Agfdhyk

How can I plot the line asymptote off this curve above?

 # Create Data

    Conc <- c(0.03125, 0.06250, 0.12500, 0.25000, 0.50000, 1.00000, 0.03125, 0.06250, 0.12500, 0.25000, 0.50000, 1.00000, 0.03125, 0.06250, 0.12500, 0.25000, 0.50000, 1.00000, 0.03125, 0.06250, 0.12500, 0.25000, 0.50000, 1.00000, 0.03125, 0.06250, 0.12500, 0.25000, 0.50000, 1.00000, 0.03125, 0.06250, 0.12500, 0.25000, 0.50000, 1.00000)



    Response <- c(167.11246201, 53.96960486, 128.42857143, 43.67173252, 4.51975684, 0.34042553, 120.10334347, 101.14589666, 155.17629179, 35.31306991, 8.56534954, 1.71124620, 146.34954407, 108.50151976, 163.60182371, 64.70212766, 2.88145897, 0.50759878, 82.92401216, 109.80547112, 116.69300912, 26.85410334, 3.01519757, 0.37386018, 87.06990881, 84.82978723, 118.36474164, 27.52279635, 2.34650456, 0.10638298, 89.47720365, 109.47112462, 85.43161094, 17.69300912, 2.31306991, 0.07294833)



    df <- data.frame(Conc = Conc, Response = Response)



    #Make Modell

    library(drc)

    #adjust model

    drm <- drm(Response ~ Conc, data = df, fct = LL.4())

    #plot

    plot(drm)

This is an exemple of what I mean?
enter image description here

I am not sure what is the right way here, but I usually do it like this:

Logistic funtion

Still don't know how to use LL.4() for this purpose

flogis <- function(x, b, c, d, e){

  c + (d - c)/(1 + exp(b*(log(x) - log(e))))

}

Dataset

Make some example data:

dose <- rep(exp(seq(-5, 5, length.out = 10)), each = 3)

dat <- data.frame(

  dose = dose,

  response = flogis(dose, -1, 0, 1, .5) + rnorm(length(dose), 0, .05)

)



head(dat)

#  dose   response

#1 0.006737947 0.01310683

#2 0.006737947 0.08292573

#3 0.006737947 0.03263079

#4 0.020468076 0.02763111

#5 0.020468076 0.01934260

#6 0.020468076 0.01296994

Fitting 4-parameters log-logistic model

library(drc)



model <- drm(response ~ dose, data = dat, fct = LL.4())



summary(model)

#Model fitted: Log-logistic (ED50 as parameter) (4 parms)

#

#Parameter estimates:

#

#                Estimate Std. Error  t-value   p-value    

#b:(Intercept) -1.0012680  0.0887792 -11.2782 1.637e-11 ***

#c:(Intercept)  0.0049506  0.0243151   0.2036    0.8402    

#d:(Intercept)  0.9889417  0.0163848  60.3573 < 2.2e-16 ***

#e:(Intercept)  0.4054848  0.0419639   9.6627 4.310e-10 ***

#---

#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#

#Residual standard error:

#

#0.04466107 (26 degrees of freedom)

Grab model parameters for use with ggplot

coefs <- setNames(coef(model), c("b", "c", "d", "e"))

y50 <- predict(model, newdata = data.frame(dose = coefs["e"]))

Plot data

(Sorry, have no time to play with text labels, and don't understand what phi2 + phi3 means on the example plot, but pretty sure that is something which is going on around EC50)

ggplot(dat, aes(x = dose, y = response)) +

  stat_function(fun = function(x, b, c, d, e){

    c + (d - c)/(1 + exp(b*(log(x) - log(e))))

  }, args = coefs, col = "skyblue", lwd = 1) +

  geom_point(pch = 21, fill = "white") +

  geom_hline(yintercept = coefs[c("c", "d")], lty = 2, colour = "gray50") +

  geom_segment(aes(x = coefs["e"], y = 0, xend = coefs["e"], yend = y50), 

               lty = 2, colour = "gray50") +

  geom_segment(aes(x = coefs["e"], y = y50, xend = 0, yend = y50), 

               lty = 2, colour = "gray50") +

  scale_x_log10(

    breaks = scales::trans_breaks("log10", function(x) 10^x),

    labels = scales::trans_format("log10", scales::math_format(10^.x))

  ) +

  annotation_logticks(sides = "b") +

  labs(x = "Dose",

       y = "Response"

  ) +

  expand_limits(y = 1) +

  ggthemes::theme_few()

try this :

library(drc)



#adjust model

drm <- drm(Response ~ Conc, data = df, fct = LL.4())



#plot

plot(drm)

abline(a=112.6868 , 0)

You can extract the value of the asymptote from the drm object:

asymptote <- coef(drm)[3]

This value can be used for plotting:

plot(drm)

abline(asymptote, 0)