I am using cpsd (https://cran.r-project.org/web/packages/gsignal/gsignal.pdf) to find the cross-spectral density between two signals. It seems that there is no way to define the frequencies I want to get the density for. E.g. in MATLAB I can do cpsd(x,y,window,noverlap,f,fs), where f=[2,10], so the output will be only two values. Is there an alternative for R?
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Given the plot of the time-series data, I was wondering if there is a robust function/mathematical formula I can use in R to determine which plots are oscillating. For example each individual graph corresponds to a single cell's intensity value over a certain time period. I would want a method to give a score or some value that would be able to differentiate between plots that are not oscillating (#513 and 559) compared to the plots that are oscillating (508,512,557,558). All the plots have the same scaling.
I have a matrix with 4 variables whereas 3 variables are parameters and the 4th variable gives the mean sum of squares for simulation results with the corresponding variables. Now I'd like to create a ternary plot with R where the triangle corresponding to the 3 parameter values should be colored by the mean sum of squares value. Alternatively, I'd like to plot interpolated mean sum of squares in the whole simplex triangle.
I was already looking for some functions or code that does what I'm looking for. But I didn't succeed.
Nevertheless, here's an example code of how my data set looks like (for which I'd like to create the ternary plot):
grid <- as.matrix(expand.grid(seq(0,0.5,0.025), seq(0,0.5,0.025), seq(-0.25,0.25,0.025)))
data <- cbind (grid, runif(9261,0,2))
I'd be very thankful if you'd provide R code that can create the plot I'd like to get. Maybe there's even a pre-implemented function in a package that I haven't found?!
Thanks a lot in advance for your help!
I am using the contour function from Julia's Plots to plot level curves. I want to extract a list of x coordinates and a list of y coordinates corresponding to the level curves from the plot, e.g., something like this. Is there a way to do it in Julia?
Not for contour, unfortunately. For most plot types you can extract the input data of, e.g. the first series in the first subplot, with p[1][1][:x]. But for contour in particular Plots does not generate the level curves, it simply passes the matrix to the backend that then does the computation and display.
I've just started learning R, and was wondering, say I have the dataset quake, and I want to generate the probability histogram of quakes near Fiji, would the code simply be hist(quakes$lat,freq=F)?
A histogram shows the frequency or proportion of a given value out of all the values in a data set. You need a numeric vector as the x argument for hist(). There is no flat variable in quakes, but there is a lat variable. hist(quakes$lat, freq = F) would show the following:
This shows the north/south geographical distribution of earthquakes, centering around -20, and, since it is approximately normal (with a left skew) suggests that there is a mechanism for earthquake generation that centers around a specific latitude.
The best way to learn is to try. If you wonder if that would be the way to do it, try it.
You might also want to look at this tutorial on creating kernel density plots with ggplot.
I need to get a plot of a Lorentz curve of a cumulative variable as a function of the number of observations. I want both axes to be displayed on a percentage basis (e.g. say observations are the number of buyers and the y variable is the amount they bought, buyers are already ranked in descending order, I want to get the plot that says "The top 10% buyers purchased 90% of the total bought"). My dataset is a couple million observations.
What is the best way to do this? Sub-questions:
If I need to add two variables for the quantiles of total observations and total $ bought (so as to use them to plot), what is the object that returns the row number? I tried:
user_quantile <- row(df)/nrow(df)
but I get a matrix of identical columns (user_quantile.1, user_quantile.2) of which I only need one column.
Is there instead any way to skip adding percentages as variables and only have them for axes values?
The plot has way to many points than I need to get the line. What is the best approach to minimize the computational effort and get a nice graph?
Thanks.
You may want to acquaint yourself with the excellent RSeek search engine for R content. One quick query for Lorentz curve (and Lorenz curve) lead to these packages:
ineq: Measuring inequality, concentration, and poverty
reldist: Relative Distribution Methods
GeoXp: Interactive exploratory spatial data analysis
lawstat: An R package for biostatistics, public policy and law
all of which seem to supply a Lorenz curve function.
In order to get the plot done you need first to arrange the raw data.
1) You can use the cut2() function from the Hmisc package to cut the data in quantiles. Check the documentation, it's not hard. It's similar to the cut() from the base package.
2) After using the cut2() function with the income data, you need to compute the frequency of each decile. Use table() for that. Then calculate percentages of income for each decile.
3) Now you should have a very small table with the following columns:
Decile, cumulative % of total income.
Add another column with the 45 degree line. Just add a constant cumulative % of income.
finaltable$cumulative_equality_line = seq(0.1, 1, by = 0.1)
4) You can use base graphics or ggplot2 for plotting. I guess you can do it with the info of step 3 or perhaps check out specific plotting questions.
I'll have to do it soon, but i already have the final table. I'll post the code for plotting once i do it.
Good luck!