如何解决当我尝试对 3 因子交互进行建模时,“NA”出现在我的回归输出中
我首先创建我的数据集:
ExperimentDesign <- expand.grid(A = gl(2,1,labels = c("-","+")),B = gl(2,C = gl(2,"+")))
ExperimentDesign$response <- c(266.4,270.8,240.8,245.6,280.6,277.2,285.8,280.6)
数据帧的输出为:
A B C response
1 - - - 266.4
2 + - - 270.8
3 - + - 240.8
4 + + - 245.6
5 - - + 280.6
6 + - + 277.2
7 - + + 285.8
8 + + + 280.6
我做回归:
model <- lm(response ~ A + B + C + A*B + A*C + B*C + A*B*C,data = ExperimentDesign)
summary(model)
我在 std 错误、t 值和 p 值列中使用一堆 NA 获得此输出
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 266.4 NA NA NA
A+ 4.4 NA NA NA
B+ -25.6 NA NA NA
C+ 14.2 NA NA NA
A+:B+ 0.4 NA NA NA
A+:C+ -7.8 NA NA NA
B+:C+ 30.8 NA NA NA
A+:B+:C+ -2.2 NA NA NA
Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared: 1,Adjusted R-squared: NaN
F-statistic: NaN on 7 and 0 DF,p-value: NA
当我将模型更改为:
model <- lm(response ~ A + B + C + A*B + A*C + B*C,data = ExperimentDesign)
summary(model)
我的输出工作正常:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 266.1250 0.7276 365.767 0.00174 **
A+ 4.9500 0.9526 5.196 0.12104
B+ -25.0500 0.9526 -26.296 0.02420 *
C+ 14.7500 0.9526 15.483 0.04106 *
A+:B+ -0.7000 1.1000 -0.636 0.63921
A+:C+ -8.9000 1.1000 -8.091 0.07829 .
B+:C+ 29.7000 1.1000 27.000 0.02357 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
关于为什么我不使用三因素交互进行建模时模型运行良好的任何见解,以及如何使用三因素交互正确运行模型?
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