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because R is trying to subtract a length-3 vector from a length-20 vector. It repeats the length-3 vector over the length-20 vector until its done it six times, then it only has two elements left. Hmmmm... thinks R, I'll do this but it looks wrong so here's a warning.
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Actually I want to use a graph to show that the log likelihood function cannot be differentiated with respect to k in order to find the maximum likelihood estimator (MLE). How can I express the third graph in terms of parameter k? $\endgroup$ â Chi Yum Mar 5 '13 at 13:02
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1 Answer. This must be plotted as the parameters μ and Ï vary. In R, use contour or filled.contour to make such a plot. As with many scale families, it will be clearer to plot Ï on a logarithmic scale. So, we estimate a reasonable range for μ, a reasonable range for log (Ï), divide those ranges into little pieces,...
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In order to make a plot of this, we'll use a chart. So I'll highlight the column with the likelihood values. Go to Charts, choose a line chart, and do a basic line chart. If you look carefully, you'll be able to see that the likelihood function is maximized at 0.18 or 72 over 400. This is the maximum likelihood estimate.
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The likelihood is a function of the mortality rate theta. So we'll create a function in r, we can use the function command, and store our function in an object. You can call this object likelihood. Use the function command and we specify what arguments this function will have.
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Aug 18, 2013 · Then we formulate the log-likelihood function. > LL - function(mu, sigma) { + R = dnorm(x, mu, sigma) + # + -sum(log(R)) + } And apply MLE to estimate the two parameters (mean and standard deviation) for which the normal distribution best describes the data.
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Oct 13, 2011 · Playing in R with an example. It is difficult to see the maximum likelihood in this plot, so we will zoom-in by generating a smaller grid around the typical mean and standard deviation estimates. In real life, software will use an iterative process to find the combination of parameters that maximizes the log-likelihood value.
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Use of custom distributions requires specification of a custom likelihood function in the argument func. plot.likfunc. A logical command for indicating whether a graph of the log-likelihood function should be created. plot.density. A logical command for indicating whether a second graph, in which densities are plotted on the pdf, should be created.
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If data are standardised (having general mean zero and general variance one) the log likelihood function is usually maximised over values between -5 and 5. The transformed.par is a vector of transformed model parameters having length 5 up to 7 depending on the chosen model.
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