Question

DF <- data.frame(factor1=rep(1:4,1000), factor2 = rep(1:4,each=1000),base = rnorm(4000,0,1),dep=rnorm(4000,400,5))

DF$f1_1 = DF$factor1 == 1
DF$f1_2 = DF$factor1 == 2
DF$f1_3 = DF$factor1 == 3
DF$f1_4 = DF$factor1 == 4

DF$f2_1 = DF$factor2 == 1
DF$f2_2 = DF$factor2 == 2
DF$f2_3 = DF$factor2 == 3
DF$f2_4 = DF$factor2 == 4

我想运行以下回归：

Dep = (f1_1 + f1_2 + f1_3 + f1_4)*(f2_1 + f2_2 + f2_3 + f2_4)*(base+base^2+base^3+base^4+base^5)

有更聪明的方法吗？

Answer 1

您应该将factor1和factor2编码为实际因素变量。此外，最好将poly用于多项式。以下是我们可以做的事情：

DF <- data.frame(factor1=rep(1:4,1000), factor2 = rep(1:4,each=1000),
                 base = rnorm(4000,0,1), dep = rnorm(4000,400,5))

DF$factor1 <- as.factor(DF$factor1)
DF$factor2 <- as.factor(DF$factor2)

fit <- lm(dep ~ factor1 * factor2 * poly(base, degree = 5))

默认情况下，poly为数值稳定性生成正交基础。如果您想要base + base ^ 2 + base ^ 3 + ...之类的普通多项式，请使用poly(base, degree = 5, raw = TRUE)。

请注意，您将从此模型中获得大量参数，因为您为factor1和factor2之间的每对级别拟合了一个五阶多项式。

考虑一个小例子。

set.seed(0)
f1 <- sample(gl(3, 20, labels = letters[1:3]))    ## randomized balanced factor
f2 <- sample(gl(3, 20, labels = LETTERS[1:3]))    ## randomized balanced factor
x <- runif(3 * 20)  ## numerical covariate
y <- rnorm(3 * 20)  ## toy response

fit <- lm(y ~ f1 * f2 * poly(x, 2))

#Call:
#lm(formula = y ~ f1 * f2 * poly(x, 2))
#
#Coefficients:
#        (Intercept)                  f1b                  f1c  
#            -0.5387               0.8776               0.1572  
#                f2B                  f2C          poly(x, 2)1  
#             0.5113               1.0139               5.8345  
#        poly(x, 2)2              f1b:f2B              f1c:f2B  
#             2.4373               1.0666               0.1372  
#            f1b:f2C              f1c:f2C      f1b:poly(x, 2)1  
#            -1.4951              -1.4601              -6.2338  
#    f1c:poly(x, 2)1      f1b:poly(x, 2)2      f1c:poly(x, 2)2  
#           -11.0760              -2.3668               1.9708  
#    f2B:poly(x, 2)1      f2C:poly(x, 2)1      f2B:poly(x, 2)2  
#            -3.7127              -5.8253               5.6227  
#    f2C:poly(x, 2)2  f1b:f2B:poly(x, 2)1  f1c:f2B:poly(x, 2)1  
#            -7.3582              20.9179              11.6270  
#f1b:f2C:poly(x, 2)1  f1c:f2C:poly(x, 2)1  f1b:f2B:poly(x, 2)2  
#             1.2897              11.2041              12.8096  
#f1c:f2B:poly(x, 2)2  f1b:f2C:poly(x, 2)2  f1c:f2C:poly(x, 2)2  
#            -9.8476              10.6664               4.5582

注意，即使对于每个3因子水平和3阶多项式，我们已经得到了大量的系数。

Answer 2

使用I（）强制公式将+ - ×/视为算术而不是模型运算符。示例：lm（y~I（x1 + x2））

如何在R中的回归中编写交互？

2 个答案: