similar to: how to fit a model that is nonlinear with multiplicate errors

When I saw the subject of the original message on R-help, I was 95% confident that I knew the answer (before I had seen the question). This made me think that perhaps for some functions there should be a 'Troubleshooting' section in the help file. The current help file for 'integrate' does say, as Sundar points out, what the requirements are. However, I think more people would

Dear all, I am stuck on the following problem with integrate(). I have been out of luck using RSiteSearch().. My function is g2<-function(b,theta,xi,yi,sigma2){ xi<-cbind(1,xi) eta<-drop(xi%*%theta) num<-exp((eta + rep(b,length(eta)))*yi) den<- 1 + exp(eta + rep(b,length(eta))) result=(num/den)*exp((-b^2)/sigma2)/sqrt(2*pi*sigma2)

Hi, I am trying to fit a function of the form: y = A0 + A1 * exp( -0.5* ( (X - Mu1) / Sigma1 )^2 ) - A2 * exp ( -0.5* ( (X-Mu2)/Sigma2 )^2 ) i.e. a mean term (A0) + a difference between two gaussians. The constraints are A1,A2 >0, Sigma1,Sigma2>0, and usually Sigma2>Sigma1. The plot looks like a "Mexican Hat". I had trouble (poor fits) fitting this function to toy data

Dear Researchers, I wrote two function to fit a circle using noisy data. 1- the fitCircle() is derived from MATLAB code of * zhak Bucher* from the link http://www.mathworks.com/matlabcentral/fileexchange/5557-circle-fit/content/circfit.m 2- the CircleFitByPratt() from MATLAB code of *Nikolai Chernov *from the link

Dear All, have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

Hello, I am trying to do a fit a loglikelihood function with Multivariate distribution via copulas with fitMdvc. The problem is that it doesn't recognize that my beta is a vector of km parameter and when I try to run it it say that the length of my initial values is not the same as the parameter. Can somebody guide me where my mistake is. Thanks, Elisa. #################################

Hey I'm not really sure what I should put on here, but I am having trouble with my R code. I am trying to get the p-values, R^2s etc for a number of different groups of variables that are all in one dataset. This is the code: #Stand counter st<-1 #Collections stands<-numeric(67) slopes<-numeric(67) intercepts<-numeric(67) mses<-numeric(67) rsquares<-numeric(67)

Optimize celt_pitch_xcorr function (for fixed point). Even though same code in theory should work for ARMv7 as well, turning this on only for aarch64 at the moment since there is a fixed point asm implementation for ARMv7 neon. Signed-off-by: Viswanath Puttagunta <viswanath.puttagunta at linaro.org> --- celt/arm/celt_neon_intr.c | 268 ++++++++++++++++++++++++++++++++++++++++++++++

Optimize celt_pitch_xcorr function (for floating point) using ARM NEON intrinsics for SoCs that have NEON VFP unit. To enable this optimization, use --enable-intrinsics configure option. Compile time and runtime checks are also supported to make sure this optimization is only enabled when the compiler supports neon intrinsics. --- Makefile.am | 12 ++

Optimize celt_pitch_xcorr function (for fixed point). Even though same code in theory should work for ARMv7 as well, turning this on only for aarch64 at the moment since there is a fixed point asm implementation for ARMv7 neon. Signed-off-by: Viswanath Puttagunta <viswanath.puttagunta at linaro.org> --- celt/arm/celt_neon_intr.c | 268 ++++++++++++++++++++++++++++++++++++++++++++++

On 19 December 2014 at 17:25, Viswanath Puttagunta <viswanath.puttagunta at linaro.org> wrote: > Optimize celt_pitch_xcorr function (for floating point) > using ARM NEON intrinsics for SoCs that have NEON VFP unit. > > To enable this optimization, use --enable-intrinsics > configure option. > > Compile time and runtime checks are also supported to make sure > this

Optimize celt_pitch_xcorr function (for floating point) using ARM NEON intrinsics for SoCs that have NEON VFP unit. To enable this optimization, use --enable-intrinsics configure option. Compile time and runtime checks are also supported to make sure this optimization is only enabled when the compiler supports neon intrinsics. --- Makefile.am | 12 ++

Optimize celt_pitch_xcorr function (for floating point) using ARM NEON intrinsics for SoCs that have NEON VFP unit. To enable this optimization, use --enable-intrinsics configure option. Compile time and runtime checks are also supported to make sure this optimization is only enabled when the compiler supports neon intrinsics. --- Makefile.am | 11 ++

Hi, this question may be off topic: the unbiased estimator of the variance of the errors in a linear regression moedel with p coefficients is: sigma2=sum((y-yi)^2)/(length(y)-p-1) But what if i estimate transformations of the dependent an independent variables (e.g. Box-Cox) too? May I calculate the variance using sigma2=sum((y-yi)^2)/(length(y)-2*p-1) or should I use the first formula

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

Hi R users, I have some data points (Xi, Yi), and they may follow such a pattern Yi = cCOS(Xi) + d, how to find the c and d in R? which function to use? Also, how to get the R2 and p value for this correlation? Thanks for any kind of help. [[alternative HTML version deleted]]

Dear list, There is, I believe, a minor typo in the example section of influence.measures.Rd. In the final example the word `does` appears where I suspect `dose` is required: I couldn't remember exactly what format patches should be in, so here is one as diff would produce: Index: devel/src/library/stats/man/influence.measures.Rd

Hi There, I have a set of data (xi,yi).I want to fit them with the equation y=mx. note: in the above equation, there is no intercept. I don't know how to use common software such as R , matlab, sas, or spss to do this kind of regression. Does anyone know how to do this? I know it is easy to use least square method to do this by programming. But I want to find if there exists some common

Start with the lm() function; i.e., see ?lm -Don -- Don MacQueen Lawrence Livermore National Laboratory 7000 East Ave., L-627 Livermore, CA 94550 925-423-1062 On 6/14/17, 3:40 PM, "R-help on behalf of lily li" <r-help-bounces at r-project.org on behalf of chocold12 at gmail.com> wrote: Hi R users, I have some data points (Xi, Yi), and they may follow such a

Hi, FWIW: /* Taken from R/src/main/unique.c */ static int requal(SEXP x, int i, SEXP y, int j) { if (i < 0 || j < 0) return 0; if (!ISNAN(REAL(x)[i]) && !ISNAN(REAL(y)[j])) return (REAL(x)[i] == REAL(y)[j]); else if (R_IsNA(REAL(x)[i]) && R_IsNA(REAL(y)[j])) return 1; else if (R_IsNaN(REAL(x)[i]) && R_IsNaN(REAL(y)[j])) return 1;