Plotting drc model in ggplot2; issue with seq( )

My model does not continue towards the asymptotes when plotted in ggplot2, though it does in R base graphics. In ggplot2, it stops at certain points on the X axis (images included below), I am 90% certain this is related to seq().

I am using predict() to transform data from a drm (dose-response package) logit model. In the base graphics, the sigmoidal curve looks great, in ggplot2, not so much:

library(drc)
library(ggplot2)

fitting data to logit model:

mod1 <- drm(probability ~ (dose), weights = total, data = mydata1,     type ="binomial", fct = LL.2())

plot(mod1,broken=FALSE,type="all",add=FALSE, col= "purple", xlim=c(0, 10000))

Image of Base graphic 2-parameter logit:

Extracting the data of the model using code from the author's demonstration (linked below), I have:

newdata1 <-expand.grid(dose=exp(seq(log(0.5),log(100),length=200)))

pm1<- predict(mod1, newdata=newdata1,interval="confidence")

newdata1$p1 <-pm1[,1]

newdata1$pmin1 <-pm1[,2]

newdata1$pmax1 <- pm1[,3]

And finally the ggplot2 graphic:

p1 <- ggplot(mydata1, aes(x=dose01,y=probability))+
  geom_point()+
   geom_ribbon(data=newdata1, aes(x=dose,y=p1,
 ymin=pmin1,ymax=pmax1),alpha=0.2,color="blue",fill="pink") +
   geom_step(data=newdata1, aes(x=dose,y=p1))+
  coord_trans(x="log") +  #creates logline for x axis
  xlab("dose")+ylab("response")

Images 1&2 and 3&4 show differences in my plot depending on seq:

When the following is used for seq(), the graphic gets pulled out past the data! (seq(log(0.5),**log(10000)**,length=200))) (images 1&2)

I don't understand seq() despite researching it. What is going on with my plots?

It appears that the first term in seq is defining the lower bound, but then what is the third term defining? You can see this in images 3&4 - The graphic is decent; I've somewhat obfuscated the issue, but it still doesn't continue towards infin. This is a slight issue because I will be co-plotting 8 logit models.

For anyone having trouble plotting drc/drm models with ggplot2, the following posts were very helpful: Search for plotting-dose-response-curves-with-ggplot2-and-drc
and this title: plotting-dose-response-curves-with-ggplot2-and-drc

I've followed the author of DRC's instructions, which can found in the supporting information of his article - Part of this code was used above. Article title: Dose-Response Analysis Using R, Christopher Ritz. PlosOne.

Data:

> dput(mydata1)

structure(list(dose = c(25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 75L, 75L, 75L, 75L, 75L, 75L, 
75L, 75L, 75L, 75L, 75L, 75L, 75L, 75L, 75L, 100L, 100L, 100L, 
100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 
100L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 
150L, 150L, 150L, 150L, 150L, 200L, 200L, 200L, 200L, 200L, 200L, 
200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L, 200L), total = c(25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L), affected = c(2L, 
0L, 0L, 0L, 0L, 1L, 1L, 2L, 2L, 4L, 1L, 1L, 4L, 0L, 10L, 0L, 
1L, 0L, 1L, 0L, 3L, 0L, 4L, 2L, 0L, 2L, 0L, 1L, 2L, 3L, 2L, 0L, 
2L, 0L, 0L, 4L, 0L, 1L, 2L, 3L, 0L, 21L, 1L, 3L, 1L, 2L, 7L, 
0L, 0L, 0L, 0L, 8L, 7L, 3L, 7L, 2L, 2L, 10L, 3L, 4L, 0L, 7L, 
0L, 3L, 3L, 20L, 25L, 22L, 23L, 22L, 18L, 14L, 20L, 20L, 21L), 
    probability = c(0.08, 0, 0, 0, 0, 0.04, 0.04, 0.08, 0.08, 
    0.16, 0.04, 0.04, 0.16, 0, 0.4, 0, 0.04, 0, 0.04, 0, 0.12, 
    0, 0.16, 0.08, 0, 0.08, 0, 0.04, 0.08, 0.12, 0.08, 0, 0.08, 
    0, 0, 0.16, 0, 0.04, 0.08, 0.12, 0, 0.84, 0.04, 0.12, 0.04, 
    0.08, 0.28, 0, 0, 0, 0, 0.32, 0.28, 0.12, 0.28, 0.08, 0.08, 
    0.4, 0.12, 0.16, 0, 0.28, 0, 0.12, 0.12, 0.8, 1, 0.88, 0.92, 
    0.88, 0.72, 0.56, 0.8, 0.8, 0.84)), .Names = c("dose", "total", 
"affected", "probability"), row.names = c(NA, -75L), class = "data.frame")

You mistook dose values to give predict() or aes(x) values:

log10000 <- exp(seq(log(0.5), log(10000), length=200))
log1000 <- exp(seq(log(0.5), log(1000), length=200))

log10000df <- as.data.frame(cbind(dose = log10000, predict(mod1, data.frame(dose = log10000), interval="confidence")))
log1000df <- as.data.frame(cbind(dose = log1000, predict(mod1, data.frame(dose = log1000), interval="confidence")))

 ## a common part
p0 <- ggplot(mydata1, aes(x = dose, y = probability)) +
  geom_point() + coord_trans(x="log") + 
  xlab("dose") + ylab("response") + xlim(0.5, 10001)

p10000 <- p0 + geom_line(data = log10000df, aes(x = dose, y = Prediction)) +
  geom_ribbon(data = log10000df, aes(x = dose, y = Prediction, ymin = Lower, ymax = Upper),
              alpha = 0.2, color = "blue", fill = "pink")
  
p1000 <- p0 + geom_line(data = log1000df, aes(x = dose, y = Prediction)) +
  geom_ribbon(data = log1000df, aes(x = dose, y = Prediction, ymin = Lower, ymax = Upper),
              alpha = 0.2, color = "blue", fill = "pink")