Anova.III.lm中的误差（mod，Error，singular.ok=singular.ok，…）：模型中存在混叠系数

在我的实验中，我有三个自变量：试验类型、性别和游戏经历（所有这些都是分类的）

我有一个因变量：正确试验的比例（连续）

当我尝试进行三因素方差分析时，假设没有得到满足，因此我使用了对齐的秩变换方差分析

m1 <- art(Proportioncorrect ~ Videogamefrequency + Biologicalsex + + Trialtype + Videogamefrequency:Biologicalsex + Videogamefrequency:Trialtype + Biologicalsex:Trialtype + Biologicalsex:Trialtype:Videogamefrequency, data = Gaming)

谁能帮我一把吗

我的数据如下：

structure(list(ID = c("P_200214123342", "P_200224092247", "P_200219163622", 
"P_200220130332", "P_200219091823", "P_200225184226", "P_200219123120", 
"P_200219175102", "P_200214103155", "P_200219111605", "P_200217101213", 
"P_200219102411", "P_200221101028", "P_200220145557", "P_200225171612", 
"P_200224092247", "P_200219163622", "P_200220130332", "P_200214123342", 
"P_200219091823", "P_200225184226", "P_200219123120", "P_200219175102", 
"P_200214103155", "P_200219111605", "P_200217101213", "P_200219102411", 
"P_200221101028", "P_200220145557", "P_200225171612"), Trialtype = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Beaconed", 
"Probe"), class = "factor"), Proportioncorrect = c(0.729727660699102, 
1.33933990048532, 0.729727660699102, 1.075862200454, 0.578378233982015, 
1.16808048521424, 1.33933990048532, 1.13531397797248, 1.28700221758657, 
1.13531397797248, 1.28700221758657, 1.13531397797248, 1.28700221758657, 
1.28700221758657, 1.20358829695229, 0.297711691252463, 0.160690652951911, 
0.147197653346961, 0.0667161517509908, 0.080085580033659, 0.160690652951911, 
0.133731586046578, 0.214985569478799, 0.160690652951911, 0.269932799291976, 
0.339836905918588, 0.242365851038963, 0.214985569478799, 0.677268408841807, 
1.20358829695229), Videogamefrequency = structure(c(2L, 1L, 1L, 
1L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L), .Label = c("Monthly", 
"Never", "Weekly", "Yearly"), class = "factor"), Biologicalsex = structure(c(1L, 
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L), .Label = c("Female", 
"Male"), class = "factor")), row.names = c(NA, -30L), class = "data.frame")

---

我也有同样的问题，很想听听你是否找到了解决办法

structure(list(ID = c("P_200214123342", "P_200224092247", "P_200219163622", 
"P_200220130332", "P_200219091823", "P_200225184226", "P_200219123120", 
"P_200219175102", "P_200214103155", "P_200219111605", "P_200217101213", 
"P_200219102411", "P_200221101028", "P_200220145557", "P_200225171612", 
"P_200224092247", "P_200219163622", "P_200220130332", "P_200214123342", 
"P_200219091823", "P_200225184226", "P_200219123120", "P_200219175102", 
"P_200214103155", "P_200219111605", "P_200217101213", "P_200219102411", 
"P_200221101028", "P_200220145557", "P_200225171612"), Trialtype = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Beaconed", 
"Probe"), class = "factor"), Proportioncorrect = c(0.729727660699102, 
1.33933990048532, 0.729727660699102, 1.075862200454, 0.578378233982015, 
1.16808048521424, 1.33933990048532, 1.13531397797248, 1.28700221758657, 
1.13531397797248, 1.28700221758657, 1.13531397797248, 1.28700221758657, 
1.28700221758657, 1.20358829695229, 0.297711691252463, 0.160690652951911, 
0.147197653346961, 0.0667161517509908, 0.080085580033659, 0.160690652951911, 
0.133731586046578, 0.214985569478799, 0.160690652951911, 0.269932799291976, 
0.339836905918588, 0.242365851038963, 0.214985569478799, 0.677268408841807, 
1.20358829695229), Videogamefrequency = structure(c(2L, 1L, 1L, 
1L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L), .Label = c("Monthly", 
"Never", "Weekly", "Yearly"), class = "factor"), Biologicalsex = structure(c(1L, 
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L), .Label = c("Female", 
"Male"), class = "factor")), row.names = c(NA, -30L), class = "data.frame")