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Hi, I'm in dire need of a fast fourier transformation for me stupid biologist, i.e. I have a heartbeat signal and would like to decompose it into pure sin waves, getting three vectors, one containing the frequencies of the sin waves, one the magnitudes and one the phases (that's what I get from my data acquisition software's FFT function). I'd be very much obliged, if someone could point out which command would do the job in R. Thanks! Wolfgang

Greetings to you all. I have two datasets - Time and magnitude. For a particular location, the magnitude of the parameter varies with time. I wish to obtain a polar coordinate distribution of time (0-24h) and magnitudes so as to visualize how magnitude varies with different times of the day (e.g., morning, midnight hours). I have searched for "polar coord...

Greetings! I am still new to R but have been asked to look at doing astronomy with R. I have a FITS file which contains an optical telescope image (it can be viewed in SAOimageDS9). I need to estimate the magnitude of a galaxy... and eventually other optical sources. How do I find the apparent magnitude of a galaxy from a FITS file? -- View this message in context: http://r.789695.n4.nabble.com/How-do-I-find-the-apparent-magnitude-of-a-galaxy-from-a-FITS-file-tp4639907.html Sent from the R help mailing li...

Hi- I am trying to find a way to diffuse GIS data on a European map. I have a dataset consisting of particular locations scattered across Europe, along with magnitude and value information. I can plot these as discrete points with something like the following: "geocode" is a dataframe with four columns: LAT; LONG; MAGNITUDE;VALUE. library(maps) library(mapdata) map("worldHires", regions=c("Germany", "Belgium", "Neth...

...rom hist.default: dens <- counts/(n * diff(breaks)) So the count in each bin is divided by the total number of observations (n) multiplied by the size of the bin. The problem, as I see it, is that the density ends up being scaled by the size of the bins, which is inversely proportional to the magnitude of the data. Therefore the magnitude of the data is directly affecting the density, which seems problematic. For example*: set.seed(4444) x <- runif(100) y <- x / 1000 par(mfrow = c(2, 1)) hist(x, prob = TRUE) hist(y, prob = TRUE) >From this example, you see that the density for the y...

...question about the proper use of the typsize and fscale arguments to nlm. I use nlm in my sem package to fit general structural-equation models, which entails maximizing a multinormal likelihood with respect to parameters that represent regression coefficients and covariances of variables. The magnitudes of these parameters can be very different. The documentation for typsize is rather terse: "an estimate of the size of each parameter at the minimum," with the default typsize=rep(1, length(p)) [where length(p) gives the number of parameters]. Am I correct in interpreting the "si...

Hello, I am analyzing some climate time series data using the Mann Kendall package and was wondering if there was a way to calculate the trend using Sen's nonparametric estimator slope in R? Thank you in advance, Barry _________________________ Barry Baker, Ph.D. Global Climate Change Initiative The Nature Conservancy 2424 Spruce St., Suite 100 Boulder, CO 80302 Tel: (303)-541-0322 Fax:

...ut is if I have a vector of numbers, > x <- rnorm(10) > x [1] -0.44305959 -0.26707077 0.07121266 0.44123714 -1.10323616 -0.19712807 0.20679494 -0.98629992 0.97191659 -0.77561593 > mean(x) [1] -0.2081249 Using each number only once, I want to find the set of five pairs where the magnitude of the differences between the mean(x) and each pairs sum is least. > y <- outer(x, x, "+") - (2 * mean(x)) > y [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] -0.46986936 -0.29388054 0.04440289 0.4144273...

I have a need to find the order of number to get a scaling parameter as a power of 10. I have a function that works *so far*, but it is ugly and probably buggy. In the interest of avoiding code-based outliers in my data, I thought I would ask if anyone here has a better way. > scl <- function(x){ + length(charToRaw(format(trunc(x), scientific = F)))-1} > a <- 123456789 > b <-

In the precompute_partition_info_sums_ function, instead of selecting 64-bit accumulator when the signal bps is larger than 16, revert to the original approach based on partition size, but make room for few extra bits to not overflow with unusual signals where the average residual magnitude may be larger than bps. It slightly improves the performance with standard encoding levels and 16-bit files as the 17-bit side channel can still be processed with the 32-bit accumulator and correctly selects the 64-bit accumulator with very large 16-bit partitions. This is related to commits 6f7e...

...the other traffic would be carrid via E1 channels. The client has other proposals to buy a mid, range telco switch from alcatel or simens but i am trying to convince him otherwise. Though i have deployed Asterisk PBX in mid range offices (like up to 60 lines), i have never deployed anything of this magnitude. Can anyone help me by giving me information on how this sort of deployment would work, keeping in mind that the extensions would all be analog and not VOIP. I have seen some documentation on SpiderMUX (TDMoE) and some other channelized MUXes using USB interfaces but then, how would i even start to...

...meet > various tests of randomness. By picking a seed you enter that sequence > in a particular place and subsequent numbers in the sequence appear to > be unrelated. There are no guarantees that if YOU pick a SET of seeds > they won't produce a set of values that are of a similar magnitude. > > You can likely solve your problem by following Radford Neal's advice of > not using the the first number from each seed. However, you don't need > to use anything more than the second number. So, you can modify your > function as follows: > > function(x) { >...

...:2000, y = rnorm(2000), id = paste("x", 1:2000, sep =""))}) user system elapsed 0.004 0.000 0.004 and then I try to split it > system.time(split(fd, 1:nrow(fd))) user system elapsed 0.333 0.031 0.415 You will be quick to notice the roughly two orders of magnitude difference in time between creation and conversion. Granted, it's not written anywhere that they should be similar but the latter seems interpreter-slow to me (split is implemented with a lapply in the data frame case) There is also a memory issue when I hit about 20000 elements (allocating 3GB...

...E) cl <- makeCluster(1) (x <- microbenchmark(clusterEvalQ(cl, iris), times = 1000, unit = "us")) plot(x$time, ylab = "microseconds") head(x$time, n = 10) On Windows/MacOS, the test runs in 300-500 microseconds depending on hardware. A few of the 1000 runs are an order of magnitude slower but this can probably be attributed to garbage collection on the worker. On Linux, the first 5 or so executions run at comparable speeds but all subsequent executions are two orders of magnitude slower (~40 milliseconds). I see this behavior across various platforms and hardware combina...

...nd clients are all running CentOS 7.5 (kernel 3.10.0-862.14.4.el7.x86_64) However, on some clients, the NFS performance 'degrades' with time ... Running a simple test - a python script that just imports a module (python and its modules are installed on the NFS share) can be an order of magnitude or more slower on some clients. i.e. very little data is transferred, it is the rate of stat'ing and opening files on the NFS server that is 'slow' Running a tcpdump on a 'slow' client shows that the NFS traffic generated on the 'slow' client is again an order of mag...

I have prices that I am finding difficult to compare with ==, > and >, due to precision. For example: the numbers should match, with '==', but they differ in the magnitude of 1e-14 due to bunch of calculations that I run on them. Programming with java, I am used to implementing a function that compares the difference between the numbers to a pre determined precision factor. This could be very slow when I have two matrices of numbers that I could otherwise compare wi...

Hi, All. I have a logistic regression model that I have run. The question came up: which of these regressors is more important than another? (I'm using Design) Logistic Regression Model lrm(formula = iconicgesture ~ ST + SSP + magnitude + Condition + Expertise, data = d) Coef S.E. Wald Z P Intercept -3.2688 0.2854 -11.45 0.0000 ST 2.0871 0.2730 7.64 0.0000 SSP 0.7454 0.3031 2.46 0.0139 magnitude -0.9905 0.6284 -1.58 0.1150 Condition 0.9506 0.2932 3.24 0.0012 Expertise 0.8508 0.2654...

Hi all, I am trying to produce some panels with dots in an X/Y plane where the diameter of the dots indicates a Z value (like e.g. earthquake maps where dot sizes indicate magnitudes and X/Y the location). This works fine with xyplot, e.g.: xyplot(1:3~1:3,cex=1:3,pch=16) However, when I do this with a panel variable, e.g.: x<-rep(1:3,5) y <- rep(1:3,5) sz <- rep(1:5,each=3) grp <- factor(rep(1:5,each=3)) xyplot(y~x | grp , cex=sz) then sz in the cex argument i...

Fwiw, encoding magnitude in color is generally a bad idea. Using area(*not* radius) is also not great, but maybe it will work for you. See here for some explanation: https://www.amazon.com/Visual-Display-Quantitative-Information/dp/0961392142/ref=sr_1_1?s=books&ie=UTF8&qid=1518092778&sr=1-1&keywords=Tufte...