Dear Statisticians,
I would like to analyse my data with a GLM with binomial error distribution and logit link function. The point is that I want a model fitted without intercept, i.e. the fitted curve should start at y=0.5 for x=0. I tried it with the following code: glm(value~0+ppm, binomial) Does this code yield the correct model or is there another possibility? Id appreciate it very much if you could help me out with this. I attached some example data. Thanks & all the best Robert -------------------- Robert Junker Department of Animal Ecology & Tropical Biology University of Würzburg Biozentrum, Am Hubland 97074 Würzburg, Germany
ppm value 65.85986417 1 65.85986417 1 65.85986417 0 65.85986417 0 65.85986417 1 65.85986417 0 65.85986417 0 65.85986417 1 65.85986417 1 65.85986417 1 659.4188035 1 659.4188035 0 659.4188035 0 659.4188035 1 659.4188035 0 659.4188035 0 659.4188035 0 659.4188035 0 659.4188035 1 659.4188035 0 659.4188035 0 659.4188035 0 659.4188035 1 659.4188035 0 659.4188035 1 659.4188035 1 659.4188035 1 659.4188035 0 659.4188035 1 659.4188035 1 1245.665143 0 1245.665143 1 1245.665143 1 1245.665143 1 1245.665143 0 1245.665143 1 1245.665143 1 1245.665143 1 1245.665143 0 1245.665143 1 1245.665143 0 1245.665143 1 1245.665143 1 1245.665143 0 1245.665143 1 1245.665143 1 1245.665143 0 1245.665143 1 1245.665143 0 1245.665143 0 5823.423892 0 5823.423892 1 5823.423892 0 5823.423892 0 5823.423892 1 5823.423892 0 5823.423892 0 5823.423892 0 5823.423892 0 5823.423892 1
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