Reproducing drc::plot.drc with ggplot2 - r

Question

I want to reproduce the following drc::plot.drc graphs with ggplot2.
df1 <-
structure(list(TempV = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L), .Label = c("22.46FH-142", "27.59FH-142", "26.41FH-142",
"29.71FH-142", "31.66FH-142", "34.11FH-142", "33.22FH-142", "22.46FH-942",
"27.59FH-942", "26.41FH-942", "29.71FH-942", "31.66FH-942", "34.11FH-942",
"33.22FH-942"), class = "factor"), Start = c(0L, 24L, 48L, 72L,
96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L,
144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L, 144L, 168L,
192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L, 144L, 168L, 192L, 216L,
0L, 24L, 48L, 72L, 96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L,
48L, 72L, 96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L,
96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L,
144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L, 144L, 168L,
192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L, 144L, 168L, 192L, 216L,
0L, 24L, 48L, 72L, 96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L,
48L, 72L, 96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L,
96L, 120L, 144L, 168L, 192L, 216L, 0L, 24L, 48L, 72L, 96L, 120L,
144L, 168L, 192L, 216L), End = c(24, 48, 72, 96, 120, 144, 168,
192, 216, Inf, 24, 48, 72, 96, 120, 144, 168, 192, 216, Inf,
24, 48, 72, 96, 120, 144, 168, 192, 216, Inf, 24, 48, 72, 96,
120, 144, 168, 192, 216, Inf, 24, 48, 72, 96, 120, 144, 168,
192, 216, Inf, 24, 48, 72, 96, 120, 144, 168, 192, 216, Inf,
24, 48, 72, 96, 120, 144, 168, 192, 216, Inf, 24, 48, 72, 96,
120, 144, 168, 192, 216, Inf, 24, 48, 72, 96, 120, 144, 168,
192, 216, Inf, 24, 48, 72, 96, 120, 144, 168, 192, 216, Inf,
24, 48, 72, 96, 120, 144, 168, 192, 216, Inf, 24, 48, 72, 96,
120, 144, 168, 192, 216, Inf, 24, 48, 72, 96, 120, 144, 168,
192, 216, Inf, 24, 48, 72, 96, 120, 144, 168, 192, 216, Inf),
Germinated = c(0L, 0L, 0L, 0L, 3L, 67L, 46L, 12L, 101L, 221L,
0L, 0L, 0L, 0L, 57L, 50L, 44L, 31L, 32L, 236L, 0L, 0L, 0L,
31L, 68L, 50L, 31L, 34L, 29L, 207L, 0L, 0L, 8L, 30L, 31L,
55L, 27L, 22L, 4L, 273L, 0L, 0L, 46L, 64L, 16L, 8L, 15L,
15L, 20L, 266L, 0L, 0L, 0L, 0L, 4L, 13L, 63L, 51L, 147L,
172L, 0L, 0L, 4L, 26L, 92L, 31L, 91L, 14L, 7L, 185L, 0L,
0L, 0L, 0L, 0L, 32L, 59L, 36L, 50L, 273L, 0L, 0L, 0L, 4L,
13L, 32L, 42L, 52L, 42L, 265L, 0L, 0L, 0L, 6L, 22L, 40L,
57L, 44L, 73L, 208L, 0L, 1L, 2L, 24L, 55L, 41L, 68L, 24L,
33L, 202L, 0L, 0L, 18L, 31L, 26L, 30L, 61L, 25L, 58L, 201L,
0L, 0L, 36L, 54L, 33L, 55L, 12L, 27L, 55L, 178L, 0L, 0L,
6L, 28L, 26L, 31L, 53L, 48L, 33L, 225L)), .Names = c("TempV",
"Start", "End", "Germinated"), row.names = c(NA, -140L), class = "data.frame")
library(data.table)
dt1 <- data.table(df1)
library(drc)
dt1fm1 <-
drm(
formula = Germinated ~ Start + End
, curveid = TempV
# , pmodels =
# , weights =
, data = dt1
# , subset =
, fct = LL.2()
, type = "event"
, bcVal = NULL
, bcAdd = 0
# , start =
, na.action = na.fail
, robust = "mean"
, logDose = NULL
, control = drmc(
constr = FALSE
, errorm = TRUE
, maxIt = 1500
, method = "BFGS"
, noMessage = FALSE
, relTol = 1e-07
, rmNA = FALSE
, useD = FALSE
, trace = FALSE
, otrace = FALSE
, warnVal = -1
, dscaleThres = 1e-15
, rscaleThres = 1e-15
)
, lowerl = NULL
, upperl = NULL
, separate = FALSE
, pshifts = NULL
)
## ----dt1fm1Plot1----
plot(
x = dt1fm1
, xlab = "Time (Hours)"
, ylab = "Proportion Germinated (\\%)"
# , ylab = "Proportion Germinated (%)"
, add = FALSE
, level = NULL
, type = "average" # c("average", "all", "bars", "none", "obs", "confidence")
, broken = FALSE
# , bp
, bcontrol = NULL
, conName = NULL
, axes = TRUE
, gridsize = 100
, log = ""
# , xtsty
, xttrim = TRUE
, xt = NULL
, xtField = NULL
, xField = "Time (Hours)"
, xlim = c(0, 200)
, yt = NULL
, ytField = NULL
, yField = "Proportion Germinated"
, ylim = c(0, 1.05)
, lwd = 1
, cex = 1.2
, cex.axis = 1
, col = TRUE
# , lty
# , pch
, legend = TRUE
# , legendText
, legendPos = c(40, 1.1)
, cex.legend = 0.6
, normal = FALSE
, normRef = 1
, confidence.level = 0.95
)
## ----dt1fm1Plot2----
dt1fm1Means1 <- dt1[, .(Germinated=mean(Germinated)/450), by=.(TempV, Start, End)]
dt1fm1Means2 <- dt1fm1Means1[, .(Start=Start, End=End, Cum_Germinated=cumsum(Germinated)), by=.(TempV)]
dt1fm1Means <- data.table(dt1fm1Means2[End!=Inf], Pred=predict(object=dt1fm1))
dt1fm1Plot2 <-
ggplot(data= dt1fm1Means, mapping=aes(x=End, y=Cum_Germinated, group=TempV, color=TempV, shape=TempV)) +
geom_point() +
geom_line(aes(y = Pred)) +
scale_shape_manual(values=seq(0, 13)) +
labs(x = "Time (Hours)", y = "Proportion Germinated", shape="Temp", color="Temp") +
theme_bw() +
scale_x_continuous(expand = c(0, 0), breaks = c(0, unique(dt1fm1Means$End))) +
scale_y_continuous(expand = c(0, 0), labels = function(x) paste0(100*x,"\\%")) +
# scale_y_continuous(expand = c(0, 0), labels = percent) +
expand_limits(x = c(0, max(dt1fm1Means$End)+20), y = c(0, max(dt1fm1Means$Pred)+0.1)) +
theme(axis.title.x = element_text(size = 12, hjust = 0.54, vjust = 0),
axis.title.y = element_text(size = 12, angle = 90, vjust = 0.25))
print(dt1fm1Plot2)
Question
There are few discrepancies in ggplot2 output. These discrepancies occur because the predict function gives output in different pattern than the given levels in the data.
Edited
Actually drm function changed the order of levels of TempV and this is clear from summary(dt1fm1) output and the graph of drc::plot.drc output.

Answer 1

As noted in the question, there is an issue related to drm shuffling the order of factor levels. Un-shuffling this mess proved more tricky than I expected.
In the end I approached this by calling the drm function once per factor level to build up a table of results one factor level at a time.
Doing it this long-winded way uncovered the fact that your 1st plot from plot.drc and the ggplot version are both incorrect.
Let's start by wrapping your function call to drm() inside another wrapper function, to facilitate calling it repeatedly for each trace:
drcmod <- function(dt1){
drm(formula = Germinated ~ Start + End
, curveid = TempV
, data = dt1
, fct = LL.2()
, type = "event"
, bcVal = NULL
, bcAdd = 0
, na.action = na.fail
, robust = "mean"
, logDose = NULL
, control = drmc(
constr = FALSE
, errorm = TRUE
, maxIt = 1500
, method = "BFGS"
, noMessage = FALSE
, relTol = 1e-07
, rmNA = FALSE
, useD = FALSE
, trace = FALSE
, otrace = FALSE
, warnVal = -1
, dscaleThres = 1e-15
, rscaleThres = 1e-15
)
, lowerl = NULL
, upperl = NULL
, separate = FALSE
, pshifts = NULL
)
}
Now we can use this wrapper to fit the drc model to each factor level in turn:
dt2 <- data.table()
for (i in 1:nlevels(dt1$TempV)) {
dt <- dt1[TempV==levels(TempV)[i]]
dt[, TempV:=as.character(TempV)]
dt[, Germ_frac := mean(Germinated)/450, by=.(Start)]
dt[, cum_Germinated := cumsum(Germ_frac)]
dt[, Pred := c(predict(object=drcmod(dt)), NA)]
dt2 <- rbind(dt2, dt)
}
and plot:
ggplot(dt2[End != Inf], aes(x=End, y=cum_Germinated, group=TempV, color=TempV, shape=TempV)) +
geom_point() +
geom_line(aes(y = Pred)) +
scale_shape_manual(values=seq(0, 13)) +
labs(x = "Time (Hours)", y = "Proportion Germinated", shape="Temp", color="Temp") +
theme_bw()
Edit
If we run the original code in the question using a subset of the data with fewer factor levels, for example using
dt1 <- dt1[TempV %in% levels(TempV)[1:5],]
dt1 <- droplevels(dt1)
all the plots (the 2 versions in OP, and the version in this answer) give the same result. The discrepancies only seem to arise when a large number of factor levels are used. The fact that both the ggplot and the plot.drc in OP give incorrect matching of traces to factor levels indicates that the problem is most likely to be in the drm() function, rather than in plot.drc.