Cathy L. Antonakos <[email protected]>
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 <[email protected]> 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/
