Thanks for looking at this, I've been tearing my hair out for a day or so now.
I have done a multiple variable logistic regression in R, and obtained my coefficients. I am able to make predictions for the training data in R without problem. But now I would like to create a prediction model in Ruby (that was the original point of doing the regression) and I'm having some trouble. Basically, my equation is: predicted_logit = K + v1*c1 + v2*c2 + ... vn*cn odds_ratio = e^predicted_logit/(1+e^predicted_logit) But it always seems to either give 1.0 or 0.0! The output of predict() in R is generally something nice and soft like 0.5578460! I realize not everyone knows Ruby, but I'll include my code here for reference: # These are the coefficients that R gives me from my logistic regression: intercept = 0.2700309 coefficients = { high: 1.0136028, low: 1.0016712, germ_mean: 1.0233327, gdds: 0.9990283, early_gdds: 0.9986464, mid_gdds: 1.0002979, late_gdds: 0 } # And this is what R predicts for one datum: # # outcome high low germ_mean gdds early_gdds mid_gdds late_gdds p_success # 1 1 73 28 40 119 0 91 28 0.5578460 # ... # So to get my own p_success, first I multiply each coefficient by it's input data period = {:high=>73, :low=>28, :germ_mean=>40, :gdds=>119, :early_gdds=>0, :mid_gdds=>91, :late_gdds=>28} products = coefficients.map {|name,value| period[name]*value } # Then I add those together and add that to the intercept predicted_logit = intercept + products.sum # Then my probability should be e^predicted_logit over 1 + e^predicted_logit: odds_ratio = Math.exp(predicted_logit) / (1 + Math.exp(predicted_logit)) # But the odds ratio comes out as 1.0, not 0.5578460 like R predicts. |
On Jan 16, 2014, at 6:53 PM, erikpukinskis wrote: > Thanks for looking at this, I've been tearing my hair out for a day or so > now. > > I have done a multiple variable logistic regression in R, and obtained my > coefficients. I am able to make predictions for the training data in R > without problem. But now I would like to create a prediction model in Ruby > (that was the original point of doing the regression) and I'm having some > trouble. > > Basically, my equation is: > > predicted_logit = K + v1*c1 + v2*c2 + ... vn*cn > odds_ratio = e^predicted_logit/(1+e^predicted_logit) > > But it always seems to either give 1.0 or 0.0! The output of predict() in R > is generally something nice and soft like 0.5578460! > > I realize not everyone knows Ruby, but I'll include my code here for > reference: > > # These are the coefficients that R gives me from my logistic regression: > intercept = 0.2700309 > > coefficients = { > high: 1.0136028, > low: 1.0016712, > germ_mean: 1.0233327, > gdds: 0.9990283, > early_gdds: 0.9986464, > mid_gdds: 1.0002979, > late_gdds: 0 > } If you are going to write ruby code, then why post to an R mailing list? -- David. > > # And this is what R predicts for one datum: > # > # outcome high low germ_mean gdds early_gdds mid_gdds late_gdds p_success > # 1 1 73 28 40 119 0 91 28 0.5578460 > # ... > > # So to get my own p_success, first I multiply each coefficient by it's > input data > period = {:high=>73, :low=>28, :germ_mean=>40, :gdds=>119, :early_gdds=>0, > :mid_gdds=>91, :late_gdds=>28} > products = coefficients.map {|name,value| period[name]*value } > > # Then I add those together and add that to the intercept > predicted_logit = intercept + products.sum > > # Then my probability should be e^predicted_logit over 1 + > e^predicted_logit: > odds_ratio = Math.exp(predicted_logit) / (1 + Math.exp(predicted_logit)) > > # But the odds ratio comes out as 1.0, not 0.5578460 like R predicts. > > > > -- > View this message in context: http://r.789695.n4.nabble.com/Predicting-probabilities-from-a-logistic-regression-by-hand-in-code-tp4683713.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > [hidden email] 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. David Winsemius Alameda, CA, USA ______________________________________________ [hidden email] 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. |
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