This blog post is a follow up on my previous post on optimising a Weibull regression model using optimx(). This time I’ll try to find a solution for the discrepancy between the confidence interval estimates of the Weibull hazard function estimated with optimx() and flexsurvreg().

In this blog post we will optimise a Weibull regression model by maximising its likelihood function using optimx() from the {optimx} package in R. In my previous blog post I showed how to optimise a Poisson regression model in the same manner.

In this blog post, we will fit a Poisson regression model by maximising its likelihood function using optimx() in R. As an example we will use the lung cancer data set included in the {survival} package.

In my last post I estimated the point estimates for a logistic regression model using optimx() from the optimx package in R. In this post I would like to contine with this model an try to find the standard error (SE) for the derived estimates.

In my last post I used the optim() command to optimise a linear regression model. In this post, I am going to take that approach a little further and optimise a logistic regression model in the same manner.

In this post I would like to show how to manually optimise a linear regression model using the optim() command in R. Usually if you learn how to fit a linear regression model in R, you would learn how to use the lm() command to do this.

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