Extract out points in dose-response curve produced by {drc} ?

[cwright1]

Per this tutorial using the drc package, plot a dose-response curve:

library(tidyverse)
library(drc)

toxdata<- ryegrass

model<- drm(rootl~conc, data=ryegrass, fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))

plot(model, type="all")

How can I extract the predicted (interpolated?) points in between the observed points?

For example, if I want to find out which point within the curve lies closest to y=4.5 ?

[FJCC]

Here is code to get y values from a given x and x from a given y, using the ryegrass data set.

library(drc)
#> Loading required package: MASS
#> 
#> 'drc' has been loaded.
#> Please cite R and 'drc' if used for a publication,
#> for references type 'citation()' and 'citation('drc')'.
#> 
#> Attaching package: 'drc'
#> The following objects are masked from 'package:stats':
#> 
#>     gaussian, getInitial

toxdata<- data("ryegrass")

model<- drm(rootl~conc, data=ryegrass, fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))

summary(model)
#> 
#> Model fitted: Log-logistic (ED50 as parameter) (4 parms)
#> 
#> Parameter estimates:
#> 
#>                         Estimate Std. Error t-value   p-value    
#> Slope:(Intercept)        2.98222    0.46506  6.4125 2.960e-06 ***
#> Lower Limit:(Intercept)  0.48141    0.21219  2.2688   0.03451 *  
#> Upper Limit:(Intercept)  7.79296    0.18857 41.3272 < 2.2e-16 ***
#> ED50:(Intercept)         3.05795    0.18573 16.4644 4.268e-13 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error:
#> 
#>  0.5196256 (20 degrees of freedom)

S <- model$fit$par[1]
LL <- model$fit$par[2]
UL <- model$fit$par[3]
ED50 <- model$fit$par[4]

#Calc rootl from a given conc
X <- 0.94
Rootl <- LL + (UL - LL)/(1 + (ED50/X)^-S)
Rootl
#> [1] 7.582331

#Calc conc from a given rootl
Y <- 4.5
Conc <- ED50/((UL-Y)/(Y-LL))^(-1/S)
Conc
#> [1] 2.860422

plot(model, type="all", xlim = c(0,9))
points(c(X, Conc), c(Rootl, Y), pch = 17, col = "red")

^{Created on 2023-02-06 with reprex v2.0.2}

[FJCC]

I should have mentioned that you can go from a concentration to a rootl with the predict function.

predict(model, data.frame(conc = 0.94))
Prediction 
  7.582331 

[technocrat]

@FJCC beat me to it. I had gotten as far as seeing the shape

library(drc)
#> Loading required package: MASS
#> 
#> 'drc' has been loaded.
#> Please cite R and 'drc' if used for a publication,
#> for references type 'citation()' and 'citation('drc')'.
#> 
#> Attaching package: 'drc'
#> The following objects are masked from 'package:stats':
#> 
#>     gaussian, getInitial

toxdata <- ryegrass

model <- drm(rootl~conc, data=ryegrass, fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))

dev.off()
#> null device 
#>           1
plot(model, type="all")
abline(4.5,0,lty = 2)
abline(0,10,lty = 2)