I want to make a phase plot like this https://en.wikipedia.org/wiki/Phase_portrait from an non-linear time series in R, Any ideas?
Thank you
you haven't given many details, but I suggest you look at the package phaseR.
I use it to draw isoclines and a flowfield of predator-prey models.
My graphs look like this (right one):
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.
Im looking for the name of the library capable of making this plot.
The forecasting bit is I'm what I'm interested. Is it Forecast library?
Thanks.
Yes, the time-series plot is from the forecast library. You get that sort of plot when you plot a forecast object. Look at ?forecast. For example:
install.packages('forecast')
library(forecast)
fit <- StructTS(WWWusage,"level")
plot(forecast(fit))
The rest of the fancy stuff i.e. interactivity is most likely shiny magic, but I think you are interested only in the main plot.
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'm trying to create an actuarial survival analysis in R (I'm following some worked examples). I think the best way to do this is using the survival package. So something like:
library(survival)
surv.test <- survfit(Surv(TIME,STATUS), data=test)
However, to get the correct answer I will need to divide the TIME variable into 365 day intervals and I can't quite work out how to do this so it matches the given result.
As far as I can make out, there is no option within the survfit function that will do this. I went through several document examples and none of them were trying to create a stairstep type of plot (there is a type='interval' option, but seems to do something different). So I guess I need to regroup my data before I apply the survival function?
Any ideas?
P.S: In SPSS this would be INTERVAL = THRU 10000 BY 365; in Stata intervals(365) ... connect(stairsteps)
I am guessing that you want to divide the TIME variable into intervals because you want to plot a Kaplan-Meier curve. In R, that isn't necessary, you can just call plot on the survfit object. For example,
s=survfit(Surv(futime, fustat)~rx, data=ovarian)
plot(s)
I think I understand your question a little better. The reason why you are getting a thick black line is because you have a lot of censoring, and a + is being plotted at every single point where there is censoring, you can turn this off with mark.time=F. (You can see other options in ?survival:::plot.survfit)
However, if you still want to aggregate by year, simply divide your follow up time by 365, and round up. ceiling is used to round up. Here is an example of aggregating at different time levels without censoring.
par(mfrow=c(1,3))
plot(survfit(Surv(ceiling(futime), fustat)~rx, data=ovarian),col=c('blue','red'),main='Day',mark.time=F)
plot(survfit(Surv(ceiling(futime/30), fustat)~rx, data=ovarian),col=c('blue','red'),main='Month',mark.time=F)
plot(survfit(Surv(ceiling(futime/365), fustat)~rx, data=ovarian),col=c('blue','red'),main='Year',mark.time=F)
par(mfrow=c(1,1))
But I think that plotting the Kaplan-Meier without the censoring symbols will look very nice, and provide more insight.
Hurray, I should be able to post the images now:
1) this is how the R basic survival plot looks like at the moment
2) and this is how it should look like (SPSS example)
That was exactly what I was missing! Thanks!
Solution:
vas.surv <- survfit(Surv(ceiling(TIME/365), STATUS)~1, conf.type="none", data=vasectomy)
plot(vas.surv, ylim=c(0.975,1), mark.time=F, xlab="Years", ylab="Cumulative Survival")
A nice touch would be to displays the days on the x-axis instead of the years (as in SPSS) example, but I'm not too bothered about this.
I would like to produce some nice PCA plots in R. As usual, in R, there are several ways to perform a principal component analysis. I found so far 3 different ways of how to calculate your components and 3 ways of plotting them. I was wondering whether people who are familiar with these functions can give me some advise on the best combination of functions to produce the following plots:
Scores Plot
Loadings Plot
Histogram / Bar chart of the variances explained by each principal component
My research on functions and plots used for PCA in R resulted in:
Functions:
pca.xzy()
prcomp()
princomp()
dudi.pca()
Plot:
plot.pca (this one seems to belong to the function pca.xzy())
ggplot2
plot
biplot
I also found the following webpage:
http://pbil.univ-lyon1.fr/ade4/ade4-html/dudi.pca.html
And I was wondering if you can draw those circles and lines starting from each of the circle centers with one of the other functions mentioned above as the function dudi.pca from the ade4 package seems to be the most complicated one.
One question per question, please! There's psych package by William Revelle, see this and this. There's also a good tutorial here. Anyway...
for scores/loadings plot see pairs
histogram: see hist
So once again, what's your question actually? =)