Correlation structure:
> lower est. upper
> Phi 0.2035765 0.5971507 0.8957905
> attr(,"label")
> [1] "Correlation structure:"
>
> Within-group standard error:
> lower est. upper
> 0.1779472 0.2071400 0.2411220
>
> [[alternative HTML version deleted]]
>
>
>
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