R中一般线性混合模型的建立

xjreopfe  于 2023-03-05  发布在  其他
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我试图在R中构建一个glmm,但不断收到错误消息(我是一个完全的初学者)。
我用相机陷阱进行了一个实验,我在其中测试,如果他们对一个目标作出React,我拉在他们前面,所以我的响应变量是二项式。我试图建立一个GLMM,其中所有的变量是固定的因素和天(在其中进行实验是一个随机因素)。谁更有经验告诉我我做错了什么(我第一次尝试只与一个解释变量)?
我尝试了glmm()lmer()

library(glmm)
set.seed(1234)
ptm <- proc.time()
Detections <- glmm(Detection ~ 0 + Camera, random = list(~ 0 + Day), 
    varcomps.names = c("Day"), data = data1, family.glmm = bernoulli.glmm, 
    m = 10^4, debug = TRUE)`

即使使用最小的数据集,它也会产生一个非常大的glmm。

library(lme4)
Detections_glmm <- lmer(Detection ~ Camera + (1|Day), family="binomial")

此错误消息如下所示:

Error in lmer(Detection ~ Camera + (1 | Day), family = "binomial") : 
  unused argument (family = "binomial")

下面是一个最小df:

data.frame(
    Detection = c(1, 0, 0, 1, 1, 1, 1, 0, 0, 0),
             Temperature = as.factor(c("10","10","10","10","10","20","20",
                                       "0","0","0")),
                Distance = as.factor(c("75","75","75","225","225","225",
                                       "75","150","150","150")),
                    Size = as.factor(c("0","0","0","0","1","1","1","1",
                                       "2","2")),
                   Light = as.factor(c("1","1","1","1","1","0","0","0",
                                       "0","0")),
                  Camera = as.factor(c("1","1","2","2","2","3","3","3",
                                       "1","1")),
                     Day = as.factor(c("1","1","1","2","2","2","3","3",
                                       "3","2"))

以及变量的相关信息:
响应变量:
检测(二项式)
解释变量:
瓶温度:(0、10、20)
与摄像机的距离(75、150、225)
轻度(0/1)
瓶大小(0、1、3)

mpbci0fu

mpbci0fu1#

使用glmer应该可以。(lmer仅适用于线性混合模型,即高斯响应。

Detections_glmm <- glmer(Detection ~ Camera + (1|Day),    family="binomial")

如果您的数据集中确实只有3天,您可能希望将Day视为固定效应而非随机效应,例如,请参见GLMM常见问题解答或this CrossValidated question ...

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