Logistic Regression Error
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Logistic Regression Error Distribution
minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Logistic Regression - Error Term and its Distribution up vote 12 down vote favorite 6 On whether an error term exists in logistic regression (and its assumed distribution), I have read in various places that: no logistic regression example error term exists the error term has a binomial distribution (in accordance with the distribution of the response variable) the error term has a logistic distribution Can someone please clarify? logistic binomial bernoulli-distribution share|improve this question edited Nov 20 '14 at 12:43 Frank Harrell 39.1k173156 asked Nov 20 '14 at 10:57 user61124 6314 4 With logistic regression - or indeed GLMs more generally - it's typically not useful to think in terms of the observation $y_i|\mathbf{x}$ as "mean + error". Better to think in terms of the conditional distribution. I wouldn't go so far as to say 'no error term exists' as 'it's just not helpful to think in those terms'. So I wouldn't so much say it's a choice between 1. or 2. as I would say it's generally better to say "none of the above". However, irrespective of the degree to which one might argue for "1." or "2.", though, "3." is definitely wrong. Where did you see that? –Glen_b♦ Nov 20 '14 at 13:52 @Glen_b: Might one argue for (2)? I've known people to say it but
(academic discipline) Machine Learning Existence QuestionIs there an error term in logistic regression?If so, does it have a particular distribution, like
Binary Logistic Regression Spss
the normal error in linear regression?UpdateCancelAnswer Wiki2 Answers Michael Hochster, logistic regression pdf PhD in Statistics, Stanford; Director of Research, PandoraWritten 75w ago · Upvoted by Peter Flom, Independent simple logistic regression example statistical consultant for researchers in behavioral, social and medical sciencesYou can think of the logistic regression model as arising from a linear model plus a logistic http://stats.stackexchange.com/questions/124818/logistic-regression-error-term-and-its-distribution error term, but all you observe is a 1 if the linear part plus error is positive and 0 if it is negative. This is called the latent variable formulation, and you can learn more details about it here:Logistic regressionYou can get other kinds of model (e.g. probit) by assuming a different distribution https://www.quora.com/Is-there-an-error-term-in-logistic-regression for the error term.3.9k Views · View UpvotesRelated QuestionsMore Answers BelowHow can the errors of logistic regression be modelled using likelihood principal?What does the bias term represent in logistic regression?Machine Learning: In layman's terms, what is the relationship between Grid Search and Logistic Regression?Are there researchers actively working on logistic regression?Why is logistic regression considered a linear model? Jay Verkuilen, PhD Psychometrics, MS Mathematical Statistics, UIUCWritten 75w ago · Upvoted by Justin Rising, MSE in CS, PhD in Statistics and Peter Flom, Independent statistical consultant for researchers in behavioral, social and medical sciencesYes but it's implicit. By assuming that the binary variable is Bernoulli conditionally on the regressors, we have chosen it as the error distribution. The regression is not linear though so it's not expressible as an additive error term.1.9k Views · View Upvotes · Answer requested by 1 personView More AnswersRelated QuestionsWhat is the difference between linear classification and logistic regression?What is logistic regression
Mon 14 Apr 2014 – Mon 5 May 2014 (2 years ago) Dashboard ▼ Home Data Make a submission Information Description Evaluation Rules Timeline Forum Leaderboard Public Private Competition Forum All https://www.kaggle.com/c/the-analytics-edge-mit-15-071x/forums/t/7865/error-in-prediction-for-logistic-regression-model Forums » The Analytics Edge (15.071x) Error in prediction for Logistic Regression model Start Watching « Prev Topic » Next Topic 0 votes When I built a simple logistic regression model on the training set with all the variables(except userID), and tried to make prediction on the training set itself using the model, I got an error message, as follows: > Log1 = glm(Happy~.-UserID, data=train, family="binomial")> predLR logistic regression = predict(Log1, type="response")> table(train$Happy, predLR>0.5) Error in table(train$Happy, predLR > 0.5) : all arguments must have the same lengths I do not understand why it said that the two arguments does not have the same length, since I am using the training set. Do you guy encounter similar problems? Is it due to missing data on the training set, which means I need to do imputation logistic regression error on all the variables before building the model? However, I used the mice package to do imputation on the questions variables and R gave me an error message as 'no missing variables found'. I am confused. Any help will be very much appreciated. Thank you very much in advance for your time and help. #1 | Posted 2 years ago Permalink Cary C Posts 2 Joined 17 Apr '14 | Email User 0 votes Try taking the error message literally and see what this tells you: length(train$Happy) length(predLR > 0.5) #2 | Posted 2 years ago Permalink Rich Seiter Competition 69th Posts 38 | Votes 52 Joined 20 Nov '11 | Email User 0 votes train = read.csv("train.csv") #Fill in the Missing valuesfor (i in names(train)) { levels(train[,i]) <- c(levels(train[,i]), "Skipped") train[,i][train[,i] == ''] <- 'Skipped'} #build the model model1 = glm(Happy ~ .-UserID, data=train, family=binomial) The above statement fails with"Error in weights * y : non-numeric argument to binary operator" What am I doing wrong here? #3 | Posted 2 years ago Permalink Logan P Posts 5 | Votes 1 Joined 22 Apr '12 | Email User 1 vote I believe that the specific error you are
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