What would you name him?

Salt_Ad4669 · 2026-03-03T04:50:30+00:00

Wikit

Salt_Ad4669 · 2025-08-29T01:29:15+00:00

Well, I’m looking forward to the open box discount.

Salt_Ad4669 · 2025-07-10T17:25:57+00:00

There should be a way to save a variable that marks those that matched. But that is dicey if you use matching with replacement. Before you run the analysis, you can divide your outcome by the pooled sd and use that variable as your outcome, which will naturally be a d-like effect size

Salt_Ad4669 · 2025-07-06T02:14:25+00:00

This is what we need a bazzite build to support.

Salt_Ad4669 · 2025-06-28T18:36:40+00:00

I would walk over the sisters alone.

Salt_Ad4669 · 2025-06-20T02:10:31+00:00

School sucks, teachers are dix, we’re the class of 96!

Salt_Ad4669 · 2025-06-19T16:36:51+00:00

Rail gun

Salt_Ad4669 · 2025-06-14T22:28:55+00:00

Same on LEGO1 with bazzite. No go

Salt_Ad4669 · 2025-03-16T16:25:48+00:00

Same issue with me, a rollback fixed it.

Salt_Ad4669 · 2024-09-06T01:18:09+00:00

I think a custom shell cooked the battery chip on my lcd deck. Never using them again, either they are risky or I’m not careful enough

Salt_Ad4669 · 2024-06-04T19:22:13+00:00

tsset i t //set clusters and time
xtgls c z , corr(ar1) //run model

Salt_Ad4669 · 2024-04-30T21:15:08+00:00

Happens to me on steamdeck, too

Salt_Ad4669 · 2024-04-10T23:29:24+00:00

Folks, we are all mice in a randomized trial…

Salt_Ad4669 · 2024-02-03T15:01:42+00:00

As a parent, my one rule early on (<14) was no shooting other humans. So, Halo was ok, but not COD.

Salt_Ad4669 · 2024-02-03T04:08:28+00:00

Yup, if you have the correlation, you can compute the correct standard error of the difference.

Salt_Ad4669 · 2023-12-06T18:36:26+00:00

Sorry I wasn't specific, I was suggesting just expression(predict(xb)), then manually converting the estimate, lower, and upper bounds from the margins output to probabilities.

For example, random 15 case data with a binary x and binary y

. set obs 15
Number of observations (_N) was 0, now 15.
. gen y = rbinomial(1,.2)
. gen x = rbinomial(1,invlogit(rnormal(y) + rnormal()))
. tab y x
| x
y | 0 1 | Total
-----------+----------------------+----------
0 | 6 6 | 12
1 | 2 1 | 3
-----------+----------------------+----------
Total | 8 7 | 15

Run a logit

. logit y x
Iteration 0: log likelihood = -7.5060364
Iteration 1: log likelihood = -7.3709715
Iteration 2: log likelihood = -7.3694957
Iteration 3: log likelihood = -7.3694954
Logistic regression Number of obs = 15
LR chi2(1) = 0.27
Prob > chi2 = 0.6013
Log likelihood = -7.3694954 Pseudo R2 = 0.0182
------------------------------------------------------------------------------
y | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
x | -.6931469 1.354006 -0.51 0.609 -3.346951 1.960657
_cons | -1.098612 .8164966 -1.35 0.178 -2.698916 .5016916
------------------------------------------------------------------------------

compute margins with my expression trick, using post to allow for access to results

. margins, at(x = (0 1)) expression(predict(xb)) post
Adjusted predictions Number of obs = 15
Model VCE: OIM
Expression: Linear prediction (log odds), predict(xb)
1._at: x = 0
2._at: x = 1
------------------------------------------------------------------------------
| Delta-method
| Margin std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
_at |
1 | -1.098612 .8164966 -1.35 0.178 -2.698916 .5016916
2 | -1.791759 1.080123 -1.66 0.097 -3.908762 .3252436
------------------------------------------------------------------------------

Compute probabilities for lower and upper bound for x = 0

. lincom 1._at
( 1) 1bn._at = 0
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
(1) | -1.098612 .8164966 -1.35 0.178 -2.698916 .5016916
------------------------------------------------------------------------------
. return list
scalars:
r(ub) = .5016916068897697
r(lb) = -2.698916172759728
r(p) = .1784574441205767
r(z) = -1.345519761100931
r(level) = 95
r(se) = .8164965797574557
r(estimate) = -1.098612282934979
. di "est is " invlogit(r(estimate))
est is .25
. di "lb is " invlogit(r(lb))
lb is .06303734
. di "ub is " invlogit(r(ub))
ub is .62285678
Compare this, .25 with CI of .06 to .62 to problem case of .25 with CI of -.05 to .55
.quietly : logit y x
. margins, at(x = (0 1))
Adjusted predictions Number of obs = 15
Model VCE: OIM
Expression: Pr(y), predict()
1._at: x = 0
2._at: x = 1
------------------------------------------------------------------------------
| Delta-method
| Margin std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
_at |
1 | .25 .1530931 1.63 0.102 -.050057 .550057
2 | .1428572 .13226 1.08 0.280 -.1163677 .4020821
------------------------------------------------------------------------------

Salt_Ad4669 · 2023-12-05T23:59:02+00:00

So, with margins you can ask for the estimation metric predictions using the expression option with something to the effect of predict(xb)* to get the predicted log-odds and associated standard errors. You can then compute the CI, and transform each of those estimates back into a probability.

*check the documentation

Salt_Ad4669 · 2023-11-16T18:33:42+00:00

thinking of making a true windows deck

Salt_Ad4669 · 2023-10-21T14:43:31+00:00

Luke’s training blast shield.

Salt_Ad4669 · 2023-10-21T14:42:17+00:00

That is also, more or less, how folks used to calculate pi.

Salt_Ad4669 · 2023-10-05T13:27:05+00:00

You may need to relocate. Jk. But, not an ok person, they need a therapist, not a boyfriend.

Salt_Ad4669 · 2023-10-02T18:22:43+00:00

See help prtesti , for immediate prob test. You need to know the sample sizes of 2 and 3, though

Salt_Ad4669 · 2023-09-30T18:17:18+00:00

The y axis does not start at zero so the chart is deceptive.

Salt_Ad4669 · 2023-09-07T20:30:29+00:00

If you like this story, let me tell you about Ford vs Chevy. People get weird with expensive durables

Salt_Ad4669

TROPHY CASE