Dr Hesterberg: "Independent" and "dependent" were used for convenience. A person selling hot dogs would render "HD" the Y variable. The real cause of both WL and HD is likely hormonal derangement associated with diabetes.
One other question. A log negative binomial regression m.ln<-glm.nb( Count ~ ns( X1, df=4 ) * ns( X2, df=4 ) + ns( X3, df=4 ) + X4 + offset( log( Total ), data=M) could not be made to predict values on new data, even though GLM assumptions were not violated. Why does predict( m.ln, newdata ) not work? This is vital if one is to draw a graph showing the relationship of X1 and X2 to Y. Thank you so much for your wisdom. Mitchell Wachtel Tim Hesterberg-2 wrote: > > You're mixing up two concepts here, > - splines > - bootstrap confidence intervals > Separating them may help cut the confusion. > > First, to do a bootstrap confidence interval for a difference in > predictions > in the linear regression case, do: > > repeat 10^4 times > draw a bootstrap sample of the observations (subjects, keeping x & y > together) > fit the linear regression to the bootstrap sample > record the difference in predictions at the two x values > end loop > The bootstrap confidence interval is the range of the middle 95% of > the recorded differences. > > For a spline, the procedure is the same except for fitting a spline > regression: > > repeat 10^4 times > draw a bootstrap sample of the observations (subjects, keeping x & y > together) > fit the SPLINE regression to the bootstrap sample > record the difference in predictions at the two x values > end loop > The bootstrap confidence interval is the range of the middle 95% of > the recorded differences. > > Tim Hesterberg > > P.S. I think you're mixing up the response and explanatory variables. > I'd think of eating hot dogs as the cause (explanatory variable), > and waistline as the effect (response, or outcome). > > P.P.S. I don't like the terms "independent" and > "dependent" variables, > as that conflicts with the concept of independence in probability. > "Independent" variables are generally not independent, and the > "dependent" > variable may be independent of the others. > > >There appear to be reports in the literature that transform continuous > >independent variablea by the use of splines, e.g., assume the > dependent > >variable is hot dogs eaten per week (HD) and the independent variable > is > >waistline (WL), a normal linear regression model would be: > > > >nonconfusing_regression <- lm(HD ~ WL) > > > >One might use a spline, > > > >confusion_inducing_regression_with_spline <- lm(HD ~ ns(WL, df = 4) > ) > > > >Now is where the problem starts. > > > >>From nonconfusing_regression , I get, say 2 added hot dogs per > week for each > >centimeter of waistline along with a s.e. of 0.5 hot dogs per week, > which I > >multiply by 1.96 to garner each side of the 95% c.i. > >If I want to show what the difference between the 75th percentile (say > 100 > >cm) and 25th percentile (say 80 cm) waistlines are, I multiply 2 by > >100-80=20 and get 40 hot dogs per week as the point estimate with a > similar > >bumping of the s.e. to 10 hot dogs per week. > > > >What do I do to get the point estimate and 95% confidence interval for > the > >difference between 100 cm persons and 80 cm persons with > >confusion_inducing_regression_with_spline ? > > > >Best regards. > > > >Mitchell S. Wachtel, MD > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- View this message in context: http://r.789695.n4.nabble.com/Bootstrap-95-confidence-intervals-for-splines-tp3408813p3411558.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
