使用 ggvis 显示我的基于模型的预测？

Question

I'd like to display the prediction lines of a model on a ggvis plot, so I can dynamically change the scale on the x-axis.

I can plot the model predictions in ggplot easily enough:

But when I try to do it in ggvis, I get strange behaviours - I don't know how to tell ggvis to group by "pop" in the predicted dataframe. These are the graphs that I'm getting... I'm wondering if this is even possible currently? Just read on http://ggvis.rstudio.com/layers.html that "You can not currently set the component of lines to different colours: track progress at https://github.com/trifacta/vega/issues/122 ." hmmmm...

Reproducible example below.

library(nlme)
library(dplyr)
library(ggplot2)
library(ggvis)


dframe <- structure(list(pop = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label =
c("P1", "P2"), class = "factor"), id = structure(c(1L, 2L, 1L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 2L, 10L, 11L, 11L, 12L, 5L, 13L, 2L, 14L, 10L, 15L, 5L, 16L, 16L,
17L, 18L, 19L, 20L, 21L, 23L, 24L, 25L, 22L, 24L, 23L, 25L, 22L, 16L, 20L,
11L, 3L, 2L, 1L, 1L), .Label = c("A", "B", "C", "D", "E", "F", "G", "H", "I",
"J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y"
), class = "factor"), x = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
10.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 0.5, 1.5, 2.5,
3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5,
7.5, 8.5, 9.5, 10.5), act = c(13.9, 47.8, 68.3, 80.8, 88.4, 92.9, 95.7, 97.4,
98.4, 99, 99.4, 13.9, 47.8, 68.3, 80.8, 88.4, 92.9, 95.7, 97.4, 98.4, 99,
99.4, 12.7, 35.2, 48.9, 57.2, 62.2, 65.3, 67.1, 68.3, 69, 69.4, 69.6, 12.7,
35.2, 48.9, 57.2, 62.2, 65.3, 67.1, 68.3, 69, 69.4, 69.6), y = c(15L, 46L, 
68L, 80L, 92L, 89L, 95L, 97L, 99L, 96L, 103L, 14L, 43L, 72L, 81L, 88L, 94L,
93L, 98L, 96L, 100L, 102L, 12L, 36L, 50L, 54L, 62L, 66L, 68L, 65L, 71L, 69L,
68L, 14L, 37L, 51L, 56L, 63L, 66L, 69L, 65L, 70L, 69L, 73L)), .Names =
c("pop", "id", "x", "act", "y"), class = "data.frame", row.names = c(NA, -44L 
))

LVB = function(t, Linf, K, t0) 
{
  if (length(Linf) == 3) {
    K <- Linf[[2]]
    t0 <- Linf[[3]]
    Linf <- Linf[[1]]
  }
  Linf*(1-exp(-K*(t-t0)))
}

# Fit a null model with random effects (not interested in them right now)
model <- nlme(y~LVB(x,Linf, K, t0),data=dframe,
              fixed = list(Linf~pop, K~1, t0~pop),
              random = Linf ~1|id,
              start  = list(fixed= c(80, 0,
                                     0.5,
                                     -0.2, 0)))

# Create data frame of predicted values
predframe <- with(dframe, expand.grid(x = seq(0.5, 11, 0.1), y = seq(min(y), max(y), 20), pop = unique(pop)))
predframe$fitted <- predict(model, level = 0, newdata = predframe)

# Graph with ggplot 
g <- ggplot(dframe, aes(x, y, color = pop))
g + geom_point() + 
  geom_line(data =predframe, aes(x=x, y=fitted, color= pop))

# This is plotting the model bits properly
ggvis(dframe, ~x, ~y, fill = ~pop) %>%
  layer_points(size := 30) %>%
  layer_points(data = predframe, y =~fitted, fill =~pop, size := 1)

# This is the best I can get
ggvis(dframe, ~x, ~y, fill = ~pop) %>%
  layer_points() %>%
  layer_paths(data = predframe, y =~fitted, fill := NA, stroke =~pop)

# Results in squiggles
predframe <- predframe[order(predframe$fitted),]
ggvis(dframe, ~x, ~y, fill = ~pop) %>%
  layer_points() %>%
  layer_paths(data = predframe, y =~fitted, fill := NA, stroke =~pop)

# More squiggles.
predframe <- predframe[order(predframe$x),]
ggvis(dframe, ~x, ~y, fill = ~pop) %>%
  layer_points() %>%
  layer_paths(data = predframe, y =~fitted, fill := NA, stroke =~pop)

EDIT

Think I found a solution: Change the order of the arguments to ggvis:

    ggvis(predframe, ~x, ~fitted, stroke = ~pop) %>%
  layer_lines() %>%
  layer_points(data = dframe, x=~x, y=~y, fill = ~pop) %>%
  scale_numeric('x', domain = input_slider(0, 11, c(0, 11)), clamp = T)

Answer 1

With @aosmith's help (thanks!), and some tweaking, we came up with two solutions to this problem, I'm posting both solutions here - to see the solution graphed, look at the "edits" section of my original question.

First solution (you don't have to sort the input data frame, but you DO have to put the arguments in this order to ggvis):

ggvis(predframe, ~x, ~fitted, stroke = ~pop) %>%
  layer_lines() %>%
  layer_points(data = dframe, x=~x, y=~y, fill = ~pop) %>%
  scale_numeric('x', domain = input_slider(0, 11, c(0, 11)), clamp = T)

Second solution (you have to sort the predicted values data.frame first):

predframe <- predframe %>%
  arrange(x)
ggvis(dframe, ~x, ~y, fill = ~pop, stroke = ~pop) %>%
  layer_points() %>%
  layer_paths(data = group_by(predframe, pop), y =~fitted, stroke =~pop, fill := NA) %>%
  scale_numeric('x', domain = input_slider(0, 11, c(0, 11)), clamp = T)