Valeriano Parravicini
2010-May-17 17:58 UTC
[R] Quantile regression - violation of independence
I am trying to perform quantile regression (using quantreg package) and I am particularly interested to know whether the technique requires independence of observations. I am an ecologist and, in particular, I collected data of abundance of a species in 15 location around an island. In each location abundance data of the species (response variable) have been collected in about 20 replicate sampling units. Yet, I have just one measure of my explanatory variables (I have two explanatory variables) for each location. So, for each value of the explanatory variables I have 20 values of the response variable. Since my data have not normal distribution and do not show homogeneity of variance I would use quantile regression. My main concern is about violation of independece because I can not consider my 20 measure of abundance as independent since they have been collacted in the same location (i.e. they actually are replicates). However, I would not like to loose the information about the within-location variation by averaging my response variable at location level. I am not skilled in quantile regression; this is my first attempt to employ such a technique, but in a paper on econometrics I found that quantile regression do not require independence of observations. This is the only reference I found about that. Is it true? Can anyone suggests me any reference about that? Moreover, I have two explanatory variables for each location and I am conducting quantile regression using backward selection. Particularly, I am performing the regression separately for a number of tau and each time I perform backward selection. What happens is that the "best model" changes accordingly with the tau setted. For instance, using tau = 0.5, backward selection indicate me that both explanatory variables are significant. Yet, setting tau = 0.75 just one of the two explanatory variables is found significant and backward selection drops the other one. Is that a correct way to conduct quantile regression with more than one explanatory variable: i.e. performing backward selection for each tau I am interested on ? Thank you for your help Valeriano
This is not an R question, though you may receive help from this list. But you would probably do better posting on a statistical list, especially one focused on ecology. Bert Gunter Genentech Nonclinical Biostatistics -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Valeriano Parravicini Sent: Monday, May 17, 2010 10:59 AM To: r-help at r-project.org Subject: [R] Quantile regression - violation of independence I am trying to perform quantile regression (using quantreg package) and I am particularly interested to know whether the technique requires independence of observations. I am an ecologist and, in particular, I collected data of abundance of a species in 15 location around an island. In each location abundance data of the species (response variable) have been collected in about 20 replicate sampling units. Yet, I have just one measure of my explanatory variables (I have two explanatory variables) for each location. So, for each value of the explanatory variables I have 20 values of the response variable. Since my data have not normal distribution and do not show homogeneity of variance I would use quantile regression. My main concern is about violation of independece because I can not consider my 20 measure of abundance as independent since they have been collacted in the same location (i.e. they actually are replicates). However, I would not like to loose the information about the within-location variation by averaging my response variable at location level. I am not skilled in quantile regression; this is my first attempt to employ such a technique, but in a paper on econometrics I found that quantile regression do not require independence of observations. This is the only reference I found about that. Is it true? Can anyone suggests me any reference about that? Moreover, I have two explanatory variables for each location and I am conducting quantile regression using backward selection. Particularly, I am performing the regression separately for a number of tau and each time I perform backward selection. What happens is that the "best model" changes accordingly with the tau setted. For instance, using tau = 0.5, backward selection indicate me that both explanatory variables are significant. Yet, setting tau = 0.75 just one of the two explanatory variables is found significant and backward selection drops the other one. Is that a correct way to conduct quantile regression with more than one explanatory variable: i.e. performing backward selection for each tau I am interested on ? Thank you for your help Valeriano ______________________________________________ R-help at 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.