Logistic Regression Standard Error Coefficients
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Logistic Regression Standard Error Of Prediction
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Interpreting Standard Error In Logistic Regression
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 rise to the top How are covariance matrix logistic regression the standard errors computed for the fitted values from a logistic regression? up vote 17 down vote favorite 16 When you predict a 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. confidence interval logistic regression 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 matrix as $\Sigma$ and and write the coefficients for my linear combination in a vector as $C$ then the standard error is just $\sqrt{C' \Sigma C}$ # Making fake data and fitting the model and getting a prediction set.seed(500) dat <- data.frame(x = runif(20), y = rbinom(20, 1, .5)) o <- glm(y ~ x, data = dat) pred <- predict(o, newdata = data.frame(x=1.5), se.fit = TRUE) # To obtain a prediction for x=1.5 I'm really # asking for yhat = b0 + 1.5*b1 so my # C = c(1, 1.5) # and vcov applied to the glm object gives me # the covariance matrix for the es
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Wald Test Logistic Regression
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Logistic Regression Coefficient
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 Understanding standard errors in logistic regression up vote 2 down vote favorite I am having trouble http://stats.stackexchange.com/questions/66946/how-are-the-standard-errors-computed-for-the-fitted-values-from-a-logistic-regre 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 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 http://stats.stackexchange.com/questions/89810/understanding-standard-errors-in-logistic-regression 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 ran this model as both OLS and as a logistic regression? That doesn't make sense. Also, you state that you are adjusting for clustering in the data; that implies that this is a mixed-effects model, in which case it should be GLiMM or LMM, but you don't say anything about that. Can you clarify what the nature of your
Descriptive Statistics Hypothesis Testing General Properties of Distributions Distributions Normal Distribution Sampling Distributions Binomial and Related Distributions http://www.real-statistics.com/logistic-regression/significance-testing-logistic-regression-coefficients/ Student's t Distribution Chi-square and F Distributions Other Key Distributions Testing for Normality and Symmetry ANOVA One-way ANOVA Factorial ANOVA ANOVA with Random or Nested Factors Design of Experiments ANOVA with Repeated Measures Analysis of Covariance (ANCOVA) Miscellaneous Correlation Reliability Non-parametric Tests Time Series Analysis Survival Analysis Handling logistic regression Missing Data Regression Linear Regression Multiple Regression Logistic Regression Multinomial and Ordinal Logistic Regression Log-linear Regression Multivariate Descriptive Multivariate Statistics Multivariate Normal Distribution Hotelling’s T-square MANOVA Repeated Measures Tests Box’s Test Factor Analysis Cluster Analysis Appendix Mathematical Notation Excel Capabilities Matrices and Iterative Procedures Linear Algebra and Advanced Matrix logistic regression standard Topics Other Mathematical Topics Statistics Tables Bibliography Author Citation Blogs Tools Real Statistics Functions Multivariate Functions Time Series Analysis Functions Missing Data Functions Data Analysis Tools Contact Us Significance Testing of the Logistic Regression Coefficients Definition 1: For any coefficient b the Wald statistic is given by the formula Observation: For ordinary regression we can calculate a statistic t ~ T(dfRes) which can be used to test the hypothesis that a coordinate b = 0. The Wald statistic is approximately normal and so it can be used to test whether the coefficient b = 0 in logistic regression. Since the Wald statistic is approximately normal, by Theorem 1 of Chi-Square Distribution, Wald2 is approximately chi-square, and, in fact, Wald2 ~ χ2(df) where df = k – k0 and k = the number of parameters (i.e. the number of coefficients) in the model (the full model) and k0 = the number of parameters in a reduced model (esp. the baseline model which doesn’t use an
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