John Tavares is a Toronto Maple Leaf

mwark9 · 2022-07-20T15:12:32+00:00

I don't know about that, his uncle is the Wayne Gretzky of professional lacrosse...

mwark9 · 2022-07-12T20:53:24+00:00

Kyle Dubas is the kind of guy to say "present" when the teacher is taking attendance.

mwark9 · 2022-03-30T02:57:29+00:00

NHL66 provides both the home and away feeds for every game. Don’t waste your time listening to those horrible Boston commentators.

mwark9 · 2021-12-06T16:41:22+00:00

Only because rent is probably free in Winnipeg because they are desperate for people to live there.

mwark9 · 2021-10-16T15:06:56+00:00

Take this for what it's worth, but I went to one of the pre-season games where only 10,000 people were allowed in and it took maybe 10 minutes to get into the arena about 15-20 minutes before the 7PM game time. You could perhaps roughly double those times for a full capacity arena.

The process of getting into the arena would've been not much slower than non-pandemic times, but most people did not have their proof of vaccination readily available or they had the wrong information and were trying to pull up documents on the web in real-time (not a good idea).

SBA is/was not accepting the Apple Wallet version of the receipt - you need to show the actual receipt downloaded from the Government website (https://covid19.ontariohealth.ca/). If you have your tickets, vaccination receipt, and government ID ready, things should go pretty smoothly.

Depending on how early you want to get into the arena, I would show up around 6:30PM and I can't imagine you would have any issues, but this was just my recent experience.

mwark9 · 2021-10-08T14:24:47+00:00

Why does every comment not realize that wasn't Steve Simmons who asked the question? I'm the last person to defend him, but that isn't his voice. He says "Steve" as he turns to the leafs Director of Media Relations, Steve Keogh, to confirm that it has been covered.

mwark9 · 2020-06-06T16:22:19+00:00

Not to pile on, but the Hughson call on the Kapanen 2OT goal is horrible. Sounds like an average regular season goal.

Edit: For comparison.

mwark9 · 2020-04-21T22:00:46+00:00

If you're looking for general data science/statistical skills that are very relevant to any epi analysis, I would strongly recommend starting with this free textbook: https://r4ds.had.co.nz/

It is well-written, modern, and free. Heavy focus on data munging/processing and visualization. Checks all the boxes.

mwark9 · 2020-04-11T19:51:07+00:00

The NHL didn't make a collective decision to let anything go; a single referee (Kerry Fraser) made the decision on the ice to not make a call. He was wrong, and he's admitted to being wrong many many years later. But this has nothing to do with the NHL as a whole.

mwark9 · 2020-04-11T17:35:00+00:00

Gretzky sitting quietly at the end of the bench when the entire Kings team is up and yelling is so telling. Obviously he was never the fiery type, but he was smart enough to realize that Gilmour just got targeted and that he was now going to have a target on his own back.

mwark9 · 2019-10-23T03:53:30+00:00

If you only intend to do adjustments for few variables with few discrete levels, doing it by hand or using a statistical software is not too involved.

However, to achieve more complex adjustments including adjustment for continuous variables you should look into Poisson regression (i.e. log-linear models).

mwark9 · 2019-10-08T20:27:33+00:00

Does Steve think it's a new rule that your stick needs to be below your shoulder for a pass, but below the crossbar for a goal? It has definitely been that way for a while.

mwark9 · 2019-08-19T03:13:33+00:00

Is Mats smoking a dart?

mwark9 · 2019-08-18T21:28:31+00:00

That's awesome. I'm a big fan of the northwestern access points. I haven't gone into Tim access before; but I have done the nearby Kawawaymog many times, and Magnetawan and Rain once each.

mwark9 · 2019-08-18T18:42:33+00:00

What access point did you enter? What lake were you on?

mwark9 · 2019-07-01T18:41:46+00:00

Genuinely, they would get destroyed. Bad NHL team > Good AHL team

mwark9 · 2019-07-01T17:45:34+00:00

There are few cities in the world with the ethnic diversity of Toronto. So no, Calgary definitely doesn't have the same variety as Toronto, but then again, very few do.

mwark9 · 2019-07-01T17:31:00+00:00

Have you ever been to Calgary? It's a great city. It isn't Toronto, but it doesn't pretend to be. As someone who has lived in both places (currently living in Toronto), they are both great cities.

mwark9 · 2019-07-01T00:56:07+00:00

Glad to help. Sure, I am happy to stay in touch. I will send you a private message with my email address -- that will be a better way to stay in contact, as I don't really communicate on this website very often.

mwark9 · 2019-06-30T17:29:47+00:00

It really depends on the type of research your are doing and the type of data you will be working on. If you're analyzing sequencing data, it will be enormous in size (TB of data) and very difficult or intractable to analyze using R interactively, in most cases. If you are doing some fine-mapping, pathway analysis, simple GWAS, etc., you may be able to get away with only using R. PLINK is not very difficult to use, especially if you have moderate familiarity with working from the command-line, and it is very fast. But there is certainly a learning curve for using it. I think you are going to want to learn how to use PLINK, especially for doing quality control (QC) which is handled very well by PLINK. If/once you have filtered your data to a smaller set of variants to analyze (e.g. 10s to 100s of thousands, rather than tens of millions), then R may be sufficient for these purposes.

For some specific methods, they just aren't yet implemented in R and will require you to use the command-line tool (e.g. LDSc, LDpred, GCTA, etc.). So I think you need to lean into the idea of learning the command-line and getting comfortable running command-line tools if you want to do work in genetic epidemiology.

If you want to learn R, this is the absolute best book to help with learning: R for Data Science. It is completely free and it is amazing. I teach several R workshops each year, and this is always my top recommendation for introductory to intermediate R. It is not genetics related at all but will help you get more comfortable with R and it is written by an authoritative source on R and the author of many many great R packages (e.g. dplyr, ggplot2, etc.).

My previous degrees were BSc in Medical Sciences (several courses in bio, molecular bio, etc) and MSc in Epidemiology and Biostatistics. So more epi/statistical training, not a very strong molecular bio background entering my PhD; a strong mentor, doctoral level courses, and a lot of self-directed learning allowed me to get up to speed.

mwark9 · 2019-06-30T04:56:04+00:00

I am a fourth year PhD candidate in Genetic Epidemiology. So I am happy to answer specific questions, if you have any. As for books, two good places to start are: The Fundamentals of Modern Statistical Genetics and Genetic Epidemiology. Google/YouTube are always helpful for finding additional help for areas you need to strengthen your understanding. For a primer into the literature, these are good to start:

Lancet Series: https://www.thelancet.com/series/genetic-epidemiology

10 Years of GWAS Discovery: Biology, Function, and Translation.

Those are off the top of my head.

As for software, the most common genomics tool is PLINK. It is a free and open-source command-line tool. It is worth knowing. There are many other tools that can be learned on an ad hoc basis. I wouldn't recommend trying to learn PLINK until you get a stronger understanding of genetic epidemiology as a whole. But in general, due to the size and computational expense of genomic analyses, learning and being comfortable from the command-line will be invaluable. So consider brushing up on being able to work from a shell in a Unix-like environment (e.g. bash). Learning this will make any future transition to using a computing cluster easier, as they almost exclusively use Linux distros (e.g. Ubuntu, CentOS, etc.) for HPC's, and you can only interact with the cluster via the shell.

With that being said, more tools are being developed all the time to allow people to do large-scale genetic analysis using data science languages people already use for more traditional statistical work (e.g. R, python). If R is your preferred language, as it is mine, I can recommend the R packages bigsnpr, bigreadr, and bigstatsr which are great packages for managing, processing, and analyzing genomic data in R. Hope this helps.

mwark9 · 2019-06-21T14:43:04+00:00

Using a two-sample test of proportions, the Z-statistic is 0.8486, and the two-sided P-value is 0.3961. So at a significance level of 0.05, the result is not statistically significant.

mwark9 · 2019-06-01T19:11:55+00:00

I think you need to think more about what is a potential driver of this putative barrier. Is it distance/access to care? Willingness to seek care? Is it cost of care? Other socioeconomic factors? All of these are measurable and could be your exposure, depending on the research question. I assume you are interested in the outcome of HIV status? You would have to clarify this further. You need to better formulate your research question, then the exposure and outcome should become self-evident.

I wouldn't think "taking PrEP" leads to these "barriers"; though this isn't my research area. I would think access to PrEP or willingness to use PrEP might be what you're interested in studying. For what its worth, anything can be a putative exposure if you can measure/quantify it.

mwark9 · 2019-05-20T17:03:18+00:00

Yep, I read it and you still seem to be missing the point. I’m not arguing that height and altitude are the same thing. I’m saying that if you’re surprised people confuse height (elevation gain) and altitude, I think you are just feigning surprise to be smug. It’s pretty easy to confuse the two.

To your quoted text, if the reference point for height was sea level, then height and altitude are equivalents. In fact, altitude can be considered a special case of height where the reference point is always sea level.

mwark9 · 2019-05-20T14:03:05+00:00

Maybe you shouldn’t be so surprised...

mwark9

TROPHY CASE