search for: magnitudes

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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 coordinates in R" but could not get helpful hints. I would be most grateful if anyone could advise me on where to start. Thank you. Ogbos [[alternative HT...

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:

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)

Hi, I have a couple of observations, a question or two, and perhaps a suggestion related to the plotting of density on the y-axis within the hist() function when freq=FALSE. I was using the function and trying to develop an intuitive understanding of what the density is telling me. After reading through this fairly helpful post:

...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 "siz...

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:

Hi, I have what I think is some kind of linear programming question. Basically, what I want to figure out 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

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

Hello All, I have a request from a prospectieve client to deploy a PBX capacity that can do up to 3000+ lines within a geographic region similar to a campus. The client wants analog lines for extensions and maybe VoIP for some backhaul traffic while 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

On 04/11/2017 10:20 PM, Daniel Nordlund wrote: > Tirthankar, > > "random number generators" do not produce random numbers. Any given > generator produces a fixed sequence of numbers that appear 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.

Hi, I was wondering if there is anything more efficient than split to do the kind of conversion in the subject. If I create a data frame as in system.time({fd = data.frame(x=1:2000, 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

I'm exploring latency overhead of parallel PSOCK workers and noticed that serializing/unserializing data back to the main R session is significantly slower on Linux than it is on Windows/MacOS with similar hardware. Is there a reason for this difference and is there a way to avoid the apparent additional Linux overhead? I attempted to isolate the behavior with a test that simply returns

We have a number of identical NFS clients mounting a server using NFSv4.1 - server and 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

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

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

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 is...

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 Bert On Feb 7, 2018 11:13 PM, "greg holly" <mak.hholly at