search for: wshadish

...Sciences, Humanities and Arts 5200 North Lake Rd Merced CA 95343 Physical/Delivery Address: University of California Merced ATTN: William Shadish School of Social Sciences, Humanities and Arts Facilities Services Building A 5200 North Lake Rd. Merced, CA 95343 209-228-4372 voice 209-228-4390 fax wshadish@ucmerced.edu http://faculty.ucmerced.edu/wshadish/index.htm http://psychology.ucmerced.edu -- View this message in context: http://r.789695.n4.nabble.com/edf-s-higher-than-1-tp4636798.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]

...d Merced CA 95343 Physical/Delivery Address: University of California Merced ATTN: William Shadish School of Social Sciences, Humanities and Arts Facilities Services Building A 5200 North Lake Rd. Merced, CA 95343 209-228-4372 voice 209-228-4007 fax (communal fax: be sure to include cover sheet) wshadish at ucmerced.edu http://faculty.ucmerced.edu/wshadish/index.htm http://psychology.ucmerced.edu

Dear R users, I have seven regression lines I´d like to compare, in order to find out if these are significatively different. The main problem is that these are curves, non normal, non homogeneous data, I´ve tried to linearize them but it has not worked. So I´d like to know if you know any command or source in R which explains how to perform this kind of comparison. Thanks in advance for your

...d Merced CA 95343 Physical/Delivery Address: University of California Merced ATTN: William Shadish School of Social Sciences, Humanities and Arts Facilities Services Building A 5200 North Lake Rd. Merced, CA 95343 209-228-4372 voice 209-228-4007 fax (communal fax: be sure to include cover sheet) wshadish@ucmerced.edu http://faculty.ucmerced.edu/wshadish/index.htm http://psychology.ucmerced.edu [[alternative HTML version deleted]]

We are using GAM in mgcv (Wood), relatively new users, and wonder if anyone can advise us on a problem we are encountering as we analyze many short time series datasets. For each dataset, we have four models, each with intercept, predictor x (trend), z (treatment), and int (interaction between x and z). Our models are Model 1: gama1.1 <- gam(y~x+z+int, family=quasipoisson) ##no smooths Model