I want to add a stat_function layer to a plot with an aesthetic mapped to the state of some variable that identifies a set of parameters. I have manually created the two stat_function lines in the minimal working example below. That's generally what the result should look like.
p <- ggplot(data.frame(x = -1:1), aes(x = x))
p + stat_function(fun = function (x) 0 + 1 * x, linetype = 'dotted') +
stat_function(fun = function (x) 0.5 + -1 * x, linetype = 'solid')
My best guess at how to accomplish this is
params <- data.frame(
type = c('true', 'estimate'),
b = c(0, 0.5),
m = c(1, -1),
x = 0
)
linear_function <- function (x, b, m) b + m * x
p + stat_function(data = params,
aes(linetype = type, x = x),
fun = linear_function,
args = list(b = b, m = m))
This form works if I use constants like args = list(b = 0, m = 1), but when I try to get the values for the parameters out of the params data frame it's unable to find those columns. Why is this not working like I expect and what's a solution?
Unfortunately, nothing positive to add here; the fact stands: stat_function does not support this functionality.
The alternative is to either use for loops to make layers, as demonstrated in this question, or generate the grid data yourself, which is what was suggested as a closing comment to a feature request discussion about adding this functionality.
I want to display a list of text labels on a ggplot graph with the geom_text() function.
The positions of those labels are stored in a list.
When using the code below, only the second label appears.
x <- seq(0, 10, by = 0.1)
y <- sin(x)
df <- data.frame(x, y)
g <- ggplot(data = df, aes(x, y)) + geom_line()
pos.x <- list(5, 6)
pos.y <- list(0, 0.5)
for (i in 1:2) {
g <- g + geom_text(aes(x = pos.x[[i]], y = pos.y[[i]], label = paste("Test", i)))
}
print(g)
Any idea what is wrong with this code?
I agree with #user2728808 answer as a good solution, but here is what was wrong with your code.
Removing the aes from your geom_text will solve the problem. aes should be used for mapping variables from the data argument to aesthetics. Using it any differently, either by using $ or supplying single values can give unexpected results.
Code
for (i in 1:2) {
g <- g + geom_text(x = pos.x[[i]], y = pos.y[[i]], label = paste("Test", i))
}
I'm not exactly sure how geom_text can be used within a for-loop, but you can achieve the desired result by defining the text labels in advance and using annotate instead. See the code below.
library(ggplot2)
x <- seq(0, 10, by = 0.1)
y <- sin(x)
df <- data.frame(x, y)
pos.x <- c(5, 6)
pos.y <- c(0, 0.5)
titles <- paste("Test",1:2)
ggplot(data = df, aes(x, y)) + geom_line() +
annotate("text", x = pos.x, y = pos.y, label = titles)
I have a set of (2-dimensional) data points that I run through a classifier that uses higher order polynomial transformations. I want to visualize the results as a 2 dimensional scatterplot of the points with the classifier superimbosed on top, preferably using ggplot2 as all other visualizations are made by this. Pretty much like this one that was used in the ClatechX online course on machine learning (the background color is optional).
I can display the points with colors and symbols and all, that's easy but I can't figure out how to draw anything like the classifiers (the intersection of the classifiing hyperplane with the plane representing my threshold). The only thing I found was stat_function and that only takes a function with a single argument.
Edit:
The example that was asked for in the comments:
sample data:
"","x","y","x","x","y","value"
"1",4.17338115745224,0.303530843229964,1.26674990184152,17.4171102853774,0.0921309727918932,-1
"2",4.85514814266935,3.452660451876,16.7631779801937,23.5724634872656,11.9208641959486,1
"3",3.51938610081561,3.41200957307592,12.0081790673332,12.3860785266141,11.6418093267617,1
"4",3.18545089452527,0.933340128976852,2.97310914874565,10.1470974014319,0.87112379635852,-16
"5",2.77556006214581,2.49701633118093,6.93061880335166,7.70373365857888,6.23509055818427,-1
"6",2.45974169578403,4.56341833807528,11.2248303614692,6.05032920997851,20.8247869282818,1
"7",2.73947941488586,3.35344674880616,9.18669833727041,7.50474746458339,11.2456050970786,-1
"8",2.01721803518012,3.55453519499861,7.17027250203368,4.06916860145595,12.6347204524838,-1
"9",3.52376445778646,1.47073399974033,5.1825201951431,12.4169159539591,2.1630584979922,-1
"10",3.77387718763202,0.509284208528697,1.92197605658768,14.2421490273294,0.259370405056702,-1
"11",4.15821685106494,1.03675272315741,4.31104264382058,17.2907673804804,1.0748562089743,-1
"12",2.57985028671101,3.88512040604837,10.0230289934507,6.65562750184287,15.0941605694935,1
"13",3.99800728890114,2.39457673509605,9.5735352407471,15.9840622821066,5.73399774026327,1
"14",2.10979392635636,4.58358959294856,9.67042948411309,4.45123041169019,21.0092935565863,1
"15",2.26988795562647,2.96687697409652,6.73447830932721,5.15239133109813,8.80235897942413,-1
"16",1.11802248633467,0.114183261757717,0.127659454208164,1.24997427994995,0.0130378172656312,-1
"17",0.310411276295781,2.09426849964075,0.650084557879535,0.0963551604515758,4.38596054858751,-1
"18",1.93197490065359,1.72926536411978,3.340897280049,3.73252701675543,2.99035869954433,-1
"19",3.45879891654477,1.13636834081262,3.93046958599847,11.9632899450912,1.29133300600123,-1
"20",0.310697768582031,0.730971727753058,0.227111284709427,0.0965331034018534,0.534319666774291,-1
"21",3.88408110360615,0.915658151498064,3.55649052359657,15.0860860193904,0.838429850404852,-1
"22",0.287852146429941,2.16121324687265,0.622109872005114,0.0828588582043242,4.67084269845782,-1
"23",2.80277011333965,1.22467750683427,3.4324895146344,7.85552030822994,1.4998349957458,-1
"24",0.579150241101161,0.57801398797892,0.334756940497835,0.335415001767533,0.334100170299295-,1
"25",2.37193428212777,1.58276639413089,3.7542178708388,5.62607223873297,2.50514945839009,-1
"26",0.372461311053485,2.51207412336953,0.935650421453748,0.138727428231681,6.31051640130279,-1
"27",3.56567220995203,1.03982002707198,3.70765737388213,12.7140183088242,1.08122568869998,-1
"28",0.634770628530532,2.26303249713965,1.43650656059435,0.402933750845047,5.12131608311011,-1
"29",2.43812176748179,1.91849716124125,4.67752968967431,5.94443775306852,3.68063135769073,-1
"30",1.08741064323112,3.01656032912433,3.28023980783858,1.18246190701233,9.0996362192467,-1
"31",0.98,2.74,2.6852,0.9604,7.5076,1
"32",3.16,1.78,5.6248,9.9856,3.1684,1
"33",4.26,4.28,18.2328,18.1476,18.3184,-1
The code to generate a classifier:
perceptron_train <- function(data, maxIter=10000) {
set.seed(839)
X <- as.matrix(data[1:5])
Y <- data["value"]
d <- dim(X)
X <- cbind(rep(1, d[1]), X)
W <- rep(0, d[2] + 1)
count <- 0
while (count < maxIter){
H <- sign(X %*% W)
indexs <- which(H != Y)
if (length(indexs) == 0){
break
} else {
i <- sample(indexs, 1)
W <- W + 0.1 * (X[i,] * Y[i,])
}
count <- count + 1
point <- as.data.frame(data[i,])
plot_it(data, point, W, paste("plot", sprintf("%05d", count), ".png", sep=""))
}
W
}
The code to generate the plot:
plot_it <- function(data, point, weights, name = "plot.png") {
line <- weights_to_line(weights)
point <- point
png(name)
p = ggplot() + geom_point(data = data, aes(x, y, color = value, size = 2)) + theme(legend.position = "none")
p = p + geom_abline(intercept = line[2], slope = line[1])
print(p)
dev.off()
}
This was solved using material from the question and answers from Issues plotting a fitted SVM model's decision boundary using ggplot2's stat_contour(). I skipped the call to geom_point for the grid-entires and some of the aesthetical definitions like scale_fill_manual and scale_colour_manual. Removing the dots for the grid entries solved the problem with the vanishing contour-line in my case.
train_and_plot_svm <- function(train, kernel = "sigmoid", type ="C", cost, gamma) {
fit <- svm(as.factor(value) ~ x + y, data = train, kernel = kernel, type = type, cost = cost)
grid <- expand.grid (x = seq(from = -0.1, to = 15, length = 100), y = seq(from = -0.1, to = 15, length = 100))
decisionValues <- as.vector(attributes(predict(fit, grid, decision.values = TRUE))$decision)
p <- predict(fit, grid)
grid$value <- p
grid$z <- decisionValues
p <- ggplot() + stat_contour(data = grid, aes(x = x, y = y, z = z), breaks = c(0))
p <- p + geom_point(data = train, aes(x, y, colour = as.factor(value)), alpha = 0.7)
p <- p + xlim(0,15) + ylim(0,15) + theme(legend.position="none")
}
Note that this function doesn't return the result of the svm training but the ggplot2 object.
This is, what I got:
Given family of functions f(x;q) (x is argument and q is parameter) I'd like to visulaize this function family on x taking from the interval [0,1] for 9 values of q (from 0.1 to 0.9). So far my solution is:
f = function(p,q=0.9) {1-(1-(p*q)^3)^1024}
x = seq(0.0,0.99,by=0.01)
q = seq(0.1,0.9,by=0.1)
qplot(rep(x,9), f(rep(x,9),rep(q,each=100)), colour=factor(rep(q,each=100)),
geom="line", size=I(0.9), xlab="x", ylab=expression("y=f(x)"))
I get quick and easy visual with qplot:
My concern is that this method is rather memory hungry as I need to duplicate x for each parameter and duplicate each parameter value for whole x range. What would be alternative way to produce same graph without these duplications?
At some point ggplot will need to have the data available to plot it and the way that package works prohibits simply doing what you want. I suppose you could set up a blank plot if you know the x and y axis limits, and then loop over the 9 values of q, generating the data for that q, and adding a geom_line layer to the existing plot object. However, you'll have to produce the colours for each layer yourself.
If this is representative of the size of problem you have, I wouldn't worry too much about the memory footprint. We're only talking about a two vectors of length 900
> object.size(rnorm(900))
7240 bytes
and the 100 values over the range of x appears sufficient to give a smooth plot.
for loop to add layers to ggplot
require("ggplot2")
## something to replicate ggplot's colour palette, sure there is something
## to do this already in **ggplot** now...
ggHueColours <- function(n, h = c(0, 360) + 15, l = 65, c = 100,
direction = 1, h.start = 0) {
turn <- function(x, h.start, direction) {
(x + h.start) %% 360 * direction
}
if ((diff(h) %% 360) < 1) {
h[2] <- h[2] - 360 / n
}
hcl(h = turn(seq(h[1], h[2], length = n), h.start = h.start,
direction = direction), c = c, l = l)
}
f = function(p,q=0.9) {1-(1-(p*q)^3)^1024}
x = seq(0.0,0.99,by=0.01)
q = seq(0.1,0.9,by=0.1)
cols <- ggHueColours(n = length(q))
for(i in seq_along(q)) {
df <- data.frame(y = f(x, q[i]), x = x)
if(i == 1) {
plt <- ggplot(df, aes(x = x, y = y)) + geom_line(colour = cols[i])
} else {
plt <- plt + geom_line(data = df, colour = cols[i])
}
}
plt
which gives:
I'll leave the rest to you - I'm not familiar enough with ggplot to draw a legend manually.