使用ggvis显示基于模型的MY预测？

Question

我想在ggvis图上显示模型的预测线，所以我可以动态地改变x轴上的比例。

我可以很容易地在ggplot中绘制模型预测：

但是当我尝试在ggvis中做这件事时，我会遇到奇怪的行为 - 我不知道如何告诉ggvis分组＆＃34; pop＆＃34;在预测的数据框中。这些是我得到的图表......我想知道目前这是否可行？只需在http://ggvis.rstudio.com/layers.html上阅读＆＃34;您目前无法将线条组件设置为不同的颜色：跟踪https://github.com/trifacta/vega/issues/122处的进度。＆＃34; hmmmm ...

以下可重现的示例。

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)

修改

想想我找到了一个解决方案： 将参数的顺序更改为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)