Displaying 20 results from an estimated 1000 matches similar to: "gauss.quad.prob"
2006 May 05
0
Spline integration & Gaussian quadrature (was: gauss.quad.prob)
Spencer
Thanks for your thoughts on this. I did a bit of work and did end up
with a method (more a trick), but it did work. I am certain there are
better ways to do this, but here is how I resolved the issue.
The integral I need to evaluate is
\begin{equation}
\frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta}
{\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta}
\end{equation}
Where
2009 Aug 07
1
Gauss-Laguerre using statmod
I believe this may be more related to analysis than it is to R, per se.
Suppose I have the following function that I wish to integrate:
ff <- function(x) pnorm((x - m)/sigma) * dnorm(x, observed, sigma)
Then, given the parameters:
mu <- 300
sigma <- 50
m <- 250
target <- 200
sigma_i <- 50
I can use the function integrate as:
> integrate(ff, lower= -Inf, upper=target)
2013 Oct 11
3
Gaussian Quadrature for arbitrary PDF
Hi all,
We know that Hermite polynomial is for
Gaussian, Laguerre polynomial for Exponential
distribution, Legendre polynomial for uniform
distribution, Jacobi polynomial for Beta distribution. Does anyone know
which kind of polynomial deals with the log-normal, Studentæ¯ t, Inverse
gamma and Fisheræ¯ F distribution?
Thank you in advance!
David
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2008 Sep 27
3
Double integration - Gauss Quadrature
Hi,
I would like to solve a double integral of the form
\int_0^1 \int_0^1 x*y dx dy
using Gauss Quadrature.
I know that I can use R's integrate function to calculate it:
integrate(function(y) {
sapply(y, function(y) {
integrate(function(x) x*y, 0, 1)$value
})
}, 0, 1)
but I would like to use Gauss Quadrature to do it.
I have written the following code (using R's statmod package)
2006 Jul 20
2
Timing benefits of mapply() vs. for loop was: Wrap a loop inside a function
List:
Thank you for the replies to my post yesterday. Gabor and Phil also gave
useful replies on how to improve the function by relying on mapply
rather than the explicit for loop. In general, I try and use the family
of apply functions rather than the looping constructs such as for, while
etc as a matter of practice.
However, it seems the mapply function in this case is slower (in terms
of CPU
2006 Aug 22
1
a generic Adaptive Gauss Quadrature function in R?
Hi there,
I am using SAS Proc NLMIXED to maximize a likelihood with
multivariate normal random effects. An example is the two part random
effects model for repeated measures semi-continous data with a
cluster at 0. I use the "model y ~ general(loglike)" statement in
Proc NLMIXED, so I can specify a general log likelihood function
constructed by SAS programming statements. Then the
2010 Nov 14
1
Integrate to 1? (gauss.quad)
Does anyone see why my code does not integrate to 1?
library(statmod)
mu <- 0
s <- 1
Q <- 5
qq <- gauss.quad(Q, kind='hermite')
sum((1/(s*sqrt(2*pi))) * exp(-((qq$nodes-mu)^2/(2*s^2))) * qq$weights)
### This does what's it is supposed to
myNorm <- function(theta) (1/(s*sqrt(2*pi))) * exp(-((theta-mu)^2/(2*s^2)))
integrate(myNorm, -Inf, Inf)
2007 Mar 21
2
Gaussian Adaptive Quadrature
Hi all,
Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions?
Thanks in advance,
Regards,
Caio
__________________________________________________
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2009 May 08
1
ADAPTIVE QUADRATURE WEIGHTS AND NODES
Can anyone help me on how to get the nodes and weights of the adaptive quadrature
using R.
Best wishes
Boikanyo.
-----
The University of Glasgow, charity number SC004401
2010 Apr 14
2
Gaussian Quadrature Numerical Integration In R
Hi All,
I am trying to use A Gaussian quadrature over the interval (-infty,infty) with weighting function W(x)=exp(-(x-mu)^2/sigma) to estimate an integral.
Is there a way to do it in R? Is there a function already implemented which uses such weighting function.
I have been searching in the statmode package and I found the function "gauss.quad(100, kind="hermite")" which uses
2008 Mar 12
3
Types of quadrature
Dear R-users
I would like to integrate something like \int_k^\infty (1 - F(x)) dx, where F(.) is a cumulative distribution function. As mentioned in the "integrate" help-page: integrate(dnorm,0,20000) ## fails on many systems. This does not happen for an adaptive Simpson or Lobatto quadrature (cf. Matlab). Even though I am hardly familiar with numerical integration the implementation
2010 Sep 29
1
nlminb and optim
I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to.
The likelihood I have is (in tex below)
\begin{equation}
\label{eqn:marginal}
L(\beta) = \prod_{s=1}^N \int
2010 Sep 21
3
bivariate vector numerical integration with infinite range
Dear list,
I'm seeking some advice regarding a particular numerical integration I
wish to perform.
The integrand f takes two real arguments x and y and returns a vector
of constant length N. The range of integration is [0, infty) for x and
[a,b] (finite) for y. Since the integrand has values in R^N I did not
find a built-in function to perform numerical quadrature, so I wrote
my own after
2009 Jan 17
2
DierckxSpline segfault
I've just encountered a segfault when using DierckxSpline::percur
function. Below is the minimal example which triggers the error:
---
library(DierckxSpline)
x <- 1:10
y <- rep(0, 10)
pspline <- percur(x, y)
---
*** caught segfault ***
address (nil), cause 'memory not mapped'
Traceback:
1: .Fortran("percur", iopt = as.integer(iopt), m = as.integer(m),
x =
2011 Nov 10
2
performance of adaptIntegrate vs. integrate
Dear list,
[cross-posting from Stack Overflow where this question has remained
unanswered for two weeks]
I'd like to perform a numerical integration in one dimension,
I = int_a^b f(x) dx
where the integrand f: x in IR -> f(x) in IR^p is vector-valued.
integrate() only allows scalar integrands, thus I would need to call
it many (p=200 typically) times, which sounds suboptimal. The
2009 Nov 29
1
optim or nlminb for minimization, which to believe?
I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard
2007 Nov 09
5
Multivariate integration with infinite limits
Dear All,
Can R perform multivariate integration with infinite limits of integration?
Thanks in advance,
Paul
2010 Mar 26
1
Poisson Lognormal
Hi R Users,
I'm going to estimate via. ML the parameters in Poisson Lognormal
model. The model is:
x | lambda ~ Poisson(lambda)
lambda ~ Lognormal(a,b)
Unfortunately, I haven't found a useful package allowing for such
estimation. I tried to use "poilog" package, but there is no equations
and it's hard to understand what exactly this package really does.
Using it I get the
2005 Dec 15
1
generalized linear mixed model by ML
Dear All,
I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The
2008 May 17
0
fast multipole methods(FMM)/fast Gauss transfrorm(FGT)/improved fast gauss transform (IGFT)
I'm just curious, but wondering if there has been any work in making
these algorithms available in R. They are aimed at accelerating
matrix-vector products using approximation ideas, and might be useful in
applications such as kernel machines, Gaussian processes/kriging.
Thanks
Mark Palmer
Landscape Monitoring and Modelling
CSIRO Mathematical and