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From |
"Cathy L. Antonakos" <cathya@umich.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: working with a vector and scalars (fwd) |

Date |
Mon, 9 Jun 2008 20:01:16 -0400 (EDT) |

Several syntax lines wrapped in the earlier post. Here they are again, with line breaks.

----

*Calculate reliabilities for place/hour groups.

egen N = count(crowdindx), by(place hour)

gen nr=1/N

mean nr, over(place hour)

mat b=e(b)

mat N=b'

matrix rownames N = `e(over_labels)'

loneway crowdindx placeXhr

matrix sj = N * (r(sd_w) * r(sd_w))

local tau = r(sd_b) * r(sd_b)

matrix t = J(48,1,`tau')

matrix J = t,sj

matrix rownames J = `e(over_labels)'

matrix colnames J = tau sigma_nj

preserve

drop _all

svmat J, names(col)

tempfile mat

save `mat'

use `mat'

gen lambda_j = tau / (tau + sigma_nj)

restore

*End of reliability analysis.

---------- Forwarded message ----------

Date: Mon, 9 Jun 2008 19:48:09 -0400 (EDT)

From: Cathy L. Antonakos <cathya@umich.edu>

To: statalist@hsphsun2.harvard.edu

Subject: Re: st: working with a vector and scalars

Here's a solution to my question about working with vectors and scalars. Thank you, Austin Nichols, for providing initial code and a hint about difficulty generating element-wise reciprocals.

I initially had a problem using the "placeXhr" variable in the -mean- command, because "placeXhr" is a string variable. That problem is solved by using both "place" and "hour" variables in -mean-.

The reliabilities in the sub-groups turned out to be very similar.

Cathy

----

*Calculate reliabilities for place/hour groups.

egen N = count(crowdindx), by(place hour)

gen nr=1/N

mean nr, over(place hour)

mat b=e(b)

mat N=b'

matrix rownames N = `e(over_labels)'

loneway crowdindx placeXhr

matrix sj = N * (r(sd_w) * r(sd_w)) local tau = r(sd_b) * r(sd_b) matrix t = J(48,1,`tau') matrix J = t,sj matrix rownames J = `e(over_labels)'

matrix colnames J = tau sigma_nj

preserve

drop _all

svmat J, names(col)

tempfile mat

save `mat'

use `mat'

gen lambda_j = tau / (tau + sigma_nj)

restore

*End of reliability analysis.

On Mon, 9 Jun 2008, Cathy L. Antonakos wrote:

Date: Mon, 9 Jun 2008 08:52:07 -0400 (EDT)

From: Cathy L. Antonakos <cathya@umich.edu>

Reply-To: statalist@hsphsun2.harvard.edu

To: statalist@hsphsun2.harvard.edu

Subject: Re: st: working with a vector and scalars

You're right. The "d" in the equation should be "s" (sigma-squared). I can get the reliability reported by -loneway- for the overall sample mean, but I want the reliabilities for each group.

Yes, I think it would be easier in Mata, but I don't know how to do that.

When I ran the syntax you provided, the -mean- command gave an error:

. mean nr, over(placeXhr)

no observations

r(2000);

If I can figure out that error, I think the approach will work.

On Sun, 8 Jun 2008, Austin Nichols wrote:

Date: Sun, 8 Jun 2008 21:00:38 -0400

From: Austin Nichols <austinnichols@gmail.com>

Reply-To: statalist@hsphsun2.harvard.edu

To: statalist@hsphsun2.harvard.edu

Subject: Re: st: working with a vector and scalars

Cathy L. Antonakos <cathya@umich.edu>

I take it you don't want the reliability reported by -loneway-? Not

sure what you want from your description... but I don't think that

equation looks right, and I don't have the ref handy. Can you confirm

that t cancels out? Is your calculated s equiv to d in your formula?

I don't see where you've tried to calculate something and gotten an

error, but try:

egen N = count(crowdindx), by(placeXhr)

g nr=1/N

mean nr, over(placeXhr)

mat b=e(b)

mat N=b'

loneway crowdindx placeXhr

scalar t = r(sd_b) * r(sd_b)

scalar s = r(sd_w) * r(sd_w)

mat lambda = s*N

mat li lamdba

But if you need to calculate the element-wise reciprocal, it's easier in Mata...

On Sun, Jun 8, 2008 at 8:23 PM, Cathy L. Antonakos <cathya@umich.edu> wrote:

I am working with scalars and a vector to estimate sample mean reliability by group, using a formula from Bryk & Raudenbush (1992) Hierarchical Linear Models: lambda-j = t / [ t / (d /Nj)] lambda-j is the reliability estimate for group j, t is between-group variance, d is within-group variance, and Nj is the group N. Nj is a vector. The other parameters (t, d) are scalars. Here's what I tried using -matrix- and -scalar-. The vector comes from a collapsed dataset. The scalars come from -loneway- using the original (uncollapsed) dataset. I have no trouble producing the vector and scalars, but can't figure out how to get them into the same matrix or file together so I can calculate the reliability esimates. Any help will be much appreciated. Cathy Antonakos -------------- . bys placeXhr: egen N = count(crowdindx) . preserve . collapse (max) N, by(placeXhr) . mkmat N . levelsof placeXhr, local(l) . matrix rownames N = `l' . matrix list N N[49,1] N 2_10 2 2_11 81 2_12 150 2_13 340 2_14 138 2_15 82 2_16 102 2_17 119 2_18 288 2_19 160 2_8 48 2_9 265 3_10 166 3_11 71 3_12 63 3_13 25 3_14 53 3_15 68 3_16 44 3_17 61 3_18 33 3_19 35 3_8 36 3_9 16 6_10 33 6_11 69 6_12 82 6_13 30 6_14 87 6_15 90 6_16 90 6_17 92 6_18 63 6_19 79 6_8 86 6_9 41 9_10 57 9_11 135 9_12 113 9_13 83 9_14 152 9_15 168 9_16 160 9_17 151 9_18 97 9_19 147 9_8 216 9_9 166 9_9 190 . restore . *create matrices from -loneway- parameter estimates. . preserve . loneway crowdindx placeXhr One-way Analysis of Variance for crowdindx: Crowding Index (wtd. sum) Number of obs = 5121 R-squared = 0.5384 Source SS df MS F Prob > F ------------------------------------------------------------------------- Between placeXhr 682650.69 47 14524.483 125.87 0.0000 Within placeXhr 585364.53 5073 115.38824 ------------------------------------------------------------------------- Total 1268015.2 5120 247.65922 Intraclass Asy. correlation S.E. [95% Conf. Interval] ------------------------------------------------ 0.54152 0.06135 0.42126 0.66177 Estimated SD of placeXhr effect 11.67412 Estimated SD within placeXhr 10.74189 Est. reliability of a placeXhr mean 0.99206 (evaluated at n=105.73) . return list scalars: r(lb) = .4212643949911408 r(ub) = .6617675540515819 r(se) = .0613539740927638 r(sd_w) = 10.74189173110937 r(sd_b) = 11.67412402231762 r(rho_t) = .9920556043556278 r(rho) = .5415159745213614 r(N) = 5121 . scalar t = r(sd_b) * r(sd_b) . scalar s = r(sd_w) * r(sd_w) . restore * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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