Displaying 20 results from an estimated 20000 matches similar to: "1D and 2D gaussian fittings"
2012 Mar 19
1
fitting a histogram to a Gaussian curve
Hello,
I am trying to fit my histogram to a smooth Gaussian curve(the data
closely resembles one except a few bars).
This is my code :
#!/usr/bin/Rscript
out_file = "irc_20M_opencl_test.png"
png(out_file)
scan("my.csv") -> myvals
hist(myvals, breaks = 50, main = "My Distribution",xlab = "My Values")
pdens <- density(myvals, na.rm=T)
plot(pdens,
2011 Feb 08
1
Simulation of Multivariate Fractional Gaussian Noise and Fractional Brownian Motion
Dear R Helpers,
I have searched for any R package or code for simulating multivariate
fractional Brownian motion (mFBM) or multivariate fractional Gaussian noise
(mFGN) when a covariance matrix are given. Unfortunately, I could not find
such a package or code.
Can you suggest any solution for multivariate FBM and FGN simulation? Thank
you for your help.
Best Regards,
Ryan
-----
Wonsang You
2009 Feb 24
2
Syntax in taking log to transfrom the data to fit Gaussian distribution
Hi,
I have a data set (weight) that does not follow the Gaussian (Normal)
distribution. However, I have to transform the data before applying the
Gaussian distribution. I used this syntax and used log(weight) as:
posJy.model<-glm(log(weight) ~ factor(pos),
family=gaussian(link='identity'), subset=Soil=="Jy"). This syntax COULD NOT
transform the data. But if I transform the
2011 Dec 07
1
How to fit the log Gaussian Cox process model
Hi,
As far as I know, there exist some programs via the function INLA,
but I'm so curious if there is a specific function directly used to fit the
log Gaussian Cox process model and
predict the latent Gaussian field. That is, if I have a data points, then I
input it in the function and
don't need to revise the program. Thanks for your help.
Joseph
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2006 Oct 06
2
Fitting a cumulative gaussian
Dear R-Experts,
I was wondering how to fit a cumulative gaussian to a set of empirical
data using R. On the R website as well as in the mail archives, I found
a lot of help on how to fit a normal density function to empirical data,
but unfortunately no advice on how to obtain reasonable estimates of m
and sd for a gaussian ogive function.
Specifically, I have data from a psychometric function
2005 Aug 26
2
Fitting data to gaussian distributions
Hi!
I need to fit a data that shows up as two gaussians partially
superimposed to the corresponding gaussian distributions, i.e.
data=c(rnorm(100,5,2),rnorm(100,-6,1))
I figured it out how to do it with mle or fitdistr when only one
gaussian is necessary, but not with two or more. Is there a function in
R to do this?
Thank you very much in advance,
Luis
2011 Mar 14
0
Fitting 4 moments distribution w/ Mixture Gaussian
Hello,
I know that Mclust does the fitting on its own but I am trying to implement
an optimization with the aim to generate a the mixture gaussian with the
combine moments as closed as possible to the moment of my return
distribution.
The objective is to Min Abs((Mean Ret - MeanFit)/Mean Fit) + Abs((Std Ret
-Stdev Fit)/Stdev) + Abs((Sk Ret-Sk fit)/Sk Fit) + Abs((Kurt Ret- Kurt Fit))
Taking
2008 Nov 12
1
Getting parameters of Gaussian fit in density
Is there a way to obtain the parameters (mean, sd, amplitude) of the
gaussian functions obtained in a density fit to data. The faithful
$waiting times is a standard example. The 2-gaussian fit is very nice,
but how can I obtain the parameters?
Thanks for your help.
Regards,
Victor Bloomfield
2005 Jan 07
0
Help in customising the NLS function to spit out mean and SD ofnew fit!!
Doesn't look like nonparametric fit to me, since nls() is used to fit to a
gaussian density, so the result is a gaussian density (with estimated
parameter).
What I do not understand is why people would do this. This is not the first
time I've seen people doing this, on both R-help and S-news (if my memory is
still any good). If the objective is to fit a Gaussian distribution to the
2006 Aug 25
4
fitting a gaussian to some x,y data
I apologize if this is redundant. I've been Googling, searching the
archive and reading the help all morning and I am not getting closer
to my goal.
I have a series of data( xi, yi). It is not evenly sampled and it is
messy (meaning that there is a lot of scatter in the data). I want to
fit a normal distribution (i.e. a gaussian) to the data in order to
find the center. (The data
2007 Dec 13
1
Gaussian Smoothing
Hello,
I'm new to R, and I would like to know if there is a way to smooth a curve
using a Gaussian smoothing with R.
Thank you very much,
TDB
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2013 Feb 22
2
Fitting this data with a gaussian would be great
Hello,I'm still working with this data set, and trying to fit it with a nonlinear model. Here is my data
> small <- c(507680,507670,508832,510184,511272,513380,515828,519160,525046,534046,547982,567124,590208,614506,637876,656846,669054,672976,668800,656070,637136,614342,590970,570752,554480,542882,535630,531276,528682,527682,527020,526834,526802,526860)
test <- glm(dnorm(x),
2004 Jul 29
3
fitting gaussian mixtures
Hi R-helpers,
I'm trying to model a univariate as a bi-modal normal mixtures. I need to estimate the parameters of each gaussian (mean and sd) and their weights. What's the best way to do this in R?
Thanks,
Xiao-Jun
2006 Feb 28
1
ex-Gaussian survival distribution
Dear R-Helpers,
I am hoping to perform survival analyses using the "ex-Gaussian"
distribution.
I understand that the ex-Gaussian is a convolution of exponential and
Gaussian
distributions for survival data.
I checked the "survreg.distributions" help and saw that it is possible to
mix
pre-defined distributions. Am I correct to think that the following code
makes
the
2009 Dec 06
3
estimate inverse gaussian in R
I have a one-variable data set in R.
The plot of histogram of my numerical variable suggests an inverse
gaussian distribution.
How can I obtain best estimation for the two parameters of inverse
gaussian based on my data?
Thanks.
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2006 Mar 16
1
running median and smoothing splines for robust surface f itting
loess() should be able to do robust 2D smoothing.
There's no natural ordering in 2D, so defining running medians can be
tricky. I seem to recall Prof. Koenker talked about some robust 2D
smoothing method at useR! 2004, but can't remember if it's available in some
packages.
Andy
From: Vladislav Petyuk
>
> Hi,
> Are there any multidimenstional versions of runmed() and
>
2004 Oct 21
1
inverse gaussian distribution of frailty variable
Hello,
I'm Emanuela, I'm implemented a survival analysis and I'm trying to use a frailty model with inverse gaussian distribution, but I'm not able to find the right code, because it seems to be only a gamma and a gaussian distribution. Is there also the inverse gaussian distribution?
Thanks a lot
Emanuela Rossi
2011 Feb 07
1
tri-cube and gaussian weights in loess
>From what I understand, loess in R uses the standard tri-cube function.
SAS/INSIGHT offers loess with Gaussian weights. Is there a function in R
that does the same?
Also, can anyone offer any references comparing properties between tri-cube
and Gaussian weights in LOESS?
Thanks. - Andr?
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2006 May 11
1
Simulating scalar-valued stationary Gaussian processes
Hi,
I have a sample of size 100 from a function in interval [0,1] which can be
assumed to come from a scalar-valued stationary Gaussian process. There are
about 500 observation points in the interval. I need an effective and fast
way to simulate from the Gaussian process conditioned on the available data.
I can of course estimate the mean and 500x500 covariance matrix from data.
I have searched
2008 Jun 12
0
using MCLUST package to estimate a poisson-gaussian process
Hi All,
I am using em() function to estimate a poisson-gaussian process from a
univariate one dimension time series, but not sure how to do. In the help
manual, it specify that in "pro" of the argument "parameter", if the model
includes a Poisson term for noise, there should be one more mixing
proportion than the number of Gaussian components. But in the example, the
parameter