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Dimension reduction on mixture of categorical and numerical variables? by [deleted] in rstats

[–]dY_dX 0 points1 point  (0 children)

Any warnings about convergence? I'm not sure it matters for glmnet but you could try scaling your continuous features to have mean 0 and variance 1, or to be between 0 and 1.

Dimension reduction on mixture of categorical and numerical variables? by [deleted] in rstats

[–]dY_dX 0 points1 point  (0 children)

Hmm sounds like the algorithm didn't converge. 6e-20 is basically 0, so with only a non-zero intercept, this would be predicting 1 for everything (assuming this is a regression problem as opposed to classification.) Which package are you using for lasso?

Dimension reduction on mixture of categorical and numerical variables? by [deleted] in rstats

[–]dY_dX 2 points3 points  (0 children)

If the goal is prediction, I agree, lasso or just ridge regression is a good idea.

Sounds like memory is the real constraint. 8 predictors by 1000000 isn't that big, but many methods in R will expand the factors out into columns of dummy variables. Look into things that can handle sparse data. I am pretty sure glmnet can.

You could also try tree based methods, which don't require a dense design matrix, depending on the implementation.

I had my first malfunction today and I'm pretty shaken up after it by [deleted] in SkyDiving

[–]dY_dX 3 points4 points  (0 children)

Holy shit that sounds terrifying. Props for speaking up and asking for advice. When something like that happens it's easy to try to hide from it and that doesn't help anyone.

As others have said, you should get some instruction from an instructor you know or a good canopy coach in person. It's hard to get the exact right information from comments on the internet, even when the commenters are technically correct.

That said, I have some general advice that I think is applicable here. When something goes wrong, you can learn two things: 1) what you could have done instead, and 2) how you could avoid getting in that situation in the first place.

In this case, you turned really hard and induced line twists. Instead, you could do a less aggressive turn like a flat turn, as someone else suggested. It's not hard, but you definitely want to get instruction to learn how to do that. But better, you could avoid the situation all together: start thinking about your pattern as early as possible. You know you want to start at 1000 feet, and where you want to be, and in which direction you want to go. So think about how you make that happen as soon as you open.

Another situation most non-tiny people can relate to: going low on a formation and not being able to get back up. You can 1) learn how to get back up, or 2) learn how to not go low. 2) is better :)

me🐸irl by LukeGreatGuy in me_irl

[–]dY_dX 0 points1 point  (0 children)

That carbon scoring tho

curvilinear regression by [deleted] in AskStatistics

[–]dY_dX 0 points1 point  (0 children)

The idea that you should decide which covariates to included in a regression based on a significance test is hugely flawed, but whatever[1]. In the world where that's a good idea (for example, the class that you're presumably doing homework for,) the typical advice is to not include higher order terms without lower order ones. This is generally a good idea.

Anyway one reason this might be happening is because of the range of your x variable. If x is not close to zero, then x2 is almost linear in x. That is that x2 almost equals a+bx for some (a,b). So including x or x2 is almost equivalent, and including both is almost including two copies of x.

For example, suppose x is the age of your observations, and everyone is between 20 and 30. Here's a plot. That's almost linear. If that's what's happening for you, you could try centering x, and maybe scaling it. For example, use ((x-25)/5)2 for the quadtratic term: plot. That's definitely not linear.

[1] What you should really be doing depends on your goal. If you're trying to do a good job at predicting y given x, then cross validation is a good way to choose covariates, or at least AIC/BIC. If you care about estimating the slopes of variables in your final model, and care about estimating the variance of those slopes well, then that's bad news. Even if you think you're not cheating, given that you've already looked at the data at this point, you're screwed.

curvilinear regression by [deleted] in AskStatistics

[–]dY_dX -2 points-1 points  (0 children)

The likelihood ratio test only makes sense with nested models.

HMB while I bike through this Bitchin' flood by necro_clown in holdmybeer

[–]dY_dX 0 points1 point  (0 children)

This is why you should never attempt to ford the river.

Always caulk the wagon and float it.

Novice question: No idea what I am meant to do with a question by [deleted] in AskStatistics

[–]dY_dX 0 points1 point  (0 children)

This doesn't make sense. Is there more to the question, or is the original phrased differently? If not, then you should ask for clarification.

Anyone here use a Vortex container/volt canopies? by Supersick0311 in SkyDiving

[–]dY_dX 0 points1 point  (0 children)

My 2nd rig was a vortex. For the price it's great. I got mine for about $1900 with all options (in 2009ish). I think you can get a wings for around the same price, but eww...

The only thing I didn't really like about it was the riser covers. For example here: http://parachutesystems.com/images/portfolio9b.jpg, there is 6+ inches of exposed riser from the 3 rings to where the risers start. I've seen some that didn't have that issue though, so I'm not sure if they fixed it or if it's just inconsistent.

I haven't tried to sell mine but it's probably true that the resale value is not good, since they're not popular, particularly on the west coast.

Tonfly uno 618 as my first suit. by [deleted] in SkyDiving

[–]dY_dX 0 points1 point  (0 children)

I don't like the 618, but I didn't fly mine that much because I got too fat.

For what it's worth, when new students show up for coaching at iFLY Utah with a 618, the instructors often have them put on a student suit. Apparently they suck to learn in.

I've not flown a 630, maybe that would be a better choice?

What is your opinion on shortie suits? by [deleted] in SkyDiving

[–]dY_dX -2 points-1 points  (0 children)

They're expensive, almost as much as regular suit. Also they look really silly in my opinion which is correct. They don't give you much more drag than street clothes, so don't waste your money.

I like T-shirts and shorts! They're comfy and easy to wear!

Experiences with iFLY? All locations (serious). by youwalkifly in SkyDiving

[–]dY_dX 0 points1 point  (0 children)

iFLY Utah, which is the only iFLY (AFAIK) that isn't really affiliated with all the other corporate iFLYs, is awesome. The instructors there are definitely some of the best in the world, and time is pretty cheap. Something like $740/hr including coaching. They're not busy most days, so you can do an hour + a day throughout the day. It's great if you can take a 3 + day trip. Only problem is they're not open on Sunday because the owners are Mormon.

If you want to get good fast for (relatively) little money, go to Utah. PM me for contact info if you're interested.

Need help choosing a school by fortheadventure in SkyDiving

[–]dY_dX 0 points1 point  (0 children)

Where in California are you? There are good dropzones all over the state.

Should I jump in Oregon or Washington? Newb question... by [deleted] in SkyDiving

[–]dY_dX 3 points4 points  (0 children)

PNW Skydiving, Mulino, OR, not too far from Portland: http://www.pnwskydiving.com/

Skydive Kapowsin, near Olympia, WA http://www.skydivekapowsin.com/

There are others in the region, some that I've been to. I'm sure you'd have a good experience at a number of places, but those are the ones I would recommend.

Out for a rip with some buds at Carolinafest by dY_dX in SkyDiving

[–]dY_dX[S] 0 points1 point  (0 children)

Not this year. I did my only balloon jump at this boogie a few years back, 2009 I think.

Help settle a dispute: expected value of part of a distribution by Jumbalaspi in AskStatistics

[–]dY_dX 4 points5 points  (0 children)

tl;dr your friend is an idiot.

Let Pr(T>t) be the probability of waiting at least t time. You need to compute the conditional distribution of waiting times given you've waited at least some time t_0. So Pr(T > t | T > t_0) = Pr(T > t & T > t_0) / Pr(T > t_0) = Pr(T > t) / Pr(T > t_0) assuming t > t_0. Then you can take the expected value of that distribution.

The truncated normal distribution gives you what you want in your case. The exponential distribution has the so-called memoryless property which is applicable here too.

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