I'm creating a facetted plot to view predicted vs. actual values side by side with a plot of predicted value vs. residuals. I'll be using shiny to help explore the results of modeling efforts using different training parameters. I train the model with 85% of the data, test on the remaining 15%, and repeat this 5 times, collecting actual/predicted values each time. After calculating the residuals, my data.frame looks like this:
head(results)
act pred resid
2 52.81000 52.86750 -0.05750133
3 44.46000 42.76825 1.69175252
4 54.58667 49.00482 5.58184181
5 36.23333 35.52386 0.70947731
6 53.22667 48.79429 4.43237981
7 41.72333 41.57504 0.14829173
What I want:
- Side by side plot of
predvs.actandpredvs.resid - The x/y range/limits for
predvs.actto be the same, ideally frommin(min(results$act), min(results$pred))tomax(max(results$act), max(results$pred)) - The x/y range/limits for
predvs.residnot to be affected by what I do to the actual vs. predicted plot. Plotting forxover only the predicted values andyover only the residual range is fine.
In order to view both plots side by side, I melt the data:
library(reshape2)
plot <- melt(results, id.vars = "pred")
Now plot:
library(ggplot2)
p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")
print(p)
That's pretty close to what I want:
What I'd like is for the x and y ranges for actual vs. predicted to be the same, but I'm not sure how to specify that, and I don't need that done for the predicted vs. residual plot since the ranges are completely different.
I tried adding something like this for both scale_x_continous and scale_y_continuous:
min_xy <- min(min(plot$pred), min(plot$value))
max_xy <- max(max(plot$pred), max(plot$value))
p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")
p <- p + scale_x_continuous(limits = c(min_xy, max_xy))
p <- p + scale_y_continuous(limits = c(min_xy, max_xy))
print(p)
But that picks up the min() of the residual values.
One last idea I had is to store the value of the minimum act and pred variables before melting, and then add them to the melted data frame in order to dictate in which facet they appear:
head(results)
act pred resid
2 52.81000 52.86750 -0.05750133
3 44.46000 42.76825 1.69175252
4 54.58667 49.00482 5.58184181
5 36.23333 35.52386 0.70947731
min_xy <- min(min(results$act), min(results$pred))
max_xy <- max(max(results$act), max(results$pred))
plot <- melt(results, id.vars = "pred")
plot <- rbind(plot, data.frame(pred = c(min_xy, max_xy),
variable = c("act", "act"), value = c(max_xy, min_xy)))
p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")
print(p)
That does what I want, with the exception that the points show up, too:
Any suggestions for doing something like this?
I saw this idea to add geom_blank(), but I'm not sure how to specify the aes() bit and have it work properly, or what the geom_point() equivalent is to the histogram use of aes(y = max(..count..)).
Here's data to play with (my actual, predicted, and residual values prior to melting):
> dput(results)
structure(list(act = c(52.81, 44.46, 54.5866666666667, 36.2333333333333,
53.2266666666667, 41.7233333333333, 35.2966666666667, 30.6833333333333,
39.25, 35.8866666666667, 25.1, 29.0466666666667, 23.2766666666667,
56.3866666666667, 42.92, 41.57, 27.92, 23.16, 38.0166666666667,
61.8966666666667, 37.41, 41.6333333333333, 35.9466666666667,
48.9933333333333, 30.5666666666667, 32.08, 40.3633333333333,
53.2266666666667, 64.6066666666667, 38.5366666666667, 41.7233333333333,
25.78, 33.4066666666667, 27.8033333333333, 39.3266666666667,
48.9933333333333, 25.2433333333333, 32.67, 55.17, 42.92, 54.5866666666667,
23.16, 64.6066666666667, 40.7966666666667, 39.0166666666667,
41.6333333333333, 35.8866666666667, 25.1, 23.2766666666667, 44.46,
34.2166666666667, 40.8033333333333, 24.5766666666667, 35.73,
61.8966666666667, 62.1833333333333, 74.6466666666667, 39.4366666666667,
36.6, 27.1333333333333), pred = c(52.8675013282404, 42.7682474758679,
49.0048248585123, 35.5238560262515, 48.7942868566949, 41.5750416040131,
33.9548164913007, 29.9787449128663, 37.6443975781139, 36.7196211666685,
27.6043278172077, 27.0615724310721, 31.2073056885252, 55.0886903524179,
43.0895814712768, 43.0895814712768, 32.3549865881578, 26.2428426737583,
36.6926037128343, 56.7987490221996, 45.0370788180147, 41.8231642271826,
38.3297859332601, 49.5343916620086, 30.8535641206809, 29.0117492750411,
36.9767968381391, 49.0826677983065, 54.4678549541069, 35.5059204731218,
41.5333417555995, 27.6069075391361, 31.2404889715121, 27.8920960978598,
37.8505531149324, 49.2616631533957, 30.366837650159, 31.1623492639066,
55.0456078770405, 42.772538591063, 49.2419293590535, 26.1963523976241,
54.4080781796616, 44.9796700541254, 34.6996927469131, 41.6227713664027,
36.8449646519306, 27.5318686661673, 31.6641793552795, 42.8198894266632,
40.5769177148146, 40.5769177148146, 29.3807781312816, 36.8579132935989,
55.5617033901752, 55.8097119335638, 55.1041728261666, 43.6094641699075,
37.0674887276681, 27.3876960746536), resid = c(-0.0575013282403773,
1.69175252413213, 5.58184180815435, 0.709477307081826, 4.43237980997177,
0.148291729320228, 1.34185017536599, 0.704588420467079, 1.60560242188613,
-0.832954500001826, -2.50432781720766, 1.98509423559461, -7.93063902185855,
1.29797631424874, -0.169581471276786, -1.51958147127679, -4.43498658815778,
-3.08284267375831, 1.32406295383237, 5.09791764446704, -7.62707881801468,
-0.189830893849219, -2.38311926659339, -0.541058328675241, -0.286897454014273,
3.06825072495888, 3.38653649519422, 4.14399886836018, 10.1388117125598,
3.03074619354486, 0.189991577733821, -1.82690753913609, 2.16617769515461,
-0.088762764526507, 1.47611355173427, -0.268329820062384, -5.12350431682565,
1.5076507360934, 0.124392122959534, 0.147461408936991, 5.34473730761318,
-3.03635239762411, 10.1985884870051, -4.18300338745873, 4.31697391975358,
0.0105619669306023, -0.958297985263961, -2.43186866616734, -8.38751268861282,
1.64011057333683, -6.36025104814794, 0.226415618518729, -4.80411146461488,
-1.1279132935989, 6.33496327649151, 6.37362139976954, 19.5424938405001,
-4.17279750324084, -0.467488727668119, -0.254362741320246)), .Names = c("act",
"pred", "resid"), row.names = c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L,
52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L
), class = "data.frame")
Here's some code with a dummy geom_blank layer,
range_act <- range(range(results$act), range(results$pred))
d <- reshape2::melt(results, id.vars = "pred")
dummy <- data.frame(pred = range_act, value = range_act,
variable = "act", stringsAsFactors=FALSE)
ggplot(d, aes(x = pred, y = value)) +
facet_wrap(~variable, scales = "free") +
geom_point(size = 2.5) +
geom_blank(data=dummy) +
theme_bw()
I am not sure I understand what you want, but based on what I understood
the x scale seems to be the same, it is the y scale that is not the same, and that is because you specified scales ="free"
you can specify scales = "free_x" to only allow x to be free (in this case it is the same as pred has the same range by definition)
p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free_x")
worked for me, see the picture
I think you were making it too difficult - I do seem to remember one time defining the limits based on a formula with min and max and if faceted I think it used only those values, but I can't find the code
You can also specify the range with the coord_cartesian command to set the y-axis range that you want, an like in the previous post use scales = free_x
p <- ggplot(plot, aes(x = pred, y = value)) +
geom_point(size = 2.5) +
theme_bw()+
coord_cartesian(ylim = c(-20, 80))
p <- p + facet_wrap(~variable, scales = "free_x")
p
来源：https://stackoverflow.com/questions/18046051/setting-individual-axis-limits-with-facet-wrap-and-scales-free-in-ggplot2