Hi all,
I am trying to fit a gam using mgcv which has a mix of smooth and parametric
terms. The model is for some count data on fish catches. I am modelling
variation in location and time, but also differences among individual
operators. I am interested in the differences in operators specif
gam, fpar, fmixgam))
>
> -- Forwarded message -
> From: Charles Thuo
> Date: Wed, Oct 21, 2020 at 12:03 AM
> Subject: [R] Fitting Mixed Distributions in the fitdistrplus package
> To:
>
>
> Dear Sirs,
>
> The below listed code fits a gamma and a pare
dmixgampar <- function (x, param1, param2, ...)
{
#compute density at x
}
On Wed, Oct 21, 2020 at 8:03 PM Charles Thuo wrote:
>
> Dear Sirs,
>
> The below listed code fits a gamma and a pareto distribution to a data set
> danishuni. However the distributions are not appropriate
Dear Sirs,
The below listed code fits a gamma and a pareto distribution to a data set
danishuni. However the distributions are not appropriate to fit both tails
of the data set hence a mixed distribution is required which has ben
defined as "mixgampar"
as shown below.
library(fitdistrplus)
x<-
I have been using nlsr() to fit s curves to Covid-19 data over the past few
weeks and I have not had any issues.
Bernard
Sent from my iPhone so please excuse the spelling!"
> On May 13, 2020, at 5:16 PM, Abby Spurdle wrote:
>
> Hi Christofer,
>
> This doesn't really answer your question.
> B
> It's possible that Martin's package, cobs, can do this, but not sure,
> I haven't tried it.
> And there may be other R packages for fitting splines/smoothers to
> data, subject to shape constraints.
Further to my previous post.
I read through the documentation for the cobs package.
And (someone
Hi Christofer,
This doesn't really answer your question.
But if the goal is to fit an S-shaped curve to data, with increased
flexibility...
(I'm assuming that's the goal).
...then I'd like to note the option of splines (or smoothing), subject
to shape constraints...
My guess, is it's probably ea
Many moons ago (I think early 80s) I looked at some of the global optimizers,
including several based on intervals. For problems of this size, your suggestion
makes a lot of sense, though it has been so long since I looked at those
techniques
that I will avoid detailed comment.
I've not looked to
Also, in the full curve referenced on Wikpedia, the parameters Q And M are
confounded - you only need one or the other But not both. If you are using both
and trying to estimate them both you will have problems.
I have fitted these curves quite easily using the Solver in Excel.
Bernard
Sent fro
John, have you ever looked at interval optimization as an alternative since it
can lead to provably global minima?
Bernard
Sent from my iPhone so please excuse the spelling!"
> On May 13, 2020, at 8:42 AM, J C Nash wrote:
>
> The Richards' curve is analytic, so nlsr::nlxb() should work better
The Richards' curve is analytic, so nlsr::nlxb() should work better than nls()
for getting derivatives --
the dreaded "singular gradient" error will likely stop nls(). Also likely,
since even a 3-parameter
logistic can suffer from it (my long-standing Hobbs weed infestation problem
below), is
th
Shouldn't be hard to set up with nls(). (I kind of suspect that the Richards
curve has more flexibility than data can resolve, especially the subset
(Q,B,nu) seems highly related, but hey, it's your data...)
-pd
> On 13 May 2020, at 11:26 , Christofer Bogaso
> wrote:
>
> Hi,
>
> Is there a
Hi Christofer
Try FlexParamCurve or maybe drc package.
Cheers
Petr
> -Original Message-
> From: R-help On Behalf Of Christofer Bogaso
> Sent: Wednesday, May 13, 2020 11:26 AM
> To: r-help
> Subject: [R] Fitting Richards' curve
>
> Hi,
>
> Is the
Hi,
Is there any R package to fit Richards' curve in the form of
https://en.wikipedia.org/wiki/Generalised_logistic_function
I found there is one package grofit, but currently defunct.
Any pointer appreciated.
__
R-help@r-project.org mailing list -- T
Hi, i trying to extend the functional autoregressive model one FAR(1) to fit
the functional autoregressive model of order two FAR(2). the coding i do for
far(1) is library(fda)library(far)# CREATE DUMMY
VARIABLESfactor2dummy=function(x){ n=length(x) tab=as.factor(names(table(x)))
p=length(t
This list doesn't do statistics -- it does R programming, though statistics
does occur incidentally sometimes in that context. Not in your post
though. You should post on a statistics site like stats.stackexchange.com
for statistics questions.
Cheers,
Bert
Bert Gunter
"The trouble with having an
Hi All,
By a production curve I mean for example the output of a mine, peak oil
production or the yield of a farm over time within the same season. It is this
last example that we should take as the prototypical case.
What I would like to do is to fit a curve that inherits qualities of the
dis
Dear All,
I got a warning message "X matrix deemed to be singular" in Cox model with
a time dependent coefficient. In my analysis, the variable "SEX" is a
categorical variable which violate the PH assumption in Cox. I first used
the survSplit() function to break the data set into different time
i
Dear all,
I'm interested in fitting survival trees with competing risk analysis.
The tree should show the cumulative incidence function for each terminal
node .
I read several paper illustrating this possibility, but to the best of my
knowledge no R code are reported.
There is any R pa
Stop right there and rethink! The normalization factor depends on the parameter
that you are maximizing over.
-pd
> On 21 Dec 2017, at 11:29 , Lorenzo Isella wrote:
>
> In the code, dbeta1 is the density of the beta distribution for
> shape1=shape2=shape.
> In the code, dbeta2 is the same quan
I answer my own question: I had overlooked the fact that the normalization
factor is also a function of the parameters I want to optimise, hence I
should write
dbeta2 <- function(x, shape){
res <- x^(shape-1)*(1-x)^(shape-1)/beta(shape, shape)
return(res)
}
after which the results ar
Dear All,
I need to fit a custom probability density (based on the symmetric beta
distribution B(shape, shape), where the two parameters shape1 and shape2
are identical) to my data.
The trouble is that I experience some problems also when dealing with the
plain vanilla symmetric beta distribution.
On Wed, 21 Jun 2017, J C Nash wrote:
Using a more stable nonlinear modeling tool will also help, but key is to get
the periodicity right.
The model is linear up to omega after transformation as Don and I noted.
Taking a guess that 2*pi/240 = 0.0262 is about right for omega:
rsq <- function
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,
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
On Tue, 20 Jun 2017, lily li wrote:
Hi R users,
I have a question about fitting a cosine curve. I don't know how to set the
approximate starting values.
See
Y.L. Tong (1976) Biometrics 32:85-94
The method is known as `cosinor' analysis. It takes advantage of the
*intrinsic* linear
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
wrote:
>
> If you know the period and want to fit
Thanks. I will do a trial first. Also, is it okay to have the datasets that
have only part of the cycle, or better to have equal or more than one
cycle? That is to say, I cannot have the complete datasets sometimes.
On Tue, Jun 20, 2017 at 7:37 PM, Don Cohen
wrote:
>
> If you know the period and
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
Thanks, that is cool. But would there be a way that can approximate the
curve by trying more starting values automatically?
On Tue, Jun 20, 2017 at 5:45 PM, Jim Lemon wrote:
> Hi lily,
> You can get fairly good starting values just by eyeballing the curves:
>
> plot(y)
> lines(supsmu(1:20,y))
>
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 wrote:
> Hi R users,
>
> I have a question about fitting a cosine curve. I don't know how to set the
>
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 work for sine curve
as well? Thanks.
Part of the dataset is in the following:
y=c(16.82, 16.72, 16.63, 16.47, 16.84, 16.25, 16.15, 16.83, 17.41, 17.67,
17.
Dear friends,
I have 5 exogenous variables which I´d like to incorporate into my
auto.arima model.
I was able to incorporate the xreg, and I understand that newxreg should be
the forecast of my exogenous variables, but I have not been able to get it
to work.
Newxreg should only have one column?
Did you search for the princurve package? Sounds like it may be what you
want.
See https://cran.r-project.org/web/packages/princurve/index.html
Best, MEH
Mark E. Hall, PhD
Assistant Field Manager
Black Rock Field Office
Winnemucca District Office
775-623-1529.
On Sun, Jan 1, 2017 at 2:56 AM, Ne
> On Jan 1, 2017, at 12:26 PM, Bert Gunter wrote:
>
> I couldn't find anything, but you might try searching on "thin plate
> splines" on rseek.org. I realize these are different than principal
> surfaces, but they might nevertheless be useful to you. Or not.
>
> Cheers,
> Bert
>
>
> Bert Gu
I couldn't find anything, but you might try searching on "thin plate
splines" on rseek.org. I realize these are different than principal
surfaces, but they might nevertheless be useful to you. Or not.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming al
Hello,
I need to summarize a three-dimensional dataset through a principal surface
that passes through the middle of the data. Principal surfaces are non-linear
generalization of the plane created by the first two principal components and
provide a non-linear summary of p-dimensional dataset. P
Hello,
Why quantile(train, 0.9) ? If you use quantile(train) it seems to fit
the data much better. You haven't posted a data example so I've made up one.
library(eva) # needed for rgpd()
library(extRemes)
set.seed(1)
train <- rgpd(1e3, scale = 0.9, shape = -0.4)
thresh90 <- quantile(train)
Let the train be the data set consisting of numbers that I need to fit.
Code is as follows:
library(extRemes)
thresh90 <- quantile(train, 0.90)
model<-fevd(train,threshold =thresh90,type="GP")
Model returns the following :
Negative Log-Likelihood Value: 317.7561
Estimated parameters:
scal
Hello,
Please provide us with a reproducible example. A data exampla would be
nice and some working code, the code you are using to fit the data.
Rui Barradas
Em 27-11-2016 15:04, TicoR escreveu:
I am trying to fit some data using Generalized Pareto Distribution in R
using extRemes package(h
I am trying to fit some data using Generalized Pareto Distribution in R
using extRemes package(https://cran.r-project.org/web/packages/extRemes) I
am able to get the parameters for the distribution. How would I get the
simulated values for the model using the parameters?
[[alternative HTML
Yes, I mentioned it wrong , I increased the value. This did not help
either. what helped is removing some samples which had zero (close to zero)
values. So its working fine for this error.
But there is another problem.
For one of the genes it says throws following error:
iteration = 1 log-lik di
Do you mean "increase the convergence value." Decreasing it should
make it harder to converge (I believe, depending on exactly how
"convergence vaue" is defined, so doublecheck.)
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into i
Thanks for the reply.
I have another related issue with Gamma mixture model. here is the
description:
I am trying to fit a 2 component gamma mixture model to my data (residual
values obtained after running Generalized Linear Model), using following
command (part of the code):
expr_mix_gamma <-
> Bert Gunter
> on Wed, 7 Sep 2016 23:47:40 -0700 writes:
> "please suggest what can I do to resolve this
> issue."
> Fitting normal mixtures can be difficult, and sometime the
> optimization algorithm (EM) will get stuck with very slow convergence.
> Presumably t
"please suggest what can I do to resolve this
issue."
Fitting normal mixtures can be difficult, and sometime the
optimization algorithm (EM) will get stuck with very slow convergence.
Presumably there are options in the package to either increase the max
number of steps before giving up or make th
Hi Simon
I am facing same problem as described above. i am trying to fit gaussian
mixture model to my data using normalmixEM. I am running a Rscript which
has this function running as part of it for about 17000 datasets (in loop).
The script runs fine for some datasets, but it terminates when i
> On May 26, 2016, at 7:51 PM, Franco Danilo Roca Landaveri
> wrote:
>
> Hello,
>
> I hope you can help me. In class, we were given an Excel worksheet with
> specified formulas that take the total score from a survey (or from a
> specific section) and convert it to a percentage, according to
A possible (simple) solution is to use a binomial GLM which guarantees
fitted values (percentiles) in (0,1):
plot(percentile, score)
o<-glm(percentile~sc, family=binomial)
points(fitted(o), sc, col=2)
You can "predict" percentiles given score via predict.glm()
best,
vito
Franco Danilo Roca L
Hello,
I hope you can help me. In class, we were given an Excel worksheet with
specified formulas that take the total score from a survey (or from a specific
section) and convert it to a percentage, according to a table that assigns
scores to a percentile. Since the formulas are too long and co
Thank you for the amazing response. You are right;I definitely have to
study a bit more. I am just trying to copy the procedure in a paper so
I didn't give it much thought.
for point (a) : yes the data is binned counts; and my aim is to find
out which curve best approximates these counts.
I am go
On 10/14/2015 05:00 AM, r-help-requ...@r-project.org wrote:
I am trying to fit this data to a weibull distribution:
My y variable is:1 1 1 4 7 20 7 14 19 15 18 3 4 1 3 1 1 1
1 1 1 1 1 1
and x variable is:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19 20 21 22 23 24
There's a number of issues with this:
(a) your data appear to be binned counts, not measurements along a curve.
(b) The function you are trying to fit is the Weibull _density_ This has
integral 1, by definition, whereas any curve anywhere near your y's would have
integral near sum(y)=127
(c) SSw
Yes. I do.I'm trying to repeat the methodology of a paper. They have fitted
their data to a weibull curve and so I want to do the same too, but I'm
unable to figure out how..
On Wed, Oct 14, 2015, 9:44 AM David Winsemius
wrote:
>
> On Oct 13, 2015, at 2:42 PM, Aditya Bhatia wrote:
>
> > I am try
On Oct 13, 2015, at 2:42 PM, Aditya Bhatia wrote:
> I am trying to fit this data to a weibull distribution:
>
> My y variable is:1 1 1 4 7 20 7 14 19 15 18 3 4 1 3 1 1 1
> 1 1 1 1 1 1
>
> and x variable is:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
> 19 20 21 22 23 24
I am trying to fit this data to a weibull distribution:
My y variable is:1 1 1 4 7 20 7 14 19 15 18 3 4 1 3 1 1 1
1 1 1 1 1 1
and x variable is:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19 20 21 22 23 24
The plot looks like this:http://i.stack.imgur.com/FrIKo.png and
Thanks for the help.
Frederic Ntirenganya
Maseno University,
African Maths Initiative,
Kenya.
Mobile:(+254)718492836
Email: fr...@aims.ac.za
https://sites.google.com/a/aims.ac.za/fredo/
On Wed, Mar 25, 2015 at 4:48 PM, S Ellison wrote:
>
> > Call:
> > lm(formula = curr_data[[tmin_col]] ~ curr_d
> Call:
> lm(formula = curr_data[[tmin_col]] ~ curr_data[[year_col]] -
> 1 | curr_data[[month_col]])
First, this is not a sensible formula for lm; lm() does not use '|' to denote
grouping. It would be a valid formula for xyplot, in which | specifies grouping
variables. In lm(), '|' is sim
On 25/03/2015 12:30, Frederic Ntirenganya wrote:
Hi all,
I am doing analysis which involves fitting a line on trellis plot.
But the commands below (or at least the output from them) are not
plotting commands.
The
factor is month. As you know a year has 12 months and I expect to get
12 l
Hi all,
I am doing analysis which involves fitting a line on trellis plot. The
factor is month. As you know a year has 12 months and I expect to get
12 lines one for each month. I am getting the following results which
is different to my expectation. Am I have to do anything about the
data? Any su
I have data for 90 climate stations. For each station, I have made 100+
simulations using a statistical model. So, in R, I have 90 dataframes, each
dataframe has 100+ simulations arranged column-wise.
Now, I would like to fit an extreme value distribution (EVD) to each climate
station. That is
On Thu, 5 Mar 2015, Michael Friendly wrote:
Why not make the legend fit on one line above or below the plot matrix?
?legend
-- look at ncol=, horiz= and xpd= args
Michael,
That's just the pointer I was hoping to see. There's no reason not to have
the legend outside the plot matrix and now I
Why not make the legend fit on one line above or below the plot matrix?
?legend
-- look at ncol=, horiz= and xpd= args
On 3/4/2015 4:39 PM, Rich Shepard wrote:
I have a matrix plot of ternary diagrams (pdf attached) generated with
these commands:
opar <- par(xpd=NA,no.readonly=T)
plot(Wint
I have a matrix plot of ternary diagrams (pdf attached) generated with
these commands:
opar <- par(xpd=NA,no.readonly=T)
plot(WintersY, pch=as.numeric(WintersX4),
col=c("black","red","green","blue","yellow","orange")[WintersX4])
legend(x=0.75, y=0.0, abbreviate(levels(WintersX4),
On 17/02/15 12:59, smart hendsome wrote:
I'm very new to r-programming. I have rainfall data. I have tried to fit gamma
into my data but there is error. Anyone can help me please.
My rainfall data as I uploaded. When I try run the coding:
library(MASS)
KLT1<-read.csv('C:/Users/User/Dropbox/PhD
I'm very new to r-programming. I have rainfall data. I have tried to fit gamma
into my data but there is error. Anyone can help me please.
My rainfall data as I uploaded. When I try run the coding:
library(MASS)
KLT1<-read.csv('C:/Users/User/Dropbox/PhD
Materials/Coding_PhD_Thesis/Kelantan_Avera
Hi All,
First of all, I would like to wish you all a Happy New Year, full of
creativity, inspiration and prosperity.
Now, I have a data set with two uneven size parameters which is the following:
x1 x2
1 0.98 0.952 0.99 0.98
3 1.11 1.01
4
On Jan 2, 2015, at 12:04 PM, Ben Bolker wrote:
> Ben Bolker gmail.com> writes:
>
>>
>> Paul Hudson gmail.com> writes:
>>
>
> [snip]
>
>> library("tweedie")
>> set.seed(1001)
>> r <- rtweedie(1000,1.5,mu=2,phi=2)
>> library("bbmle")
>> dtweedie2 <- function(x,power,mu,phi,log=FALSE,debug=FA
Ben Bolker gmail.com> writes:
>
> Paul Hudson gmail.com> writes:
>
[snip]
> library("tweedie")
> set.seed(1001)
> r <- rtweedie(1000,1.5,mu=2,phi=2)
> library("bbmle")
> dtweedie2 <- function(x,power,mu,phi,log=FALSE,debug=FALSE) {
> if (debug) cat(power,mu,phi,"\n")
> res <- dtweed
Paul Hudson gmail.com> writes:
>
> Hello all,
>
> I want to fit a tweedie distribution to the data I have.
>
> The R packages I have been able to find assume that I want to use it as
> part as of a generalized linear model.
>
> This is not the case, I want to directly fit the distribution to
On Jan 2, 2015, at 10:33 AM, Paul Hudson wrote:
> Hello all,
>
> I want to fit a tweedie distribution to the data I have.
>
> The R packages I have been able to find assume that I want to use it as
> part as of a generalized linear model.
>
> This is not the case, I want to directly fit the di
The tweedle package[1] claims to have "functions for computing and fitting
the Tweedie family of distributions". Hope that helped. -- H
1. http://cran.r-project.org/web/packages/tweedie
On 2 January 2015 at 10:33, Paul Hudson wrote:
> Hello all,
>
> I want to fit a tweedie distribution to the da
Hello all,
I want to fit a tweedie distribution to the data I have.
The R packages I have been able to find assume that I want to use it as
part as of a generalized linear model.
This is not the case, I want to directly fit the distribution to the data.
Is there a package that allows this?
Dear Guillaume,
Please see comments interspersed below:
> -Original Message-
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of
> Guillaume Souchay
> Sent: Thursday, December 18, 2014 8:50 AM
> To: r-help@r-project.org
> Subject: [R] Fitting Structural E
Hi all,
I am trying to analyse bird data to investigate carry-over effect
using structural equation model.
I failed to run properly a big model with several latent variables
with both L -> M block and M -> L block.
Rather than trying again and again with the huge model, I am now
looking to a subse
Dear UseRs,
I have a dataset in the following form.
> dput(Da)
structure(c(0.0238095238095238, 0.0476190476190476, 0.0714285714285714,
0.0952380952380952, 0.119047619047619, 0.142857142857143, 0.167,
0.19047619047619, 0.214285714285714, 0.238095238095238, 0.261904761904762,
0.285714
Hi Thomas,
Thank you very much for your help. I will try to calculate some summary
statistics and fit observed and simulated data using a time series (as your
examples). With observed data, I would use two values (e.g. 0 and 10),
because I do not have intermediate values.
Thanks!
Javier
2014-06-1
On 17.06.2014 10:37, Javier Rodríguez Pérez wrote:
Hi Thomas, Thank you very much for your help. I will try to calculate
some summary statistics and fit observed and simulated data using a
time series (as your examples). With observed data, I would use two
values (e.g. 0 and 10), because I do not
Hello!
I'm trying to generate grid-based landscape mimicking observed data. For
this purpose I'm using simecol and adapting the "CA" (stochastic celular
automaton) model included as example in vignette. I think this example
could be nice given observed data has spatial autocorrelation. My observed
Hi,
I have no example at hand, but the usual way could be indeed to
calculate a time series of an adequate statistic (e.g. spatial
statistic) from both, the observed and the simulated data and then to
apply standard model fitting.
Thomas
__
R-help@r-p
Greetings R community,
I would like to regress a nonlinear trend onto several subsets of data
(representing different treatments) within a dataset. In my case, I would like
to fit a nonlinear trend to several different "Tillage" treatments.
model.global = nlrob(FinalBiomass ~
Wm[Tillage]*(1
Hi Johannes,
Below code gives good results for me; note that trying multiple
starting is often important in fitting mixture models, even in simple
cases like this.
Note also that the sigma and nu parameters in gamlssMX are fitted on a
log scale, hence the possible occurrence of negative results.
ht
Dear R community,
I`d like to extract the parameters of a two-component mixture
distribution of noncentral student t distributions which was fitted to a
one-dimensional sample.
There are many packages for R that are capable of handling mixture
distributions in one way or another. Some in the
Can you provide some sample data and the family of curves that you
would like to fit?
Reproducible examples greatly increase your chances of receiving a
useful response.
On Wed, Apr 23, 2014 at 12:33 AM, andreas betz wrote:
> Hello,
>
> is it possible to fit a group of curves simultaneously to a
Hello,
is it possible to fit a group of curves simultaneously to an equation with
some parameters shared among the curves others fit for each curve
individually. Several commercial software programs like Originlab have this
option often referred to as global fit. I would appreciate any advice or
r
given the data below
x1<-c(-1,0,1,-1,0,1,-1,0,1,-1,0,1,-1,0,1,-1,0,1,-1,0,1,-1,0,1,-1,0,1)
y<-c(-1,-1,-1,0,0,0,1,1,1,-1,-1,-1,0,0,0,1,1,1,-1,-1,-1,0,0,0,1,1,1)
z<-c(-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1)
using maple i can fit as below and get (1)
#PolynomialFit(3, x1,
require(copula)
# I specify the copula
gmb<-gumbelCopula(4,dim=2)
# The bivariate CDF is generated
myCDF<-mvdc(gmb,margins=c("lnorm","nbinom"),paramMargins=list(list(meanlog=11.69,sdlog=0.7781),list(mu=16,size=2.6)))
# A random sample of the bivariate risk data is generated as a matrix.
Th
require(copula)
# I specify the copula
gmb<-gumbelCopula(4,dim=2)
# The bivariate CDF is generated
myCDF<-mvdc(gmb,margins=c("lnorm","nbinom"),paramMargins=list(list(meanlog=11.69,sdlog=0.7781),list(mu=16,size=2.6)))
# A random sample of the bivariate risk data is generated as a matrix.
Th
# I define a bivariate copula composed of 2 marginals each with 2 parameters
gmb<-gumbelCopula(4,dim=2)
# Idefine the bivariate CDF specifying the already fitted parameters
from the log normal and negative binomial
myCDF<-mvdc(gmb,margins=c("lnorm","nbinom"),paramMargins=list(list(meanlog=11.6
Hi,
I have one set of observations containing two parameters.
How to fit it into copula (estimate the parameter of the copula and the
margin function)?
Let's say the margin distribution are log-normal distributions, and the
copula is Gumbel copula.
The data is as below:
1 974.0304 1010
2 60
Thanks David for stopping by this thread. I have to admit that the word
"arbitrary" was arbitrarily chosen. As I explained in the second email in
the thread, what I am really interested are some estimates obtained via
other methods that have the same asymptotic distribution as MLE. The
following si
On Feb 26, 2014, at 12:24 AM, Xiaogang Su wrote:
> Dear All,
>
> Does anyone know if there is any way to obtain the variance-covariance
> matrix for any arbitrarily given estimates with the glm() function?
>
> Here is what I really want. Given an arbitrary estimate (i.e., as starting
> points w
Dear All,
Does anyone know if there is any way to obtain the variance-covariance
matrix for any arbitrarily given estimates with the glm() function?
Here is what I really want. Given an arbitrary estimate (i.e., as starting
points with the start= argument), the glm() function could return the
cor
next
time!
HTH
daniel
Feladó: r-help-boun...@r-project.org [r-help-boun...@r-project.org] ;
meghatalmazó: Charles Thuo [tcmui...@gmail.com]
Küldve: 2014. február 25. 8:36
To: r-help@r-project.org
Tárgy: [R] fitting a time series into a GARCH model using
I have been trying to fit a time series object using the garchFit()
function. However am getting the results below
> class(v.ts)
[1] "ts"
fit=garchFit(v.ts)
Error in if (allVarsTest != 1) { : missing value where TRUE/FALSE needed
I do not understand the error. Which missing value is required. K
On 2/10/2014 11:12 AM, Bert Gunter wrote:
Perhaps:
?tapply
and/or various wrappers like ?by .
Cheers,
Bert
Thanks, Bert
The examples for ?by gave the answer I was looking for:
# using by()
mods <- with(Punishment,
by(Punishment, list(age, education),
function(x)
Perhaps:
?tapply
and/or various wrappers like ?by .
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch
On Mon, Feb 10, 2014 at 8:05 AM, Michael Friendly
With data like the following, a frequency table in data frame form, I'd
like to fit a collection of loglm models
of independence of ~ attitude + memory for each combination of education
and age.
I can use apply() if I first convert the data to a 2 x 2 x 3 x 3 array,
but I can't figure out an
e
ned solution.
Petr
From: Zorig Davaanyam [mailto:dzo...@gmail.com]
Sent: Saturday, December 21, 2013 12:36 AM
To: PIKAL Petr
Subject: Re: [R] Fitting particle size analysis data
Hi,
Thanks for the reply. Do you know how to find the fitted parameters of
lognormal distribution?
Regards,
Zorig
On Fri,
t.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Zorig Davaanyam
> Sent: Friday, December 20, 2013 2:01 AM
> To: r-help@r-project.org
> Subject: [R] Fitting particle size analysis data
>
> Hi all,
>
> How do you fit a sieve analysis data to a statistical func
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