r - Plotting dose response curves with ggplot2 and drc -

in biology want plot dose response curves. r package 'drc' useful , base graphics can handle 'drm models'. however, add drm curves ggplot2.

my dataset:

 library("drc")  library("reshape2")  library("ggplot2")  demo=structure(list(x = c(0, 1e-08, 3e-08, 1e-07, 3e-07, 1e-06, 3e-06,   1e-05, 3e-05, 1e-04, 3e-04), y1 = c(0, 1, 12, 19, 28, 32, 35,   39, na, 39, na), y2 = c(0, 0, 10, 18, 30, 35, 41, 43, na, 43,   na), y3 = c(0, 4, 15, 22, 28, 35, 38, 44, na, 44, na)), .names = c("x",  "y1", "y2", "y3"), class = "data.frame", row.names = c(na, -11l )) 

using base graphics:

plot(drm(data = reshape2::melt(demo,id.vars = "x"),value~x,fct=ll.4(),na.action = na.omit),type="bars") 

produces nice 4-parameter dose response plot.

trying plot same plot in ggplot2, stumble upon 2 issues.

1. there no way of directly adding drm model curve. need rewrite 4-pl function , add in form of stat_function, cumbersome least.

   ggplot(reshape2::melt(demo,id.vars = "x"),aes(x,value)) +    geom_point() +    stat_function(fun = function(x){     drm_y=function(x, drm){       coef(drm)[2]+((coef(drm)[3]-coef(drm)[2])/(1+exp((coef(drm)[1]*(log(x)-log(coef(drm)[4]))))))     } + drm_y(x,drm = drm(data = reshape2::melt(demo,id.vars = "x"), value~x, fct=ll.4(), na.action = na.omit))  }) 

2. if wasn't enough works if scale_x continuous. if want add scale_x_log10(), get: warning message: in log(x): nans produced.

i realise log10(0) = -inf there ways of handling this. either (as case plot.drc) x=0 value plotted on x-axis 1/100 of pre-lowest x-value. (demo$x[which.min(demo$x)+1]/100) or in graphpad prism, 0s omitted dose response curve entirely.

my questions are:

1. is there way of plotting drm models in ggplot2 directly?

2. how can link dataset corresponding 4-pl curvefit plotted in same colour?

a recent paper authors of drc package included instructions extracting parameters use ggplot2. don't work within ggplot2 extract data model. solution applied data.

demo1 <- reshape2::melt(demo,id.vars = "x") # numbers ready use. demo.ll.4 <- drm(data = demo1,value~x,fct=ll.4(),na.action = na.omit) # run model. 

the predict function can extract parameters drm models. isn't compatible multiple curves fit using curveid.

# predictions , confidence intervals. demo.fits <- expand.grid(conc=exp(seq(log(1.00e-04), log(1.00e-09), length=100)))  # new data predictions pm <- predict(demo.ll.4, newdata=demo.fits, interval="confidence")      demo.fits$p <- pm[,1]     demo.fits$pmin <- pm[,2]     demo.fits$pmax <- pm[,3] 

they advise shifting 0 concentration avoid issues coord_trans.

demo1$xx <- demo1$x demo1$xx[demo1$xx == 0] <- 1.00e-09 

then comes plotting curve, omitting geom_ribbon stops errors being drawn.

ggplot(demo1, aes(x = xx, y = value)) +   geom_point() +   geom_ribbon(data=demo.fits, aes(x=conc, y=p, ymin=pmin, ymax=pmax), alpha=0.2) +   geom_line(data=demo.fits, aes(x=conc, y=p)) +   coord_trans(x="log")  

to graph multiple curves process can repeated. add ids each set.

demo.fits_1 <- data.frame(label = "curve1", demo.fits) 

then use rbind combine extracted parameters. there ggplot can handle colours.