我有一个准二项式glm，其中包含两个连续的解释变量（假设为“ LogPesticide”和“ LogFood”），并且存在交互作用。我想在不同食物量（例如最小和最大食物值）下以置信区间计算农药的LC50。如何做到这一点？
示例：首先，我生成一个数据集。
mydata <- data.frame(
LogPesticide = rep(log(c(0, 0.1, 0.2, 0.4, 0.8, 1.6) + 0.05), 4),
LogFood = rep(log(c(1, 2, 4, 8)), each = 6)
)
set.seed(seed=16)
growth <- function(x, a = 1, K = 1, r = 1) { # Logistic growth function. a = position of turning point
Fx <- (K * exp(r * (x - a))) / (1 + exp(r * (x - a))) # K = carrying capacity
return(Fx) # r = growth rate …我在设置先验对比方面遇到了麻烦,并希望寻求一些帮助.以下代码应给出与因子水平"d"的两个正交对比.
Response <- c(1,3,2,2,2,2,2,2,4,6,5,5,5,5,5,5,4,6,5,5,5,5,5,5)
A <- factor(c(rep("c",8),rep("d",8),rep("h",8)))
contrasts(A) <- cbind("d vs h"=c(0,1,-1),"d vs c"=c(-1,1,0))
summary.lm(aov(Response~A))
我得到的是:
Call:
aov(formula = Response ~ A)
Residuals:
Min 1Q Median 3Q Max
-1.000e+00 -3.136e-16 -8.281e-18 -8.281e-18 1.000e+00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.0000 0.1091 36.661 < 2e-16 ***
Ad vs h -1.0000 0.1543 -6.481 2.02e-06 ***
Ad vs c 2.0000 0.1543 12.961 1.74e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard …