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From |
Lee Sieswerda <Lee.Sieswerda@tbdhu.com> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: graph mean and sd over/by category |

Date |
Mon, 10 Feb 2003 18:51:46 -0500 |

I was writing a reply to George Hoffman's question, when Vince Wiggins reply popped into my mail box. I have a question for Vince. First, here was what I was suggesting for George. bysort foreign: egen mean = mean(weight) bysort foreign: egen sd = sd(weight) bysort foreign: gen ub = mean + invttail(_N-1,.025)*(sqrt((sd^2)/_N)) bysort foreign: gen lb = mean - invttail(_N-1,.025)*(sqrt((sd^2)/_N)) twoway (rcap lb ub foreign) (scatter mean foreign) This gives the same results as -ci-. Specifically, it gives a 95% CI with the t critical value based on _N-1 observations within strata (of foreign in this case). It is -ci-'s results that I gather Nick Cox is using to produce his new -ciplot- (pardon me Nick, if I'm misrepresenting you). Then, I read your reply suggesting the use of -predictnl-, which is intriguing. Vince, if I'm understanding your post correctly, I could obtain a 95% CI for the mean of weight by foreign like so: regress weight foreign predictnl yhat=predict(), ci(lb ub) When I do so, I get the following upper and lower bounds: . bysort foreign: sum ub lb ____________________________________________________________________________ ___ -> foreign = Domestic Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- ub | 52 3491.338 0 3491.338 3491.338 lb | 52 3142.893 0 3142.893 3142.893 ____________________________________________________________________________ ___ -> foreign = Foreign Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- ub | 22 2583.76 0 2583.76 2583.76 lb | 22 2048.058 0 2048.058 2048.058 These differ from what -ci- produces: . ci weight, by(foreign) ____________________________________________________________________________ ___ -> foreign = Domestic Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+-------------------------------------------------------------- - weight | 52 3317.115 96.4296 3123.525 3510.706 ____________________________________________________________________________ ___ -> foreign = Foreign Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+-------------------------------------------------------------- - weight | 22 2315.909 92.31665 2123.926 2507.892 Now, I gather that the difference is a results of this message that I received after running -predictnl-: . predictnl yhat=predict(), ci(lb ub) note: Confidence intervals calculated using t(72) critical values. So, here is the dumb question. For what George is looking for (and many others I'm sure), should a person be using t critical values based on the total sample (_N-2=72), or based on the sample within strata (_N-1=21 and _N-1=51)? Thanks (and sorry for the long posting), Lee Lee Sieswerda, Epidemiologist Thunder Bay District Health Unit 999 Balmoral Street Thunder Bay, Ontario Canada P7B 6E7 Tel: +1 (807) 625-5957 Fax: +1 (807) 623-2369 Lee.Sieswerda@tbdhu.com www.tbdhu.com > -----Original Message----- > From: vwiggins@stata.com [SMTP:vwiggins@stata.com] > Sent: Monday, February 10, 2003 5:55 PM > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: graph mean and sd over/by category > > Among other things, George Hoffman <ghoffman@mcw.edu> asks, > > > [...] fitted curves under scatter plots look beautiful - can the > > regression coefficients from fplotci or qplotci be captured somehow, > > as poor-man's curve fit? > > I think George is referring to the -fpfitci- and -qfitci- plot types of > -graph twoway-. If so, he can readily perform the regressions that > produced > the graphs. > > -qfitci- just performs a quadratic regression. If we use the auto data, > -sysuse auto-, the lines for the graph command, > > . twoway qfitci mpg weight > > are the predictions of the quadratic fit, > > . gen weight2 = weight^2 > . regress mpg weight weight2 > > The coefficients can be seen in the output of -regress-, or manipulated in > the > usual way through the saved results. > > If George wants to add the predictions, and their CIs to his dataset, he > can > type, > > . predictnl mpg_hat2 = predict() , ci(ci_low ci_high) > > > This is a very simple application of -predictnl-, Bobby Gutierrez > <rgutierrez@stata.com> said more in a prior post, but it lets us get both > the > predictions and their CIs with one command. > > We could then get a graph similar to our earlier -twoway qfitci-, by > typing, > > . twoway rarea ci_low ci_high weight, sort || line mpg_hat2 weight, > sort > > which we will immediately think looks ugly and decide to relabel the CI in > the > legend, option -legend(label())-, and change the fill color of the CI to > be > the standard for our scheme, option -p(ci)-. > > . twoway rarea ci_low ci_high weight , sort p(ci) || > line mpg_hat2 weight , sort legend(label(1 "CI")) > > > The -fpfitci- plot type just uses -fracpoly- as the engine to produce the > fits, much like -regress- is used for the quadratic fit. For our example, > the > corresponding -fracpoly- estimation command is, > > . fracpoly regress mpg weight > > and we can repeat the rest of the story, or just use -fracplot-, to plot > the > fit and CI. > > > -- Vince > vwiggins@stata.com > > * > * 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/

**Follow-Ups**:**RE: st: graph mean and sd over/by category***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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