我有一个名为datamoth的数据集，其中survival是响应变量，而treatment是既可以分类又可以量化的变量。数据集如下所示：
survival <- c(17,22,26,20,11,14,37,26,24,11,11,16,8,5,12,3,5,4,14,8,4,6,3,3,10,13,5,7,3,3)
treatment <- c(3,3,3,3,3,3,6,6,6,6,6,6,9,9,9,9,9,9,12,12,12,12,12,12,21,21,21,21,21,21)
days <- c(3,3,3,3,3,3,6,6,6,6,6,6,9,9,9,9,9,9,12,12,12,12,12,12,21,21,21,21,21,21)
datamoth <- data.frame(survival, treatment)
因此，我可以将线性处理考虑为分类来拟合线性回归模型，如下所示：
lmod<-lm(survival ~ factor(treatment), datamoth)
我的问题是如何将治疗作为分类变量，同时将治疗作为定量混杂变量，以拟合线性回归模型。 我发现了这样的东西：
model <- lm(survival ~ factor(treatment) + factor(treatment)*days, data = datamoth)
summary(model)
Call:
lm(formula = survival ~ factor(treatment) + factor(treatment) *
days, data = datamoth)
Residuals:
Min 1Q Median 3Q Max
-9.833 -3.333 -1.167 3.167 16.167
Coefficients: (5 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.333 2.435 7.530 6.96e-08 ***
factor(treatment)6 2.500 3.443 0.726 0.47454
factor(treatment)9 -12.167 3.443 -3.534 0.00162 **
factor(treatment)12 -12.000 3.443 -3.485 0.00183 **
factor(treatment)21 -11.500 3.443 -3.340 0.00263 **
days NA NA NA NA
factor(treatment)6:days NA NA NA NA
factor(treatment)9:days NA NA NA NA
factor(treatment)12:days NA NA NA NA
factor(treatment)21:days NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.964 on 25 degrees of freedom
Multiple R-squared: 0.5869, Adjusted R-squared: 0.5208
F-statistic: 8.879 on 4 and 25 DF, p-value: 0.0001324
但是很明显，此代码无法正常工作，因为这两个变量是共线的。
有谁知道如何解决它？任何帮助将不胜感激。