I am trying to plot the function described below. However, when I do plot it, the x-axis shows 0 up to 1000, probably due to my definition of x0, instead of 0 up to 10. However, if I just use 1:10 as my x-values, the plot is not smooth at all. My question is: how do I set the x-axis to give me the real values that correspond to the function at that point?
PS: I have seen solutions where people replace the x-axis fully with their own values, but I am looking for a method that just uses the actual values of x.
eq <- function(x){
4*x^3 - 72*x^2 + 288*x
}
x0 <- seq(1, 10, by = 0.01)
plot(eq(x0),type='l',main="Plot of 4*x^3 - 72*x^2 + 288*x", xlab = "x", ylab = "4*x^3 - 72*x^2 + 288*x")
You have missed your x0 values. R deduced the x values based on the number of elements in eq(x0) (A sequence 1:1000 was used by default). By the way, you can improve your labels with expression
plot(x = x0,
y = eq(x0),
type='l',
main=expression(paste("Plot of ", 4*x^3 - 72*x^2 + 288*x)),
xlab = expression(x),
ylab = expression(4*x^3 - 72*x^2 + 288*x))
It works fine
Related
How can such a non-linear transformation be done?
here is the code to draw it
my.sin <- function(ve,a,f,p) a*sin(f*ve+p)
s1 <- my.sin(1:100, 15, 0.1, 0.5)
s2 <- my.sin(1:100, 21, 0.2, 1)
s <- s1+s2+10+1:100
par(mfrow=c(1,2),mar=rep(2,4))
plot(s,t="l",main = "input") ; abline(h=seq(10,120,by = 5),col=8)
plot(s*7,t="l",main = "output")
abline(h=cumsum(s)/10*2,col=8)
don't look at the vector, don't look at the values, only look at the horizontal grid, only the grid matters
####UPDATE####
I see that my question is not clear to many people, I apologize for that...
Here are examples of transformations only along the vertical axis, maybe now it will be more clear to you what I want
link Source
#### UPDATE 2 ####
Thanks for your answer, this looks like what I need, but I have a few more questions if I may.
To clarify, I want to explain why I need this, I want to compare vectors with each other that are non-linearly distorted along the horizontal axis .. Maybe there are already ready-made tools for this?
You mentioned that there are many ways to do such non-linear transformations, can you name a few of the best ones in my case?
how to make the function f() more non-linear, so that it consists, for example, not of one sinusoid, but of 10 or more. Тhe figure shows that the distortion is quite simple, it corresponds to one sinusoid
and how to make the function f can be changed with different combinations of sinusoids.
set.seed(126)
par(mar = rep(2, 4),mfrow=c(1,3))
s <- cumsum(rnorm(100))
r <- range(s)
gridlines <- seq(r[1]*2, r[2]*2, by = 0.2)
plot(s, t = "l", main = "input")
abline(h = gridlines, col = 8)
f <- function(x) 2 * sin(x)/2 + x
plot(s, t = "l", main = "input+new greed")
abline(h = f(gridlines), col = 8)
plot(f(s), t = "l", main = "output")
abline(h = f(gridlines), col = 8)
If I understand you correctly, you wish to map the vector s from the regular spacing defined in the first image to the irregular spacing implied by the second plot.
Unfortunately, your mapping is not well-defined, since there is no clear correspondence between the horizontal lines in the first image and the second image. There are in fact an infinite number of ways to map the first space to the second.
We can alter your example a bit to make it a bit more rigorous.
If we start with your function and your data:
my.sin <- function(ve, a, f, p) a * sin(f * ve + p)
s1 <- my.sin(1:100, 15, 0.1, 0.5)
s2 <- my.sin(1:100, 21, 0.2, 1)
s <- s1 + s2 + 10 + 1:100
Let us also create a vector of gridlines that we will draw on the first plot:
gridlines <- seq(10, 120, by = 2.5)
Now we can recreate your first plot:
par(mar = rep(2, 4))
plot(s, t = "l", main = "input")
abline(h = gridlines, col = 8)
Now, suppose we have a function that maps our y axis values to a different value:
f <- function(x) 2 * sin(x/5) + x
If we apply this to our gridlines, we have something similar to your second image:
plot(s, t = "l", main = "input")
abline(h = f(gridlines), col = 8)
Now, what we want to do here is effectively transform our curve so that it is stretched or compressed in such a way that it crosses the gridlines at the same points as the gridlines in the original image. To do this, we simply apply our mapping function to s. We can check the correspondence to the original gridlines by plotting our new curves with a transformed axis :
plot(f(s), t = "l", main = "output", yaxt = "n")
axis(2, at = f(20 * 1:6), labels = 20 * 1:6)
abline(h = f(gridlines), col = 8)
It may be possible to create a mapping function using the cumsum(s)/10 * 2 that you have in your original example, but it is not clear how you want this to correspond to the original y axis values.
Response to edits
It's not clear what you mean by comparing two vectors. If one is a non-linear deformation of the other, then presumably you want to find the underlying function that produces the deformation. It is possible to create a function that applies the deformation empirically simply by doing f <- approxfun(untransformed_vector, transformed_vector).
I didn't say there were many ways of doing non-linear transformations. What I meant is that in your original example, there is no correspondence between the grid lines in the original picture and the second picture, so there is an infinite choice for which gridines in the first picture correspond to which gridlines in the second picture. There is therefore an infinite choice of mapping functions that could be specified.
The function f can be as complicated as you like, but in this scenario it should at least be everywhere non-decreasing, such that any value of the function's output can be mapped back to a single value of its input. For example, function(x) x + sin(x)/4 + cos(3*(x + 2))/5 would be a complex but ever-increasing sinusoidal function.
I am working on a script where I need to calculate the coordinates for a beeswarm plot without immediately plotting. When I use beeswarm, I get x-coordinates that aren't swarmed, and more or less the same value:
But if I generate the same plot again it swarms correctly:
And if I use dev.off() I again get no swarming:
The code I used:
n <- 250
df = data.frame(x = floor(runif(n, 0, 5)),
y = rnorm(n = n, mean = 500, sd = 100))
#Plot 1:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
#Plot 2:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
dev.off()
#Plot 3:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
It seems to me like beeswarm uses something like the current plot parameters (or however it is called) to do the swarming and therefore chokes when a plot isn't showing. I have tried to play around with beeswarm parameters such as spacing, breaks, corral, corralWidth, priority, and xlim, but it does not make a difference. FYI: If do.plot is set to TRUE the x-coordinates are calculated correctly, but this is not helpful as I don't want to plot immediately.
Any tips or comments are greatly appreciated!
You're right; beeswarm uses the current plot parameters to calculate the amount of space to leave between points. It seems that setting "do.plot=FALSE" does not do what one would expect, and I'm not sure why I included this parameter.
If you want to control the parameters manually, you could use the functions swarmx or swarmy instead. These functions must be applied to each group separately, e.g.
dfsplitswarmed <- by(df, df$x, function(aa) swarmx(aa$x, aa$y, xsize = 0.075, ysize = 7.5, cex = 1, log = ""))
dfswarmed <- do.call(rbind, dfsplitswarmed)
plot(dfswarmed)
In this case, I set the xsize and ysize values based on what the function would default to for this particular data set. If you can find a set of xsize/ysize values that work for your data, this approach might work for you.
Otherwise, perhaps a simpler approach would be to leave do.plot=TRUE, and then discard the plots.
I am having some trouble with a homework I have at Statistics.
I am required to graphical represent the density and the distribution function in two inline plots for a set of parameters at my choice ( there must be minimum 4 ) for Student, Fisher and ChiS repartitions.
Let's take only the example of Student Repartition.
From what I have searched on the internet, I have come with this:
First, I need to generate some random values.
x <- rnorm( 20, 0, 1 )
Question 1: I need to generate 4 of this?
Then I have to plot these values with:
plot(dt( x, df = 1))
plot(pt( x, df = 1))
But, how to do this for four set of parameters? They should be represented in the same plot.
Is this the good approach to what I came so far?
Please, tell me if I'm wrong.
To plot several densities of a certain distribution, you have to first have a support vector, in this case x below.
Then compute the values of the densities with the parameters of your choice.
Then plot them.
In the code that follows, I will plot 4 Sudent-t pdf's, with degrees of freedom 1 to 4.
x <- seq(-5, 5, by = 0.01) # The support vector
y <- sapply(1:4, function(d) dt(x, df = d))
# Open an empty plot first
plot(1, type = "n", xlim = c(-5, 5), ylim = c(0, 0.5))
for(i in 1:4){
lines(x, y[, i], col = i)
}
Then you can make the graph prettier, by adding a main title, changing the axis titles, etc.
If you want other distributions, such as the F or Chi-squared, you will use x strictly positive, for instance x <- seq(0.0001, 10, by = 0.01).
I tried to run the following script to change the x axis. I followed other answers given in Stack Overflow; however I don’t get what I’m looking for. The original x axis goes from 0 to 1 but I want to change it from 0 to 20.
x<-c(0,0.1,0.2,0.25,0.30,0.35,0.40,0.60,0.90,1)
y<-c(0,0.014,0.41165,0.258,0.57,0.57,0.1803,0.5844,0.10185,0.085)
da<-cbind(x,y)
dat=data.frame(da)
plot(y~x,data=dat,xaxt="n")
pas=c(0,1,2,3,4,5,6,7,8,10,11,12,13,14,15,16,17,18,19,20)
axis(1, at=1:20,labels=pas)
This script produces a figure with just a 0 at the last mark. I need a x axis from 0 to 20. Thanks
I am not sure why you are trying to do this but the following would work.
x <- c(0,0.1,0.2,0.25,0.30,0.35,0.40,0.60,0.90,1)
y <- c(0,0.014,0.41165,0.258,0.57,0.57,0.1803,0.5844,0.10185,0.085)
dat <- data.frame(x = x, y = y)
x.range <- c(0,20)
x.labels <- x.range[1]:x.range[2]
pas <- seq(0, 1, length.out = length(x.labels))
plot(y ~ x, data = dat, xaxt = "n", xlim = c(0,1))
axis(1, at = pas, labels = x.labels, cex.axis=0.65)
You may have missed the xlim/ylim.
Did you mean y axis? Then just use :
plot(y~x,data=dat,ylim=c(0,20))
plot(y~x,data=dat,xlim=c(0,20))# you need to remove the 'xaxt="n"' to see the x-axis
Although either way I am not sure this is the best idea given the range of the data.
Using ggplot2
library(ggplot2)
qplot(x,y,data=dat)+ scale_x_continuous(labels=c("0"="fake0","0.25"="fake5","0.5"="fake10",".75"="fake15","1"="fake20"))# replace 'fake#' with anything
Although I am not sure why are you trying to do this.
I am trying to write a function that will produce what I regard as a real dot plot (unlike the Cleveland variety, I require a univariate scatterplot with the dots stacked for (nearly) equal values). I have come close:
In this illustration, the dots you see are actually rotated text strings of lower-case "o"s. It is done this way because I need the dot spacing to stay constant if the plot is re-scaled. However, I'd like something better than lower-case "o"s, for example, filled dots instead of circles. This could be done if I could access the font that is used for the standard plotting symbols (pch = 1:25 in the plot function and relatives). Then I could make a text string with that font and get what's needed. Does anybody know how to do that?
PS - No, a histogram with lots of bins is not an acceptable substitute.
I did find a way to get the desired dot plot using low-level graphics parameters (namely "usr", the actual user coordinates of the plotting area, and "cxy", the character size). The recordGraphics() function wraps the part that needs to be changed when the graph is resized. Here's the function:
dot.plot = function(x, pch = 16, bins = 50, spacing = 1, xlab, ...) {
if(missing(xlab))
xlab = as.character(substitute(x))
# determine dot positions
inc = diff(pretty(x, n = bins)[1:2])
freq = table(inc * round(x / inc, 0))
xx = rep(as.numeric(names(freq)), freq)
yy = unlist(lapply(freq, seq_len))
# make the order of the dots the same as the order of the data
idx = seq_along(x)
idx[order(x)] = idx
xx = xx[idx]
yy = yy[idx]
# make a blank plot
plot(xx, yy, type = "n", axes = FALSE, xlab = xlab, ylab = "")
# draw scale
axis(1)
ylow = par("usr")[3]
abline(h = ylow) # extend to full width
# draw points and support resizing
recordGraphics({
yinc = 0.5 * spacing * par("cxy")[2]
points(xx, ylow + yinc * (yy - .5), pch = pch, ...)
},
list(),
environment(NULL))
invisible()
}
The spacing argument may be used if you want a tighter or looser gap between dots. An example...
with(iris, dot.plot(Sepal.Length, col = as.numeric(Species)))
This is a better solution than trying to do it with text, but also a little bit scary because of the warnings you see in the documentation for recordGraphics