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Posted: Mon 18:46, 02 Sep 2013 Post subject: Extracting meaningful information from a logistic |
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Extracting meaningful information from a logistic regression model,roger vivier australia store
Let's say that I model whether I wear red shoes depending on the weather. Red shoes,parajumpers parka Outlet Online, which is my dependent variable,Peuterey Christina for sale, is a dichotomous variable as I either wear them or don't. Weather is a variable with five 'levels' and I'm trying to find the probability that I will wear read shoes.
Let's say I model out this relationship with a logistic regression model in R:
Now,http://kevindurantshoes5forsale.blogspot.com/, what I am interested in is finding what are the probabilities for wearing red shoes for each of the five 'levels' in weather. So perhaps I might have a 20% probability of wearing red shoes when cold,peuterey Jacket for sale, 30% when mild,http://kevindurant4shoesforsale.blogspot.com/, 40% when warm,duvetica vestes paris, and so forth.
I'm wondering if modeling is a requirement for finding this information,http://parajumpersparkamen.webmium.com/?
If so,Woolrich Arctic Parka, how does one go from somewhat meaningful regression coefficients to meaningful probabilities in R?
All you need to do is supply a new data frame to R with the values that you want to use for the independent variables as above,Cheap Roger Vivier Shoes Sale Outlet Store. The type "response" gives you probabilities rather than logits,Ricarda Christina and Peuterey Jacket With Front Pockets for sale.
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