cis | Computer Integrated Surgery 600.445 Johns Hopkins

Well, I don't have a global answer, but for one rest you could use \tweak:

Your struct tags are not well formed. Remove the space between json: and the string "..." in the struct tags. i.e. it MUST be json:"..." not json: "...".

And the reason some fields are correctly unmarshaled even with not well-formed struct tags is because the fields' names match the json property names.

If you’re ever curious about what’s going on with a package, I would encourage you to have a quick look through the github. It may be a little overwhelming at first, but you can learn quite a bit by reviewing what others have done.

Looking at the yarrr github, we can see in line 750 the package calculates confidence intervals using a t-interval. You are calculating the 95% confidence interval using a z-interval. The methods are different, so we’ll get different results. For smaller sample sizes (less than 30 as a general rule), t-intervals are more appropriate.

The z-interval assumes we know the population standard error. For smaller sample sizes, you can imagine our estimates are going to be pretty varied and the standard deviation of the sample may not be representative of the population. The confidence intervals that result don’t account for this extra uncertainty and so they don’t have enough coverage to truly be a 95% confidence interval as we're violating the assumption that the population standard error is known.

Let’s do the calculation with your example data just to see the difference and make sure we are matching the package.

The gtstummary package website has a solution to this. Look for "Wald confidence interval" here or see below for the my_tidy function from there. You can calculate the Wald confidence interval separately and bind it to the table.

There is likely a more elegant solution, but a workaround is to inspect the confidence interval and manually draw on the whiskers.

Having a False hypothesis is something common and not at all problematic in Coq. In fact, if you look at the negation of a proposition P, it is defined as P -> False. In other words, you can derive a contradiction from having P.

There is even the tactic exfalso which proves any goal as long as you provide a proof of False. This means that if you have contradictory hypotheses then you can conclude your proof.

Here is an even shorter proof:

That comment should have been an answer: You're doing

Your span is too small (see this), so there's too little points to estimate your confidence interval. So for example if you do:

The following post describes an alternative approach without for-loop and filter. Similar loop-free solutions can be implemented using the common nls function and lapply in "base" R or group_by in "tidyverse".

The growthrates package does not contain a von Bertalanffy function, so it has to be provided as user supplied model, as described in the package vignette. Here I borrowed the function from package FSA and adapted it accordingly: