R中两个细胞组差异分析中差异的统计显着性

Question

我正在尝试测试前波和后波期间控制和暴露的 2 个细胞之间的差异。

1 - 以下是电子表格中单元格 1 和单元格 2 的数据列布局：

Pre-wave 暴露的单元格 1 |预波控制单元 1 |波后曝光单元 1 |波后控制单元 1 |预波曝光单元 2 |预波控制单元 2 |波后曝光 Cell 2 |波后控制单元 2

2 - 然后，我计算了每个 KPI 中波前和波后期间每个曝光/控制的样本大小：

N_for_KPI <- c(683,538,2225,1458,294,307,922,781)
N <- c(1951,1564,5683,4507,819,862,2479,2511)
Wave <- factor(c("A","A","B","B","C","C"))
Brand <- factor(c(0,1,0,1,0,1))
data = data.frame(N_for_KPI,N)
Proportion <-N_for_KPI / N
Proportion

fit <- glm(Proportion~Wave*Brand, family=binomial, weights=N)
summary(fit)

3 - R 然后输出结果如下：

> Proportion
[1] 0.3500769 0.3439898 0.3915186 0.3234968 0.3589744 0.3561485 0.3719242 
0.3110315 
> fit <- glm(Proportion~Wave*Brand, family=binomial, weights=N)
Error in model.frame.default(formula = Proportion ~ Wave * Brand, weights 
= N,  : 
  variable lengths differ (found for 'Wave')
> summary(fit)

Call:
glm(formula = Proportion ~ Wave * Brand, family = binomial, weights = N)

Deviance Residuals: 
[1]  0  0  0  0

Coefficients:
         Estimate Std. Error z value Pr(>|z|)    
(Intercept)   -2.9422     0.1047 -28.096   <2e-16 ***
WaveB          0.0394     0.1203   0.328    0.743    
Brand1        -0.1574     0.1507  -1.045    0.296    
WaveB:Brand1  -0.4487     0.1786  -2.512    0.012 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance:  4.5383e+01  on 3  degrees of freedom
Residual deviance: -4.9938e-13  on 0  degrees of freedom
AIC: 35.137

Number of Fisher Scoring iterations: 3

4 - 目标是从模型中的比例变量得到显着性结果 >

问题： 1-引用的内容是否正确地描述了问题？ 2-我该如何修复错误： model.frame.default 中的错误（公式 = 比例 ~ Wave * 品牌，权重 = N，：可变长度不同（为“Wave”找到）

提前非常感谢!!