I want to have spectral density of a set of values
x.spec<-spectrum(datalist[1:128,1],log=c("no"))
this code plot form me a plot. but I dont want to have any plot. Is there any way, that I can ignore this plot and having only spectral density
x.spec$spec
x.spec<-spectrum(datalist[1:128,1],log=c("dB"),plot=FALSE)
Related
Q-Q plot is a useful graphical device used to check for example normality of residuals. Q-Q plot is constructed by putting theoretical quantiles on x-axis and observed quantiles on the y-axis. In ggplot, this can be easily done using geom_qq and stat_qq. I would like to produce a wormplot, which is like a Q-Q plot, but on the y-axis, it has a difference between theoretical and observed quantiles (see the figure).
Is there a way to do this in ggplot? For example, is there a simple way to change the y-axis of the geom_qq to show the difference between theoretical and observed quantiles? I know it should be possible to calculate observed quantiles manually, but this would not work well if I would like to create plots of multiple groups or using facets, since then I would also need to calculate the observed quantiles manually for each group separately.
blogpost mentioned in comments contains a guide to code your own statfunctions to create such plots yourself
Otherwise, library qqplotr https://aloy.github.io/qqplotr/index.html contains an option detrend=True which basically produce wormplots with accompanying confidence bands.
If you want lines, and not a band, just do fill=NA, color='black', size=0.5
I have created a series of marginal plots looking at two variables using ggMarginal and ggplot2. However, I want to include an axis on the histogram that has the scale so I know roughly how many values fall into each bin.
I have already checked the documentation for ggMarginal and am at a loss.
I would like to plot a segment of an ROC curve over a specific range of x values, instead of plotting the entire curve. I don't want to change the range of the x axis itself. I just want to plot only part of the ROC curve, within a range of x values that I specify.
library(pROC)
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$wfns)
plot(rocobj)
That code plots the whole ROC curve. Let's say I just wanted to plot the curve from x=1 to x=.5. How could I do that? Thank you.
The default plot function for roc objects plots the rocobj$sensitivities as a function of rocobj$specificities.
So
plot(rocobj$specificities,rocobj$sensitivities,type="l",xlim=c(1.5,-0.5))
abline(1,-1)
achieves the same as
plot(rocobj)
And
plot(rocobj$specificities[2:6],rocobj$sensitivities[2:6],type="l",xlim=c(1.5,-0.5),ylim=c(0,1))
abline(1,-1)
Gets close to what I think you are after (plots from 0.514 to 1.0). I don't know enough about the package to know if the sensitivities can be calculated at a specific range, or resolution of specificities.
The plot function of pROC uses the usual R semantics for plotting, so you can use the xlim argument as you would for any other plot:
plot(rocobj, xlim = c(1, .5))
I have a density function f, and I do MCMC sampling for it. To evaluate the goodness of the sampling, I need to plot the hist and curve within the same chart. The problem of
hist(samples);
curve(dfun,add=TRUE);
is that they are on the different scale: the frequency of a certain bin is usually hundreds, while the maximum of a density function is about 1 or so. What I want to do is to configure two plots at the same height, with one y-axis on the left and the other on the right. Can anyone help? Thank you.
Use the prob=TRUE argument to hist:
hist(samples, prob=TRUE)
curve(dfun,add=TRUE)
Also see this SO question
This question already has an answer here:
How To Avoid Density Curve Getting Cut Off In Plot
(1 answer)
Closed 6 years ago.
newbie here. I have a script to create graphs that has a bit that goes something like this:
png(Test.png)
ht=hist(step[i],20)
curve(insert_function_here,add=TRUE)
I essentially want to plot a curve of a distribution over an histogram. My problem is that the axes limits are apparently set by the histogram instead of the curve, so that the curve sometimes gets out of the Y axis limits. I have played with par("usr"), to no avail. Is there any way to set the axis limits based on the maximum values of either the histogram or the curve (or, in the alternative, of the curve only)?? In case this changes anything, this needs to be done within a for loop where multiple such graphs are plotted and within a series of subplots (par("mfrow")).
Inspired by other answers, this is what i ended up doing:
curve(insert_function_here)
boundsc=par("usr")
ht=hist(A[,1],20,plot=FALSE)
par(usr=c(boundsc[1:2],0,max(boundsc[4],max(ht$counts))))
plot(ht,add=TRUE)
It fixes the bounds based on the highest of either the curve or the histogram.
You could determine the mx <- max(curve_vector, ht$counts) and set ylim=(0, mx), but I rather doubt the code looks like that since [] is not a proper parameter passing idiom and step is not an R plotting function, but rather a model selection function. So I am guessing this is code in Matlab or some other idiom. In R, try this:
set.seed(123)
png("Test.png")
ht=hist(rpois(20,1), plot=FALSE, breaks=0:10-0.1)
# better to offset to include discrete counts that would otherwise be at boundaries
plot(round(ht$breaks), dpois( round(ht$breaks), # plot a Poisson density
mean(ht$counts*round(ht$breaks[-length(ht$breaks)]))),
ylim=c(0, max(ht$density)+.1) , type="l")
plot(ht, freq=FALSE, add=TRUE) # plot the histogram
dev.off()
You could plot the curve first, then compute the histogram with plot=FALSE, and use the plot function on the histogram object with add=TRUE to add it to the plot.
Even better would be to calculate the the highest y-value of the curve (there may be shortcuts to do this depending on the nature of the curve) and the highest bar in the histogram and give this value to the ylim argument when plotting the histogram.