search for: loglikehood

hi folks, I'm trying to build a table that contains information about a series of General Linear Models in order to calculate Akaike weights and other measures to compare all models in the series. i have an issue with indexing models and extracting the information (loglikehood, AIC's, etc.) that I need to compile them into the table. Below is some sample code that illustrates my approach so far and my problem. I realize that somehow i need to provide actual object for the AIC or LogLik call, and I've tried a couple of different ideas (e.g., as.name, expressio...

HiI want to construct a logliikelood function in RHere is the situationy=number of particles emitted in 1 hr period~pois(30)p=probability of detection of radiation particlesx=number of particles detected by a radiation detector~pois(30p)where p~beta(a,1)I have to calculate the loglikehood for a for the range a(2,50)I wish to simulate 100 random samples for each aHere is my code:-m=481n=100x = c(15, 36, 29, 28, 37, 32, 25, 27, 31, 21, 25, 27, 28, 31, 28, 20, 34, 25, 20, 34, 15,21, 28, 24, 31, 19, 34, 29, 18, 25, 16, 19, 44, 26, 34, 31, 21, 28, 11, 31, 21, 34, 25, 25,30, 23, 21, 35, 3...

...ith box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not produce the hessian matrix, I used hessian (numDeriv) to calculate it. However, the standard errors calculated using hessian function are quite different from the ones generated by the glm function, although the parameter estimates are very close. I was wondering w...

...ctors that are also used in the interaction term (i.e., for b1 and b2, individually, not just the b1:b2 interaction)? Using the LLRT approach, when I drop one of these individual predictors (e.g., b2) without also dropping its interaction term (i.e., b1:b2), I obtain a reduced model with a loglikehood estimate that is equal to the loglikehood estimate of the saturated model. A LLRT between this reduced and saturated model has 0 degrees of freedom (because the same number of parameters is estimated for both the reduced and the saturated model); as in the output below: Model 1: glmm.ad...

Dear All, I decided to use an AR(1) model for the residual series and trying to find the p-value for each parameter by using arima command in R, but i never find it from the output. The output gives me the parameter and mean's value, its standard error, estaimated variance, AIC and loglikehood, but no p-value is given. What i did was, i assign the residuals into the 'residual' variable in R, i then use' arima(residual, order = c(1,0,0))' to compute the parameter and mean values. Could someone help me? Thank you Fir [[alternative HTML version deleted]]

...eviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <- optim(s0, fmin, gmin, method = "BFGS", hessian = Hess, ...) . . . deviance <- 2 * res$value If so, why is it that; > x = rnorm(10) > y = rpois(10,lam=exp(1 + 2*x)) > test = glm(formula = y ~ x, family = poisson) > devia...