similar to: fitting cosine curve

Hi lily, You can get fairly good starting values just by eyeballing the curves: plot(y) lines(supsmu(1:20,y)) lines(0.6*cos((1:20)/3+0.6*pi)+17.2) Jim On Wed, Jun 21, 2017 at 9:17 AM, lily li <chocold12 at gmail.com> wrote: > Hi R users, > > I have a question about fitting a cosine curve. I don't know how to set the > approximate starting values. Besides, does the method

Using a more stable nonlinear modeling tool will also help, but key is to get the periodicity right. y=c(16.82, 16.72, 16.63, 16.47, 16.84, 16.25, 16.15, 16.83, 17.41, 17.67, 17.62, 17.81, 17.91, 17.85, 17.70, 17.67, 17.45, 17.58, 16.99, 17.10) t=c(7, 37, 58, 79, 96, 110, 114, 127, 146, 156, 161, 169, 176, 182, 190, 197, 209, 218, 232, 240) lidata <- data.frame(y=y, t=t) #I use the

If you know the period and want to fit phase and amplitude, this is equivalent to fitting a * sin + b * cos > >>> > I don't know how to set the approximate starting values. I'm not sure what you meant by that, but I suspect it's related to phase and amplitude. > >>> > Besides, does the method work for sine curve as well? sin is the same as cos with

I'm trying the different parameters, but don't know what the error is: Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates Thanks for any suggestions. On Tue, Jun 20, 2017 at 7:37 PM, Don Cohen <don-r-help at isis.cs3-inc.com> wrote: > > If you know the period and want to fit phase and amplitude, this is > equivalent to

What I did was to plot your initial values, then plot the smoothed values and guess the constants. That is, I got an "eyeball" fit to the smoothed values. As I have described this as "gross cheating" in the past, you should either split your data, estimate on one subset and then test on another, or estimate on your data and test on a replication. If you get pretty much the same

I am trying to use lm for a simple linear fit with weights. The results I get from IDL (which I am more familiar with) seem correct and intuitive, but the "lm" function in R gives outputs that seem strange to me. Unweighted case: > x<-1:4 > y<-(1:4)^2 > summary(lm(y~x)) Call: lm(formula = y ~ x) Residuals: 1 2 3 4 1 -1 -1 1 Coefficients:

Dear R users: Recently, I learn to use penalized logistic regression. Two packages (penalized and glmnet) have the function of lasso. So I write these code. However, I got different results of coef. Can someone kindly explain. # lasso using penalized library(penalized) pena.fit2<-penalized(HRLNM,penalized=~CN+NoSus,lambda1=1,model="logistic",standardize=TRUE) pena.fit2

Olá pessoal, estou realizando uma ANOVA com medidas repetidas e estou utilizando a função "Anova" do pacote "car". Medi o biovolume de algas a cada dois dias durante 10 dias (no banco de dados abaixo só coloquei até o 4° dia). Tenho 2 tratamentos ("c","t") e o experimento foi realizado em tréplicas ("A","B","C"). > Pa2

I would like to fit regression models of the form y ~ ns(x, df=splineDF) where splineDF is passed as an argument to a wrapper function. This works fine if the regression function is lm(). But with lme(), I get two different errors, depending on how I handle splineDF inside the wrapper function. A workaround is to turn the lme() command, along with the appropriate value of splineDF, into a text

I would like to fit regression models of the form y ~ ns(x, df=splineDF) where splineDF is passed as an argument to a wrapper function. This works fine if the regression function is lm(). But with lme(), I get two different errors, depending on how I handle splineDF inside the wrapper function. A workaround is to turn the lme() command, along with the appropriate value of splineDF, into a text

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

I'm in teaching mode, kernel densities. {History: density() was newly introduced in version 0.15, 19 Dec 1996; most probably by Ross or Robert } When I was telling the students about different kernels (and why their choice is not so important, and "equivalent bandwidths" etc,etc) I wondered about the "Cosine" in my teaching notes which is defined there as k(x)

I am trying to learn how to use nlme by working on a simple example. I attach the data from a toy example I made up which is similar to my real problem. (My grasp of fixed/random effects is still a bit tenuous) It is a longitudinal study of the effect of two treatments: A and B. The data were created by: A: y<-12/(1+exp((2-time)/.5)),y<-8/(1+exp((2-time)/.5)) B:

Hello, a. is there a possibility to obtain a distance matrix with the cosine between vectors?? hclust, hierclust, dist will not work and seem to be hard to extend. b. if there is not: Is the cosine between vectors implemented somewhere? Thanks for all hints and advice! Petra Steiner -------------- next part -------------- An HTML attachment was scrubbed... URL:

does anybody have a suggestion as to how to use R to fit some date to a cosine function and then have some output statistics that will evaluate the fit? -- View this message in context: http://r.789695.n4.nabble.com/cosine-curve-fit-tp4650866.html Sent from the R help mailing list archive at Nabble.com.

Dear all, below you find enclosed my message from January 9th and my program (attention: beginner). Thanks for both answers! a. However, as far as I know the cosine is not the same as the Pearson correlation (only in special cases). b. Reid Huntsinger's hint was very useful, however I had to transpose the matrix first, for I want to calculate the distance of the _rows_. Regards, Petra

I am trying to learn something of the algorithms used in Vorbis. One of them is the "Modified Discrete Cosine Transform" (MDCT). I have found a couple of books with brief descriptions of the DCT, but almost nothing about MDCT. (1) is there just one Modified DCT, which can be expected to be the same whenever someone uses that name, or does it just mean that you have changed

Hi everybody! I have intended to use library(lsa) on R 64-bits for Windows but it was not possible. Every time I try to launch library(lsa) function R give me back next message: Loading required package: SnowballError : .onLoad failed in loadNamespace() for 'Snowball', details: call: NULL error: .onLoad failed in loadNamespace() for 'rJava', details: call: stop("No

Excuse me if this is has been discussed before, but, How can I make the cos(pi/2) = 0 (instead of on my machine 6.123234e-17) and the sin(pi) = 0 (instead of 1.224647e-16 on my machine) ? I'd like sin(pi) == 0 to return TRUE, and is zapsmall appropriate here? -------------- next part -------------- A non-text attachment was scrubbed... Name: brian.vcf Type: text/x-vcard Size: 411 bytes Desc: