search for: sigma_i

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) 0.002169851 with absolute error < 4.4e-05 I would like to also use Gauss-Laguerre methods to also integrate this function. In doing so, I believe the only change of variable needed when integrating fro...

...chools data by Gelman from R2WinBUGS package. As you can see bellow use of weights argument did not had influence on results. I do not know if my specification of weights i.e. 1 / sd^2 is ok. Under least squares one minimizes sum(e^2_i) or sum(w_i * e^2_i) with weighted LS. If I consider that \sigma_i represents uncertainty in my "data" then e'_i = e_i / \sigma_i and we minimize sum(e'^2_i) = sum((e_i / \sigma_i)^2) = sum(e_i * \sigma^-2_i). Therefore weights i.e. w_i are equal to 1 / \sigma^2_i. Can anyone help me with this issue? Thank you very much! > library(&quot...

Good afternoon, I have been working on my thesis project on the topic "Urban Heat Island Pattern in India". To achieve the results I am applying a* two-dimensional Gaussian fit* on an LST raster of 1 km spatial resolution but I am facing two errors in the following code. library(raster) LST <- raster("D:/Celsius_Day/MOD_01.tif") gaussian2d <- function(x, y, mu_x, mu_y,

...onstant auto-coefficient across units can describe the data. that is, I want to find out whether x(i,t)= a + b(i) + c*x(i,t-1) is better replaced by x(i,t)=a + b(i) + c(i)*x(i,t-1). right now, I am running N OLS TS regression of x on lagged x, and am picking off the mean(c), sd(c), and mean(sigma_i) and sd(sigma_i). if there is a procedure in R that already does a test for heterogeneous autocorrelation coefficients in a more intelligent fashion, please please point me to it. however, even if this exists, I think I need to figure out how to find a more graceful optimizer anyway. [[al...

Hi all, I'm trying to define and log-likelihood function to work with MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 && ro<1 && ro>-1...

...uld be rewritten to work correctly. >> > > >> > >- Mary >> > > >> > >Andrzej Galecki (agalecki at umich.edu) writes: >> > > > Hi Mary, >> > > > >> > > > Our question is about variance-covariance matrix, hat(sigma_i), >> > > > returned by getVarCov() function when applied to model fit generated by >> > > > gls( ) function. It appears that at least in our example the returned >> > > > matrix was incorrect. Are you aware of any problem with getVarCov () >> >...

...the above data frame represent the segment and each segment contain two-mixture component. (Latter I will increase the segment and the mixture component) So for every segment with n mixture component I have to find the *max f(x) with ?infinity < x < infinity* *f(x)=sum_i c_i N(O,mu_i,sigma_i). * *Since i need to calculate the derivate f? and set f?(x)=0 I thought of using Newton method * *In R the function I selected for my problem is optim.* *Below is my code * *l = 2 # represent the number of mixture component* *i=1# represent the segment 1 if i=2 represent the 2 se...

...sition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal matrix with elements equal to Q_{i,t} = sigma_i*(1-exp(-kappa_i*h)/kappa_i*(theta_i/2*(1-exp(kappa_i*h)+exp(-kappa_i*h)y_{t-1,i} The fkf returns the filtered states variables so y_{t-1,i} is available. I just can't figure out how to write my program in such a way that this information is included and updated in the state space model for ea...

...e written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i where e_i is normal with mean 0 and variance sigma_i^2 There is a smoothing parameter lambda between 0 and 1. .when lambda is 0, smoothness is all that matters, and the fitting function will be a straight line. .when lambda is 1, the result is the interpolating spline. In my case, this parameter is an input. The resulting function is the spline...

...ackage for R to modeling glucose-insuline response with Bergman's model, very similar to the example in the documentation for the NLME package. My question concerns the model for the residuals. I use a proportional model , Var(e_{ij})=(sigma_g*G(t))^2 for the glucose response and Var(e_{ij})=(sigma_i * I(t))^2 for the insulin response. Hence I have a varPower model, but I know that the power coefficient is 2, but with different sigma coefficients for the two responses. If I fit a model I get an estimation of the standard deviation of the residuals, which directly can be obtained from the outpu...

...itten in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i where e_i is normal with mean 0 and variance sigma_i^2 There is a smoothing parameter lambda between 0 and 1. .when lambda is 0, smoothness is all that matters, and the fitting function will be a straight line. .when lambda is 1, the result is the interpolating spline. In my case, this parameter is an input. The resulting function is the spline...

This is not an R question but if anyone can help me, it's much appreciated. Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon ) and the two series have the relation y_t = Beta*x_t + epsilon My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in