As a follow up to this question, I fitted the Multiple Logistic Regression with Interaction between Quantitative and Qualitative Explanatory Variables. MWE is given below:
Type <- rep(x=LETTERS[1:3], each=5)
Conc <- rep(x=seq(from=0, to=40, by=10), times=3)
Total <- 50
Kill <- c(10, 30, 40, 45, 38, 5, 25, 35, 40, 32, 0, 32, 38, 47, 40)
df <- data.frame(Type, Conc, Total, Kill)
fm1 <-
glm(
formula = Kill/Total~Type*Conc
, family = binomial(link="logit")
, data = df
, weights = Total
)
summary(fm1)
Call:
glm(formula = Kill/Total ~ Type * Conc, family = binomial(link = "logit"),
data = df, weights = Total)
Deviance Residuals:
Min 1Q Median 3Q Max
-4.871 -2.864 1.204 1.706 2.934
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.65518 0.23557 -2.781 0.00541 **
TypeB -0.34686 0.33677 -1.030 0.30302
TypeC -0.66230 0.35419 -1.870 0.06149 .
Conc 0.07163 0.01152 6.218 5.04e-10 ***
TypeB:Conc -0.01013 0.01554 -0.652 0.51457
TypeC:Conc 0.03337 0.01788 1.866 0.06201 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 277.092 on 14 degrees of freedom
Residual deviance: 96.201 on 9 degrees of freedom
AIC: 163.24
Number of Fisher Scoring iterations: 5
anova(object=fm1, test="LRT")
Analysis of Deviance Table
Model: binomial, link: logit
Response: Kill/Total
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 14 277.092
Type 2 6.196 12 270.895 0.04513 *
Conc 1 167.684 11 103.211 < 2e-16 ***
Type:Conc 2 7.010 9 96.201 0.03005 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
df$Pred <- predict(object=fm1, data=df, type="response")
df1 <- with(data=df,
expand.grid(Type=levels(Type)
, Conc=seq(from=min(Conc), to=max(Conc), length=51)
)
)
df1$Pred <- predict(object=fm1, newdata=df1, type="response")
library(ggplot2)
ggplot(data=df, mapping=aes(x=Conc, y=Kill/Total, color=Type)) + geom_point() +
geom_line(data=df1, mapping=aes(x=Conc, y=Pred), linetype=2) +
geom_hline(yintercept=0.5,col="gray")
I want to calculate LD50, LD90 and LD95 with their confidence intervals. As the interaction is significant so I want to calculate LD50, LD90 and LD95 with their confidence intervals for each Type (A, B, and C) separately.
LD stands for lethal dose. It is the amount of substance required to kill X% (LD50 = 50%) of the test population.
Edited
Type is a qualitative variable representing different types of drugs and Conc is a quantitative variable representing different Concentrations of drugs.
You use the drc package to fit logistic dose-response models.
First fit the model
require(drc)
mod <- drm(Kill/Total ~ Conc,
curveid = Type,
weights = Total,
data = df,
fct = L.4(fixed = c(NA, 0, 1, NA)),
type = 'binomial')
Here curveid=specifies the grouping of the data and fct= specifies a 4 parameter logistic function, with parameters for lower and upper bond fixed at 0 and 1.
Note the differences to glm are negligible:
df2 <- with(data=df,
expand.grid(Conc=seq(from=min(Conc), to=max(Conc), length=51),
Type=levels(Type)))
df2$Pred <- predict(object=mod, newdata = df2)
Here's a histgramm of the differences to the glm prediction
hist(df2$Pred - df1$Pred)
Estimate Effective Doses (and CI) from the model
This is easy with the ED() function:
ED(mod, c(50, 90, 95), interval = 'delta')
Estimated effective doses
(Delta method-based confidence interval(s))
Estimate Std. Error Lower Upper
A:50 9.1468 2.3257 4.5885 13.705
A:90 39.8216 4.3444 31.3068 48.336
A:95 50.2532 5.8773 38.7338 61.773
B:50 16.2936 2.2893 11.8067 20.780
B:90 52.0214 6.0556 40.1527 63.890
B:95 64.1714 8.0068 48.4784 79.864
C:50 12.5477 1.5568 9.4963 15.599
C:90 33.4740 2.7863 28.0129 38.935
C:95 40.5904 3.6006 33.5334 47.648
For each group we get ED50, ED90 & ED95 with CI.
Your link function of choice (\eta= X\hat\beta) has variance for a new observation (x_0): V_{x_0}=x_0^T(X^TWX)^{-1}x_0
So, for a set of candidate doses, we can predict the expected percentage of deaths using the inverse function:
newdata= data.frame(Type=rep(x=LETTERS[1:3], each=5),
Conc=rep(x=seq(from=0, to=40, by=10), times=3))
mm <- model.matrix(fm1, newdata)
# get link on link terms (could also use predict)
eta0 <- apply(mm, 1, function(i) sum(i * coef(fm1)))
# inverse logit function
ilogit <- function(x) return(exp(x) / (1+ exp(x)))
# predicted probs
ilogit(eta0)
# for comfidence intervals we can use a normal approximation
lethal_dose <- function(mod, newdata, alpha) {
qn <- qnorm(1 - alpha /2)
mm <- model.matrix(mod, newdata)
eta0 <- apply(mm, 1, function(i) sum(i * coef(fm1)))
var_mod <- vcov(mod)
se <- apply(mm, 1, function(x0, var_mod) {
sqrt(t(x0) %*% var_mod %*% x0)}, var_mod= var_mod)
out <- cbind(ilogit(eta0 - qn * se),
ilogit(eta0),
ilogit(eta0 + qn * se))
colnames(out) <- c("LB_CI", "point_est", "UB_CI")
return(list(newdata=newdata,
eff_dosage= out))
}
lethal_dose(fm1, newdata, alpha= 0.05)$eff_dosage
$eff_dosage
LB_CI point_est UB_CI
1 0.2465905 0.3418240 0.4517820
2 0.4361703 0.5152749 0.5936215
3 0.6168088 0.6851225 0.7462674
4 0.7439073 0.8166343 0.8722545
5 0.8315325 0.9011443 0.9439316
6 0.1863738 0.2685402 0.3704385
7 0.3289003 0.4044270 0.4847691
8 0.4890420 0.5567386 0.6223914
9 0.6199426 0.6990808 0.7679095
10 0.7207340 0.8112133 0.8773662
11 0.1375402 0.2112382 0.3102215
12 0.3518053 0.4335213 0.5190198
13 0.6104540 0.6862145 0.7531978
14 0.7916268 0.8620545 0.9113443
15 0.8962097 0.9469715 0.9736370
Rather than doing this manually, you could also manipulate:
predict.glm(fm1, newdata, se=TRUE)$se.fit
来源：https://stackoverflow.com/questions/36065327/multiple-logistic-regression-with-interaction-between-quantitative-and-qualitati