Logistic Regression Formula Standard Error
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Covariance Matrix Logistic Regression
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 fitted value from a logistic regression model, how are standard errors computed? I mean for the fitted values, not for the coefficients (which confidence interval logistic regression 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 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(
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Wald Test Logistic Regression
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Log Likelihood Logistic Regression
Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and http://stats.stackexchange.com/questions/66946/how-are-the-standard-errors-computed-for-the-fitted-values-from-a-logistic-regre 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 Understanding standard errors in logistic regression up vote 2 down vote favorite I am having trouble understanding the meaning of the standard errors in http://stats.stackexchange.com/questions/89810/understanding-standard-errors-in-logistic-regression 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 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 wor
page shows an example of logistic regression with footnotes explaining the output. These data were collected on 200 high schools students and are scores on various tests, including science, math, reading and social http://www.ats.ucla.edu/stat/spss/output/logistic.htm studies (socst). The variable female is a dichotomous variable coded 1 if the student was female and 0 if male. In the syntax below, the get file command is used to load the data into SPSS. In quotes, you need to specify where the data file is located on your computer. Remember that you need to use the .sav extension and that you need to end the command with a logistic regression period. By default, SPSS does a listwise deletion of missing values. This means that only cases with non-missing values for the dependent as well as all independent variables will be used in the analysis. Because we do not have a suitable dichotomous variable to use as our dependent variable, we will create one (which we will call honcomp, for honors composition) based on the continuous variable write. We do not logistic regression formula advocate making dichotomous variables out of continuous variables; rather, we do this here only for purposes of this illustration. Use the keyword with after the dependent variable to indicate all of the variables (both continuous and categorical) that you want included in the model. If you have a categorical variable with more than two levels, for example, a three-level ses variable (low, medium and high), you can use the categorical subcommand to tell SPSS to create the dummy variables necessary to include the variable in the logistic regression, as shown below. You can use the keyword by to create interaction terms. For example, the command logistic regression honcomp with read female read by female. will create a model with the main effects of read and female, as well as the interaction of read by female. We will start by showing the SPSS commands to open the data file, creating the dichotomous dependent variable, and then running the logistic regression. We will show the entire output, and then break up the output with explanation. get file "c:\hsb2.sav". compute honcomp = (write ge 60). exe. logistic regression honcomp with read science ses /categorical ses. Logistic Regression Block 0: Beginning Block Block 1: Method = Enter This part of the
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