On Tue, 18 Nov 2003, Martin Wegmann wrote: > Ok I try to explain it clearer. > > I am not looking for step() add1() drop1() or similar commands. Nothing to do > with variable selection. > > I have two data frames, on with environmental variables and another one with > animal data (let's say absence/presence of 10 species) > > first I look which env. variables explain the presence of species 1 > > glm(species1~env.var1+env.var2+.....) -> glm.spec1 > > step(glm.spec1) -> glm.spec1.step > > I get certain env. variables which have the biggest explanatory power. > > Now I would like to treat the other absence/presence data of my species like > env. variables which could influence the presence of species1 > I included the env.variable from glm.spec1.step (I call them env.varX+...) > > glm(species1~env.varX+......+species2) -> glm.species1.sp2 > > > glm(species1~env.varX+......+species3) -> glm.species1.sp3 > > > and this procedure shall be done for all remaining species. > > I am looking for a method to add automatically each species2 up to species10 > and run glm().
That is what add1 does. > The first part with the env. variables shall be kept as it is but the last > variable (speciesX) shall be changed each time. I am looking for something > like a placeholder and the command greps a different species from the species > dataframe for each run and add it instead of the place holder. > > I hope I explained it better. thanks Martin > > On Tuesday 18 November 2003 22:14, Prof Brian Ripley wrote: > > Are you looking for something like add1 then? > > > > We do need a much clearer explanation of what you are trying to do to be > > able to help you: and not with y used in two separate senses! > > > > On Tue, 18 Nov 2003, Martin Wegmann wrote: > > > On Tuesday 18 November 2003 19:32, Prof Brian Ripley wrote: > > > > On Tue, 18 Nov 2003, Martin Wegmann wrote: > > > > > I have count data of animals (here y, y1, y2...) and env. variables > > > > > (x, x1, x2 ,....). > > > > > > > > > > I used a glm > > > > > > > > > > glm(y~x1+x2+x3....) > > > > > > > > > > glm(y1~x1+x2+x3....) > > > > > > > > > > and now I would like to add the count data of other species to > > > > > investigate if they might have a bigger impact than the env. > > > > > variables: > > > > > > > > > > #x? are the selected var from the first glm run > > > > > > > > > > glm(y~x?+x?+y1) > > > > > > > > > > glm(y~x?+x?+x?+y2) > > > > > > > > > > .... > > > > > > > > > > I wonder if there is a more elegant method to do this than adding > > > > > (and removing) each y by hand. > > > > > > > > Do you mean each x? In either case, see ?update. > > > > > > update looks good but with update and with adding the y I have to do it > > > manually. > > > > > > I thought something like doing > > > > > > glm(y~x+x1+x2+....+y§) > > > > > > where y§ is: grep y1 out of df.y run glm and name it > > > grep y2 out of df.y run glm ..... > > > > > > until all y's of df.y has been onced included in the model. > > > every time only one y§ has to be included > > > > > > the included x's have to be kept. I only want to look if one species > > > variables has more explanation power than the env. variables. > > > > > > perhaps this helps to understand what I am looking for: > > > I think bash scripts are not possible in R but it would look like such a > > > bash script for GRASS: > > > > > > for variable in y1 y2 y3 ....; do > > > > > > glm(y~x+x1+x2....+$variable)->glm.$variable > > > ; done > > > > > > #where $variable refers to the name of read in y's. > > > > > > > > > Martin > > > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help