我正在尝试使用 lmer 函数来研究 3 个不同条件 (cond=0, 1, 2) 和目标存在 (target=False 或 True) 之间的反应时间 (RT) 是否存在交互作用在患者（患者）中。
我写了以下等式：
lmer(RT~cond*target+(1|Patient))
我的问题是这个函数的默认截距是 cond = 0 和 target = False，而我希望截距是 cond= 0 和 target=True （为了看看 cond0*target 之间是否有显着差异） =True 且 cond1*target=True)。
我非常感谢您的帮助。
这是我的输出
stu3<-lmer(RT~cond*target+(1|Patient),
data=subset(ss, Groupe=="ugs" & primeable ==TRUE &
Correct==TRUE & NoPrac==TRUE))
pvals.fnc(stu3)
$fixed
Estimate MCMCmean HPD95lower HPD95upper pMCMC Pr(>|t|)
(Intercept) 0.5511 0.5513 0.5258 0.5807 0.0001 0.0000
cond1 0.0618 0.0619 0.0498 0.0741 0.0001 0.0000
cond2 0.0285 0.0285 0.0142 0.0438 0.0002 0.0001
targetFALSE 0.1389 0.1389 0.1239 0.1549 0.0001 0.0000
cond1:targetFALSE -0.0752 -0.0751 -0.0943 -0.0545 0.0001 0.0000
cond2:targetFALSE -0.0788 -0.0786 -0.0998 -0.0564 0.0001 0.0000
$random
Groups Name Std.Dev. MCMCmedian MCMCmean HPD95lower HPD95upper
1 Patient (Intercept) 0.0610 0.0583 0.0599 0.0425 0.0797
2 Residual 0.1674 0.1674 0.1674 0.1650 0.1699
根据我的数据，选择的截距是
cond0:targetTRUEcond1:targetFALSEcond2:targetFALSE如果我理解正确，您的模型已经在进行您希望在内部进行的解释
target==TRUE"(Intercept)" -> target==TRUE, cond==0 (even if model matrix contains all conds)
"cond1" -> target==TRUE, cond==1 on top of cond==0
"cond2" -> target==TRUE, cond==2 on top of cond==0
"targetFALSE" -> target==FALSE, cond==0 (even if model matrix contains all conds)
"cond1:targetFALSE" -> target==FALSE, cond==1 on top of cond==0
"cond2:targetFALSE" -> target==FALSE, cond==2 on top of cond==0
那么，是不是在
"(Intercept)""cond1""cond2"getME(stu3,'X')下面是我为测试您的案例而构建的示例数据。请注意，我构建了三种不同的响应：一种没有任何效果，一种仅具有
target==TRUEtarget==TRUEtarget==TRUEcondfit1fit2set.seed(0)
struct <- expand.grid(target = c(FALSE,TRUE), cond = as.factor(0:2), patient = LETTERS[1:20])
attach(struct)
ranpatient <- rep(rnorm(20), each=6)
rerror <- rnorm(120)
# Just random noise
response0 <- ranpatient + rerror
# When target==TRUE we increment the response by 1 and add errors
response1 <- 1*target + ranpatient + rerror
# When target==TRUE we increment the response by 1,
# to which we also add an interaction effect condition {0,1,2} * target {0,1}
# notice that numeric transformation of cond {0,1,2} transforms to ranks {1,2,3}
response2 <- 1*target + target*(as.numeric(cond)-1) + ranpatient + rerror
dat <- data.frame(cond, target, patient, response0, response1, response2)
detach(struct)
require(lme4)
fit0 <- lmer(response0 ~ cond*target + (1|patient), data=dat)
fit1 <- lmer(response1 ~ cond*target + (1|patient), data=dat)
fit2 <- lmer(response2 ~ cond*target + (1|patient), data=dat)
head(dat)
round(coef(summary(fit0)),2) # Notice low t values
round(coef(summary(fit1)),2) # High t value for targetTRUE
round(coef(summary(fit2)),2) # High t value for interaction cond0/1/2 with targetTRUE
# Notice how cond==1 adds 1, and cond==2 adds 2 in comparison to cond==0 when targetTRUE
# Notice also that coefficient "cond2:targetTRUE" is incremental to term "targetTRUE", not "cond1:targetTRUE"
head(getME(fit2,'X')) # Columns correspond to the fixed effect terms
带输出
> head(dat)
cond target patient response0 response1 response2
1 0 FALSE A 1.038686 1.038686 1.038686
2 0 TRUE A 1.640350 2.640350 2.640350
3 1 FALSE A 1.396291 1.396291 1.396291
4 1 TRUE A 2.067144 3.067144 4.067144
5 2 FALSE A 1.205848 1.205848 1.205848
6 2 TRUE A 1.766562 2.766562 4.766562
> round(coef(summary(fit0)),2) # Notice low t values
Estimate Std. Error t value
(Intercept) -0.13 0.31 -0.40
cond1 0.18 0.29 0.62
cond2 0.00 0.29 0.00
targetTRUE 0.00 0.29 -0.01
cond1:targetTRUE 0.13 0.41 0.32
cond2:targetTRUE 0.08 0.41 0.19
> round(coef(summary(fit1)),2) # High t value for targetTRUE
Estimate Std. Error t value
(Intercept) -0.13 0.31 -0.40
cond1 0.18 0.29 0.62
cond2 0.00 0.29 0.00
targetTRUE 1.00 0.29 3.42
cond1:targetTRUE 0.13 0.41 0.32
cond2:targetTRUE 0.08 0.41 0.19
> round(coef(summary(fit2)),2) # High t value for interaction cond0/1/2 with targetTRUE
Estimate Std. Error t value
(Intercept) -0.13 0.31 -0.40
cond1 0.18 0.29 0.62
cond2 0.00 0.29 0.00
targetTRUE 1.00 0.29 3.42
cond1:targetTRUE 1.13 0.41 2.75
cond2:targetTRUE 2.08 0.41 5.04
> # Notice how cond==1 adds 1, and cond==2 adds 2 in comparison to cond==0 when targetTRUE
> # Notice also that coefficient "cond2:targetTRUE" is incremental to term "targetTRUE", not "cond1:targetTRUE"
> head(getME(fit2,'X')) # Columns correspond to the fixed effect terms
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 0 0 0 0 0
[2,] 1 0 0 1 0 0
[3,] 1 1 0 0 0 0
[4,] 1 1 0 1 1 0
[5,] 1 0 1 0 0 0
[6,] 1 0 1 1 0 1
看看标准的“因素管理”是否有效：
target=factor(target, levels=c("TRUE", "FALSE")
lmer(RT~cond*target+(1|Patient))
（我会使用短语“更改参考水平”而不是“更改截距”，但我认为这实际上是相同的过程。我怀疑短语“[r]更改参考水平”会给你带来相当多的点击在 Google 或 SO 搜索上。）