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

Bill.Shipley@USherbrooke.ca

 <http://callisto.si.usherb.ca:8080/bshipley/>
http://callisto.si.usherb.ca:8080/bshipley/

 


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