How To Calculate Standard Error In Logistic Regression
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Interpreting Standard Error In Logistic Regression
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Covariance Matrix Logistic Regression
visualization. Join them; it only takes a 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 How are the standard errors computed for the fitted values from a logistic regression? up vote 17 down vote favorite 16 When you predict a confidence interval logistic regression fitted value from a logistic regression model, how are standard errors computed? I mean for the fitted values, not for the coefficients (which involves Fishers information matrix). I only found out how to get the numbers with R (e.g., here on r-help, or here on Stack Overflow), but I cannot find the formula. pred <- predict(y.glm, newdata= something, se.fit=TRUE) If you could provide online source (preferably on a university website), that would be fantastic. r regression logistic mathematical-statistics references share|improve this question edited Aug 9 '13 at 15:14 gung 74.2k19160309 asked Aug 9 '13 at 14:41 user2457873 8814 add a comment| 1 Answer 1 active oldest votes up vote 19 down vote accepted The prediction is just a linear combination of the estimated coefficients. The coefficients are asymptotically normal so a linear combination of those coefficients will be asymptotically normal as well. So if we can obtain the covariance matrix for the parameter estimates we can obtain the standard error for a linear combination of those estimates easily. If I denote the covariance matr
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Wald Test Logistic Regression
the company Business Learn more about hiring developers or posting ads with us Cross logistic regression equation Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, python logistic regression machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and http://stats.stackexchange.com/questions/66946/how-are-the-standard-errors-computed-for-the-fitted-values-from-a-logistic-regre rise to the top Understanding standard errors in logistic regression up vote 2 down vote favorite I am having trouble understanding the meaning of the standard errors in my thesis analysis and whether they indicate that my data (and the estimates) are not good enough. I am performing an analysis with Stata, on immigrant-native gap in school performance (dependent variable = good / bad results) controlling for a variety of regressors. I used http://stats.stackexchange.com/questions/89810/understanding-standard-errors-in-logistic-regression both logit and OLS and I adjusted for cluster at the school level. The regressors which are giving me trouble are some interaction terms between a dummy for country of origin and a dummy for having foreign friends (I included both base-variables in the model as well). In the logit estimation, more than one of the country*friend variables have a SE greater than 1 (up to 1.80 or so), and some of them are significant as well. This does not happen with the OLS. I am really confused on how to interpret this. I have always understood that high standard errors are not really a good sign, because it means that your data are too spread out. But still (some of) the coefficients are significant, which works perfect for me because it is the result I was looking for. Can I just ignore the SE? Or does it raise a red flag regarding my results? I usually just ignore the SE in regressions (I know, it is not really what one should do) but I can't recall any other example with such huge SE values. self-study logistic stata standard-error share|improve this question edited Mar 14 '14 at 5:37 Dimitriy V. Masterov 15.4k12461 asked Mar 12 '14 at 21:50 Maria 1112 1 How is it that you
standard errors into odds ratios is trivial in Stata: just add , or to the end of a logit command: . use "http://www.ats.ucla.edu/stat/data/hsbdemo", clear https://www.andrewheiss.com/blog/2016/04/25/convert-logistic-regression-standard-errors-to-odds-ratios-with-r/ . logit honors i.female math read, or Logistic regression Number of obs = 200 LR chi2(3) = 80.87 Prob > chi2 = 0.0000 Log likelihood = -75.209827 Pseudo R2 = 0.3496 ------------------------------------------------------------------------------ honors | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | female | 3.173393 1.377573 2.66 0.008 1.35524 7.430728 math | 1.140779 logistic regression .0370323 4.06 0.000 1.070458 1.21572 read | 1.078145 .029733 2.73 0.006 1.021417 1.138025 _cons | 1.99e-06 3.68e-06 -7.09 0.000 5.29e-08 .0000749 ------------------------------------------------------------------------------ Doing the same thing in R is a little trickier. Calculating odds ratios for coefficients is trivial, and exp(coef(model)) gives the same results as Stata: # Load libraries library(dplyr) # Data frame manipulation library(readr) # Read standard error in CSVs nicely library(broom) # Convert models to data frames # Use treatment contrasts instead of polynomial contrasts for ordered factors options(contrasts=rep("contr.treatment", 2)) # Load and clean data df <- read_csv("http://www.ats.ucla.edu/stat/data/hsbdemo.csv") %>% mutate(honors = factor(honors, levels=c("not enrolled", "enrolled")), female = factor(female, levels=c("male", "female"), ordered=TRUE)) # Run model model <- glm(honors ~ female + math + read, data=df, family=binomial(link="logit")) summary(model) #> #> Call: #> glm(formula = honors ~ female + math + read, family = binomial(link = "logit"), #> data = df) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -2.0055 -0.6061 -0.2730 0.4844 2.3953 #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -13.12749 1.85080 -7.093 1.31e-12 *** #> femalefemale 1.15480 0.43409 2.660 0.00781 ** #> math 0.13171 0.03246 4.058 4.96e-05 *** #> read 0.07524 0.02758 2.728 0.00636 ** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 231.29 on 199 degre
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