I would like to make a graph of the quantile function of the standard normal distribution using R. I use the command
plot(qnorm,ylim=c(-4,4))
and I get this graph
However, the graph that I get is truncated at around $-2$ and around $2$. I would like to see something like this picture
The last picture was generated using the command
plot(1:99999/10^5,qnorm(1:99999/10^5),type="l")
but it is inefficient and slow.
Why is R making the truncated graph? What command should I use to make a graph without truncation?
Any help is much appreciated!
This should be sufficient:
plot(qnorm,from=pnorm(-4),to=pnorm(4))
If you'd like it a bit smoother where the radius of curvature is small, I'd suggest increasing the n parameter a little, e.g.:
plot(qnorm,from=pnorm(-4),to=pnorm(4),n=201)
see ?plot.function and ?curve
Related
I have a plotting problem with curves when using mixtools
Using the following R code
require(mixtools)
x <- c(rnorm(10000,8,2),rnorm(10000,18,5))
xMix <- normalmixEM(x, lambda=NULL, mu=NULL, sigma=NULL)
plot(xMix, which = 2, nclass=25)
I get a nice histogram, with the 2 normal curves estimated from the model superimposed.
The problem is with the default colours (i.e. red and green), which I need to change for a publication to be black and grey.
One way I thought to doing this was first to produce the histogram
hist(xMix$x, freq=FALSE, nclass=25)
and then add the lines using the "curve" function.
....... but I lost my way, and couldn't solve it
I would be grateful for any pointers or the actual solution
thanks
PS. Note that there is an alternative work-around to this problem using ggplot:
Any suggestions for how I can plot mixEM type data using ggplot2
but for various reasons I need to keep using the base graphics
You can also edit the colours directly using the col2 argument in the mixtools plotting function
For example
plot(xMix, which = 2, nclass=25, col2=c("dimgrey","black"))
giving the problem a bit more thought, I managed to rephrase the problem and ask the question in a much more direct way
Using user-defined functions within "curve" function in R graphics
this delivered two nice solutions of how to use the "curve" function to draw normal distributions produced by the mixture modelling.
the overall answer therefore is to use the "hist" function to draw a histogram of the raw data, then the "curve" function (incorporating the sdnorm function) to draw each normal distribution. This gives total control of the colours (and potentially any other graphic parameter).
And not to forget - this is where I got the code for the sdnorm function - and other useful insights
Any suggestions for how I can plot mixEM type data using ggplot2
Thanks as always to StackOverflow and the contributors who provide such helpful advice.
I found this graphical intuitive explanation of covariance:
32 binormal points drawn from distributions with the given covariances, ordered from most negative (bluest) to most positive (reddest)
The whole material can be found at:
https://stats.stackexchange.com/questions/18058/how-would-you-explain-covariance-to-someone-who-understands-only-the-mean
I would like to recreate this sort of graphical illustration in R, but I'm not sufficiently familiar with R's plotting tools. I don't even know where to start in order to get those colored rectangles between each pair of data points, let alone make them semi-transparent.
I think this could make a very efficient teaching tool.
The cor.rect.plot function in the TeachingDemos package makes plots similar to what is shown. You can modify the code for the function to make the plot even more similar if you desire.
I am trying to make the following plot (which I made in R) using ggplot2. How do I go about doing this? (The plot has a finer resolution from 0 to 10, but coarser resolution from 10 onwards.) know how to do this in R, but I am not sure how to proceed with this in the case of ggplot2. Here is the figure obtained using base R.
To make things useful, note that i don't have that much interest in hanging on to the right-hand y-axis.
Also, I would like to do a similar task on a pair of barplots (have the same scale, and only one y-axis. I am hoping that that approach is similar.
I would also be open to other suggestions that would make a similar plot conveying the information similarly or better.
Many thanks for suggestions!
I have the following plot:
plot.ts(returns)
I have another dataframe ma_sd which contains the rolling SD from moving averages of the above returns. The df is structured exactly like returns. Is there a simple way to add each line to the corresponding plots?
lines(1:N, ma_sd) seemed intuitive, but it does not work.
Thanks
The only way I can see you doing this is to plot them separately. This code is a bit clunky but will allow you full flexibility to be able to specify labels and axis ranges. You can build on this.
par(mfrow=c(3,1),oma=c(5,4,4,2),mar=c(0,0,0,0))
time<-as.data.frame(matrix(c(1:length(returns[,1])),length(returns[,1]),3))
plot(time[,1],returns[,1],type='l',xaxt='n')
points(time[,1],ma_sd[,1],type='l',col='red')
plot(time[,2],returns[,2],type='l',xaxt='n')
points(time[,2],ma_sd[,2],type='l',col='red')
plot(time[,3],returns[,3],type='l')
points(time[,3],ma_sd[,3],type='l',col='red')
When plotting oscillations in R, e.g., using the package desolve,
df1 <-function(t,y,mu)( list(c(y[2],mu*y[1]^3-y[1]+0.005*cos(t))))
library (deSolve)
yini<-c(y1=0,y2=0)
df2 <-ode(y=yini,func=df1, times=0:520,parms=0.1667)
plot(df2,type="l",which="y1",ylab="Displacement",xlab="Time", main="")
I get raggedy plots such as:
instead of a smooth plot (not done in R) such as:
Does anyone know of a way to obtain a smoother plot in R instead of a raggedy one when displaying oscillations? Note that it is not just a matter of the difference in scale and I am not looking for a smoothing filter.
Thanks,
I generated your plot in R and exported it as PDF. I zoomed in on it and it's quite lovely. I can't see the problem you're talking about there. Therefore, there are some scaling issues or something with a raster format that are causing the issue. Perhaps you're pasting into Word and that's giving you a raster image that's bad. The plot that R is making, at a logical level, is great in spite of the one you posted. It's even better than the comparison plot you put up.
It's possible that you're generating the plot in a raster format and not setting a high enough resolution and size. Try tiff('filname', 1200, 1200, 300) for a good raster image of it. I did notice that when exporting to raster formats it was easy to make your plot into a fine mess with default png or jpg settings that would just smear things.
Maybe you really wanted to sample in your function at a higher resolution, something not done in the comparison plot. If that's the case then it's relatively easy. Change 0:520 to seq(0, 520, 0.1). That's an even nicer plot, as shown below (much better than shown as PDF, EPS, or SVG).