subject <- factor(rep(c(1,2,3,4,5,6),each=12))
dep <- c(0.3763244126,0.2185001692,0.4191841742,0.9812978664,0.7429273683,0.6254715486,0.6200958213,0.4693300191,0.1779032899,0.0035873980,0.8821949826,0.4818012617,0.0008013437,0.6280700732,0.7126500814,0.4984349359,0.2457996449,0.3085733312,0.5903398243,0.3704800352,0.8215325437,0.0445236221,0.1849731791,0.3670945817,0.0022268933,0.1630332691,0.9734050406,0.2638539758,0.8550054496,0.9413964085,0.4548943471,0.0440815873,0.5222098769,0.6553600784,0.6853486744,0.0571945074,0.0923124240,0.6976544929,0.9257440316,0.5658967043,0.0636543627,0.1038574059,0.0662497468,0.9165439918,0.5200087291,0.9528015053,0.5347318368,0.1848373057,0.9948602219,0.9633110918,0.1482162909,0.9000614029,0.0898618386,0.7975253051,0.8334557347,0.8629821099,0.0001795699,0.2488384889,0.6382902598,0.1103540359,0.2199716354,0.2737281912,0.5694398067,0.7940423761,0.4906677457,0.5191186895,0.4770589883,0.2823238128,0.2458788699,0.6363522802,0.0306954833,0.6979198116)
f1 <- factor(rep(c("Female","Male","Female","Male","Male","Female"), each=12))
f2 <- c(0.098788608,0.934606288,0.145045152,0.841969882,0.498234471,0.562897249,0.359740488,0.082046687,0.183987342,0.082418820,0.173424633,0.799291329,0.041450568,0.686708743,0.352092230,0.823550310,0.650857094,0.331705763,0.659111451,0.745187314,0.066165065,0.870759966,0.154977488,0.031703774,0.065251788,0.707452073,0.564604314,0.224798417,0.656363138,0.047954841,0.500513114,0.923316812,0.706629266,0.561530974,0.670860932,0.414969178,0.709973062,0.452946384,0.187624344,0.278656351,0.562138433,0.655193272,0.014868182,0.518697012,0.414113229,0.273464316,0.844080831,0.962636550,0.952739605,0.728627219,0.761122951,0.309977150,0.755239042,0.208627128,0.481429897,0.376021223,0.753871400,0.164842337,0.921081061,0.859677311,0.600462073,0.119193708,0.276722102,0.854752641,0.962710853,0.956277061,0.228313179,0.920405764,0.001594131,0.104930433,0.241548888,0.549643015)
f3 <- factor(rep(c("day1","day2","day3","day4"),each=3, times=6))
data <- data.frame(sub=subject, dep=dep, f1=f1, f2=f2, f3=f3)
m <- lmer(dep ~ f1*f2*f3 + (1|sub), data=data)
How can I extract the regression coefficients of the f1*f2*f3
interaction? Basically I'd like to plot this interaction with f2
on the x-axis and dep
on the y-axis.
For that I need the unique intercept for each combination of f1
and f3
(intercept for day1
and male
, intercept for day2
and female
, etc), but fixef(m)
gives me one single intercept. And how do I get the slope of the regression for each of these combinations?
fixef
, please bear with me. I thoughtfixef(m)
gives me only one intercept (the first element), and the rest are the slope values for specific contrasts? How would you extract the information about intercepts and slopes for each combination off1
andf3
from the output offixef
? $\endgroup$ – locus Oct 22 '18 at 23:36lm(dep ~ f1*f2*f3, data)
. That means you interpret the interaction in the same way you would interpret that of any ordinary least squares regression. $\endgroup$ – whuber♦ Oct 23 '18 at 1:31