[R] Optimize function in R: unable to find maximum of logistic function

Hello Everyone, I want to perform a 1-D optimization by using the optimize() function. I want to find the maximum value of a logistic function. The optimize() function gives the wrong result. My code: f= function (k) { T_s = 20 result = (2- 2/(1+ exp(-2*T_s*k))) return(result) } optimize(f, c(0,

Re: [R] Optimize function in R: unable to find maximum of logistic function

On 10/29/13 06:00, Shantanu MULLICK wrote: Hello Everyone, I want to perform a 1-D optimization by using the optimize() function. I want to find the maximum value of a logistic function. The optimize() function gives the wrong result. My code: f= function (k) { T_s = 20 result = (2- 2/(1

[R] R utilizing 25% of CPU for Dual core i3 370M processor

Hello Everyone I have a dual-core Intel i3-370M processor and Windows 64 operating system. I have a 3GB RAM and my processor can support a maximum of 8GB RAM. I have virtual memory enabled on my computer. I am running a program in the 64 bit R which implements a MCMC on a large dataset and

[R] Parameterization of Inverse Wishart distribution available in MCMCpack and bayesm libraries

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to

[R] System is computationally singular error when using cholesky decompostion in MCMC

Hello Everyone I have a MCMC loop to calculate a time varying hierarchical Bayesian structure. This requires me to use around 5-6 matrix inversions in the loop. I use cholesky and chol2inv for the matrix decomposition. Because of the data I am working with I am required to invert a 167 by 167

[R] Simulating from a Normal Inverted Wishart distribution

Hello everyone I was wondering how would one simulate from a Normal Wishart Distribution in R. A normal inverted Wishart distribution is denoted by NIW (M,C,d,S), where X/(Sigma) ~ N( M,C,(Sigma) ) - a matrix normal distribution, (Sigma) - column dispersion matrix (Sigma) ~ IW (d,S) -

Re: [R] Simulating from matrix variate normal distribution

. But it is not clear what you mean by a quantile function (of a matrix variate distribution). A cdf is going to be a lot harder, but still doable. Hope this helps! best wishes, Ranjan On Sat, 4 Feb 2012 00:57:45 +0100 Shantanu MULLICK b00295...@essec.edu wrote: Hello everyone

[R] Simulating from matrix variate normal distribution

Hello everyone Is there a function/command to simulate from matrix variate normal distribution in R. A follow up question would be is there a function/command to obtain the density, distribution and quantile function of matrix variate normal distribution in R. Wikipedia has a good description