A systematic search for attributes that make a fig species invasive, weedy or vulnerable to extinction. An account to chronicle the journey of research and the writing of a scientific paper.

Wednesday, March 11, 2009

"Models arise from questions about biology and the manner in which biological systems function." p. 16

"We recommend developing a set of candidate models prior to intensive data analysis, selecting one that is "best", and estimating the parameters of that model and their precision (using maximum likelihood or least square methods)." p. 22

"AIC is useful in selecting the best model in the set; however, if all the models are very poor, the AIC will still select the one estimated to be best, but even that relatively best model might be poor in an absolute sense." p. 62

"It is not the absolute size of the AIC value, it is the relative values over the set of models considered, and particularly the differences between AIC values, that are important." p.63

"AICc merely has an additional bias-correction term." "Unless the sample size is large with respect to the number of estimated parameters, use of AICc is recommended." p. 66

"change in AIC = AICi - AICmin"
"The model estimated to be best has change in AIC ~ 0" p. 71

"We can order teh change in AIC from smallest to largest, and the same ordering of the models indicates how good they are as an approximation to teh actual, expected K-L best model." p. 72

Burnham and Anderson, as below

Tuesday, March 10, 2009

Modeling, Model Selection and Multimodel Inference

"A model is a representation of the world, but it is rarely a perfect representation. There will always be some differences between the model and the world, that is some error. If we could build a perfect copy, it would not be a model, it would be a duplicate."

"DATA = MODEL + ERROR"

From "Applying Regression and Correlation" by Miles, J. and Shevlin, M. (2001)

"This book is about making valid inferences from scientific data when a meaningful analysis depends on a model of the information in the data."

"There are many studies to understand our world; models are important because of the parameters in them and relationships expressed between and among variables. These parameters have relevant, useful interpretations, even when they relate to quantities that are not directly observable. Science would be very limited without such unobservables as constructs in models."

"We do not believe that model selection should be treated as an activity that precedes the analysis; rather, model selection is a critical and integral aspect of scientific data analysis that leads to valid inference."

"Often, one first develops a global model (or set of models) and then derives several other plausible candidate (sub)models postulated to represent good approximations to information at hand. This forms the set of candidate models."

"... selection of an estimated "best approximating model" from the a priori set of candidate models."

"Modeling is an art as well as a science and is directed toward finding a good approximating model of the information in empirical data as the basis for statistical inference from those data."

From "Model Selection and Multimodel Inference" by Burnham, K. P. and Anderson, D. R. (2002)

Tuesday, March 3, 2009

I've come to such a stage that I cringe at seeing Chinese taxonomic description more than Latin or Spanish.

It's time to start with the thesis, with or without results.

It's time to prioritize and work smartly.

Proverbs 27:12
A prudent person foresees the danger ahead and takes precautions. The simpleton goes blindly on and suffers the consequences.