Hello. I want to estimate the predicted values and standard errors of Y=f(t) and its first derivative at each unique value of t using the smooth.spline function. However, the data (plant growth as a function of time) show substantial heterogeneity of variance since the variance of plant mass increases over time. What is the consequence of such heterogeneity of variance in terms of bias in the estimate of the predicted value of Y and its first derivative? I could Ln-transform the data to achieve homogeneity of variance, but this would give me the mean of Ln(Y) at each time (i.e. the mode of Y when back-transformed) and the derivative of Ln(Y) with time (i.e. d(Ln(Y))/dt = dY/YDt), not dY/dt.
Can anyone suggest the best strategy for solving this problem? Bill Shipley Subject Matter Editor, Ecology North American Editor, Annals of Botany Département de biologie, Université de Sherbrooke, Sherbrooke (Québec) J1K 2R1 CANADA [EMAIL PROTECTED] <http://callisto.si.usherb.ca:8080/bshipley/> http://callisto.si.usherb.ca:8080/bshipley/ [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
