Specifying random effect nested under an interaction of fixed effects
Probably an easy one.
I have data with fixed and random effects I'd like to fit a mixed effects model to:
set.seed(1)
df <- data.frame(group = c(rep("A",40),rep("B",40)),
treatment = rep(c(rep("T",20),rep("CT",20)),2),
class = c(rep("AT1",10),rep("ACT1",10),rep("AT2",10),rep("ACT2",10),rep("BT1",10),rep("BCT1",10),rep("BT2",10),rep("BCT2",10)),
value = rnorm(80),
stringsAsFactors = F)
df$group <- factor(df$group, levels = c("A","B"))
df$treatment <- factor(df$treatment, levels = c("CT","T"))
The fixed effects are group and treatment and the random effect is class, which to my understanding is nested within the group and treatment combinations.
The model I want to fit is:
value ~ group*treatment
Where the effect of interest if the group:treatment interaction.
Of course I want to account for class as a random effect, but I can't seem to find what the syntax for that is. I tried:(1|group*treatment/class) and (1|group:treatment/class) but both give an error.
Defining a group:treatment column in df:
df <- df %>% dplyr::mutate(group_treatment = paste0(group,"_",treatment))
And fitting:
fit <- lmer(value ~ group*treatment + (1|group_treatment/class), data = df)
Does seem to work, but I'm wondering if that's the only way or whether there's a more explicit syntax for such cases of random effect nesting.
Any idea?
nested lme4 mixed-models random-effects
asked Nov 11 at 8:48
dan
1,42941043
add a comment |
Probably an easy one.
I have data with fixed and random effects I'd like to fit a mixed effects model to:
set.seed(1)
df <- data.frame(group = c(rep("A",40),rep("B",40)),
treatment = rep(c(rep("T",20),rep("CT",20)),2),
class = c(rep("AT1",10),rep("ACT1",10),rep("AT2",10),rep("ACT2",10),rep("BT1",10),rep("BCT1",10),rep("BT2",10),rep("BCT2",10)),
value = rnorm(80),
stringsAsFactors = F)
df$group <- factor(df$group, levels = c("A","B"))
df$treatment <- factor(df$treatment, levels = c("CT","T"))
The fixed effects are group and treatment and the random effect is class, which to my understanding is nested within the group and treatment combinations.
The model I want to fit is:
value ~ group*treatment
Where the effect of interest if the group:treatment interaction.
Of course I want to account for class as a random effect, but I can't seem to find what the syntax for that is. I tried:(1|group*treatment/class) and (1|group:treatment/class) but both give an error.
Defining a group:treatment column in df:
df <- df %>% dplyr::mutate(group_treatment = paste0(group,"_",treatment))
And fitting:
fit <- lmer(value ~ group*treatment + (1|group_treatment/class), data = df)
Does seem to work, but I'm wondering if that's the only way or whether there's a more explicit syntax for such cases of random effect nesting.
Any idea?
nested lme4 mixed-models random-effects
asked Nov 11 at 8:48
dan
1,42941043
1
As far as I know, a fixed effect predictor can't / shouldn't be used at the same time as random intercept. However, if you think that the effect ofgroupon your outcome varies depending onclass, than you can specifygroupand/ortreatmentas random slopes:lmer(value ~ group*treatment + (1 + group*treatment | class), data = df)
– Daniel
Nov 23 at 14:26
add a comment |
Probably an easy one.
I have data with fixed and random effects I'd like to fit a mixed effects model to:
set.seed(1)
df <- data.frame(group = c(rep("A",40),rep("B",40)),
treatment = rep(c(rep("T",20),rep("CT",20)),2),
class = c(rep("AT1",10),rep("ACT1",10),rep("AT2",10),rep("ACT2",10),rep("BT1",10),rep("BCT1",10),rep("BT2",10),rep("BCT2",10)),
value = rnorm(80),
stringsAsFactors = F)
df$group <- factor(df$group, levels = c("A","B"))
df$treatment <- factor(df$treatment, levels = c("CT","T"))
The fixed effects are group and treatment and the random effect is class, which to my understanding is nested within the group and treatment combinations.
The model I want to fit is:
value ~ group*treatment
Where the effect of interest if the group:treatment interaction.
Of course I want to account for class as a random effect, but I can't seem to find what the syntax for that is. I tried:(1|group*treatment/class) and (1|group:treatment/class) but both give an error.
Defining a group:treatment column in df:
df <- df %>% dplyr::mutate(group_treatment = paste0(group,"_",treatment))
And fitting:
fit <- lmer(value ~ group*treatment + (1|group_treatment/class), data = df)
Does seem to work, but I'm wondering if that's the only way or whether there's a more explicit syntax for such cases of random effect nesting.
Any idea?
nested lme4 mixed-models random-effects
asked Nov 11 at 8:48
dan
1,42941043
Probably an easy one.
I have data with fixed and random effects I'd like to fit a mixed effects model to:
set.seed(1)
df <- data.frame(group = c(rep("A",40),rep("B",40)),
treatment = rep(c(rep("T",20),rep("CT",20)),2),
class = c(rep("AT1",10),rep("ACT1",10),rep("AT2",10),rep("ACT2",10),rep("BT1",10),rep("BCT1",10),rep("BT2",10),rep("BCT2",10)),
value = rnorm(80),
stringsAsFactors = F)
df$group <- factor(df$group, levels = c("A","B"))
df$treatment <- factor(df$treatment, levels = c("CT","T"))
The fixed effects are group and treatment and the random effect is class, which to my understanding is nested within the group and treatment combinations.
The model I want to fit is:
value ~ group*treatment
Where the effect of interest if the group:treatment interaction.
Of course I want to account for class as a random effect, but I can't seem to find what the syntax for that is. I tried:(1|group*treatment/class) and (1|group:treatment/class) but both give an error.
Defining a group:treatment column in df:
df <- df %>% dplyr::mutate(group_treatment = paste0(group,"_",treatment))
And fitting:
fit <- lmer(value ~ group*treatment + (1|group_treatment/class), data = df)
Does seem to work, but I'm wondering if that's the only way or whether there's a more explicit syntax for such cases of random effect nesting.
Any idea?
nested lme4 mixed-models random-effects
nested lme4 mixed-models random-effects
asked Nov 11 at 8:48
dan
1,42941043
asked Nov 11 at 8:48
dan
1,42941043
edited Nov 11 at 9:09
asked Nov 11 at 8:48
dan
1,42941043
asked Nov 11 at 8:48
dan
1,42941043
asked Nov 11 at 8:48
dan
1,42941043
1,42941043
1
As far as I know, a fixed effect predictor can't / shouldn't be used at the same time as random intercept. However, if you think that the effect ofgroupon your outcome varies depending onclass, than you can specifygroupand/ortreatmentas random slopes:lmer(value ~ group*treatment + (1 + group*treatment | class), data = df)
– Daniel
Nov 23 at 14:26
add a comment |
1
As far as I know, a fixed effect predictor can't / shouldn't be used at the same time as random intercept. However, if you think that the effect ofgroupon your outcome varies depending onclass, than you can specifygroupand/ortreatmentas random slopes:lmer(value ~ group*treatment + (1 + group*treatment | class), data = df)
– Daniel
Nov 23 at 14:26
1
1
As far as I know, a fixed effect predictor can't / shouldn't be used at the same time as random intercept. However, if you think that the effect of
group on your outcome varies depending on class, than you can specify group and/or treatment as random slopes: lmer(value ~ group*treatment + (1 + group*treatment | class), data = df)– Daniel
Nov 23 at 14:26
As far as I know, a fixed effect predictor can't / shouldn't be used at the same time as random intercept. However, if you think that the effect of
group on your outcome varies depending on class, than you can specify group and/or treatment as random slopes: lmer(value ~ group*treatment + (1 + group*treatment | class), data = df)– Daniel
Nov 23 at 14:26
add a comment |
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1
As far as I know, a fixed effect predictor can't / shouldn't be used at the same time as random intercept. However, if you think that the effect of
groupon your outcome varies depending onclass, than you can specifygroupand/ortreatmentas random slopes:lmer(value ~ group*treatment + (1 + group*treatment | class), data = df)– Daniel
Nov 23 at 14:26