Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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Simulating data for logistic regression with a categorical variable
I was trying to create some test data for logistic regression and I found this post How to simulate artificial data for logistic regression?
It is a nice answer but it creates only continuous ...
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Using Duan Smear factor on a two-part model
I'm running a two-part model on a health insurance claims dataset where I predict the probability of nonzero health care costs using a logistic regression (1st part), then predict the magnitude of the ...
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Pooling data with unequal sample sizes
I have searched far and wide for a clear answer to my question (and am aware that a clear answer may not exist) and I'm hoping someone might be able to help me:
I have survey data for 4 samples of ...
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Assumptions of generalized linear models
On page 232 of "An R companion to applied regression" Fox and Weisberg note
Only the Gaussian family has constant variance, and in all other GLMs the conditional variance of y at $\bf{x}$ depends ...
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Logistic Regression - Bayesian Approach - Assessing Classification Accuracy
I have recently begun to read about bayesian statistics and I am playing around with the R2WinBUGS package. I'm trying to fit a logistic regression to the spam data (that can be found on the webpage ...
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How do I compute a cutoff based on sensitivity/specificity when the characteristics of my sample is different from the population?
I have a dataset containing the performance of a novel instrument to screen for disease A. The novel instrument uses a scoring system to score the subject to determine if they have disease A. I then ...
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How to compare whether the odds of success of different levels of a predictor are different from 0
I hope I am able to word my question clearly. Suppose I have a model below:
glmer(Y~X + (1|subject), family="binomial", data=dat)
The intercept is the log odds ...
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What to do when the predictors do not accord with common sense/literature, but the model is fine/best according to log likelihood and LRT?
I would try to clarify the problem and then ask the questions.
The problem (variable names are masked due to confidentiality):
I ran a binary logistic regression, in which there were 5 independent ...
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Comparing logistic regression models with the same IVs
I have multiple logistic regression models with all of the same IVs/controls and a variety of DVs (all health outcomes from the same sample). The primary IV is the sum of types of childhood abuse (...
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Treating multiple Dichotomies combined?
Let's say I am interested in choosing a new country $c_1, \ldots, c_k$ to live in. For some reason I can only apply to one country and only once.
I know for each country a set of 2000 observations (...
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Adjusting for confounding variables in binary response variables
I have a dataset of patient information and I'm looking to find a way to compare two groups of patients and take into account confounding variables. My dataset has an N of ~1500 and I'm looking for a ...
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Scorecard logistic regression -- include or omit credit grade?
I am using logistic regression to create a credit scorecard from past loan data. We will not approve loans in the future if the applicant has an insufficient credit score (no credit or insufficient ...
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How to show what is going on if we drop significiant variable in logit model?
How to show what is going on if we drop significant variable in logit model ? Bias and heteroskedasticity should emerge. But what is the framework for showing such behaviours in econometric models ?
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Centering to use a sparse covariate?
In my dataset, there's a binary response, some factors, and some covariates. In particular, there are some covariates that are always present when factor1=="A" and ...
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Logistic regression, loss function and KL divergence [closed]
In decision theory, a loss function signature is supposed to be
output space * output space -> error
There seems to be many different definition of 'the ...
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Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why?
I am teaching a class on logistic regression with SPSS. The textbook supplies a sample data set with a binary predictor and two numeric covariates. The sample contains 1000 rows and a number of these ...
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Understanding the predictions from logistic regression
My predictions coming from a logistic regression model (glm in R) are not bounded between 0 and 1 like I would expected. My understanding of logistic regression is that your input and model parameters ...
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Multinomial log. regression in SPSS with a dummy independent variable
This is my test design which is simply a grouping variable:
When I choose to regress a gender variable (w/m) onto this grouping variable with a multinomial logisitic regression model I'll observe
...
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Pearson correlation vs. logistic regression coefficients
From a bunch of data points which look like:
...
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Ordinal variable with 0.3% of observations in one category - delete, ignore?
The response variable in my ordered logit model has 5 categories ranging from '1 = strongly disagree' to '5 = strongly agree'. However, only 0.3% of observations fall into category 1 (4 observations ...
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Finding choice probabilities by using utility with logit and probit models
I am using a formula to calculate the utility, which is as follows:
v_{ij} = 1 - x*beta + delta_i + e_{ij}
delta_i ~ N(0,phi^2)
e_ij ~ N(0,sigma^2)
v_{ij} is ...
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Intepretation of crossvalidation result - cv.glm()
My logistic model has been suspicious due to enormous coefficients, so I tried to do a crossvalidation, and also do a crossvalidation of simplified model, to confirm the fact that the original model ...
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Observed vs predicted values from a logit model
I have a logit model and am trying to understand and compare the predicted and observed values generated by the model. Let's say data set had 100 values and I generate all the predicted probabilities, ...
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Enormous coefficients in logistic regression - what does it mean and what to do?
I get enormous coefficients during logistic regression, see coefficients with krajULKV:
...
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Logistic regression and odds ratio
If the odds ratio is greater than one with an insignificant p value for a variable in logistic regression should the variable be kept in the model?
Can I select the variable with odds close to 1?
...
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Causality test for logistic regression [closed]
For time series there is the Granger causality test. Is there some causality test for the logistic regression?
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What is the difference between logit-transformed linear regression, logistic regression, and a logistic mixed model?
Suppose I have 10 students, who each attempt to solve 20 math problems. The problems are scored correct or incorrect (in longdata) and each student's performance can be summarized by an accuracy ...
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Variance of log-odds probabilities
I am wondering how I could express the variance of log-odds into understandable terms.
For example the variance in the log-odds of crime being reported to the police between neighbourhoods is 0.07 (0....
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How do factor categories with no variance influence logistic regression?
I'm modeling the effect of a categorical predictor on a binary dependent variable using logistic regression. I'm comparing models with/without the predictor using a likelihood-ratio test.
Two ...
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Power calculations, logistic regression with continuous exposure--cohort [duplicate]
I'm trying to estimate power in a logistic regression with a continuous exposure in a cohort study (ie, the ratio of the sampling probabilities is 1). I have population cumulative incidence (...
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Is standardization needed before fitting logistic regression?
My question is do we need to standardize the data set to make sure all variables have the same scale, between [0,1], before fitting logistic regression. The formula is:
$$\frac{x_i-\min(x_i)}{\max(...
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Delta method in a multinomial logistic regression
I am estimating a multinomial logistic model with R (package "mlogit"). I use the estimated coefficients to get the estimated odds and then I apply the Delta Method (package "deltamethod") to get ...
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Variable selection with restricted cubic splines [closed]
Is there any function in R for doing variable selection (backward elimination) in a multiple logistic regression using restricted cubic splines like mvrs procedure for STATA?
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Scale parameter
I came across the scale parameter used in the logit and probit models. Does any one know what that is and what it is used for? What would go wrong if I did not use it?
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Variable selection and logistic regression
I am using Matlab, I have a $600 \times 9$ matrix with each row representing the 9 features which I am trying to evaluate using logistic regression.
I understand that I need to perform feature ...
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Likelihood-ratio test or z-test?
Consider the following two logistic regression models:
$$
\begin{aligned}
&\text{Model A: }&P(Y=1)&=\frac{\text{exp}\left(b_1+b_2X_2\right)}{1+\text{exp}\left(b_1+b_2X_2\right)} \\
&\...
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Exploratory data analysis for discrete data
I am using a probit and a logit model for obtaining the choice probabilities of some data. What kind of plots can be useful to conduct a exploratory data analysis for these data?
Here is the ...
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Extremely unequal sample size in logistic regression [duplicate]
Possible Duplicate:
Does an unbalanced sample matter when doing logistic regression?
I’m hoping for help on dealing with unequal sample sizes for logistic regression analysis. Any assistance is ...
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Is the logit function always the best for regression modeling of binary data?
I've been thinking about this problem. The usual logistic function for modeling binary data is:
$$
\log\left(\frac{p}{1-p}\right)=\beta_0+\beta_1X_1+\beta_2X_2+\ldots
$$
However is the logit function,...
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How can I implement logistic regression in live decision system?
I got the equation for logistic reg, and I am comfortable with the result. Let's say logit(p), ln(p/q), or the model is something like
$$\text{logit}(p) = b+a_1X_1 + a_2X_2 + a_3X_3$$
For example --> ...
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Indicator variables and backward elimination with GLM
I am running logistic regression using glm in R on data, that has some indicator variables to it. Two of those have multiple levels and have been rewritten as (#...
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In logistic regression, does the lack of significance of the parameter estimates in a test sample indicate overfitting?
I am trying to build a logistic regression model where I have a dependent variable $y$ and independent variables $x_1$, $x_2$... $x_n$. $y$ can take only two values - 0 or 1.
My original modelling ...
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Confusion related to multicollinearity, FA and regression of heterogeneous data
I am currently working with a data set that contains about 26 IVs of almost all sorts of scale of measurement (binary, nominal, ordinal and interval scale variables). There are strong reasons to ...
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Explain ridge in the log-likelihood for Logistic Regression classifier
What does the ridge parameter change in a Logistic Regression classifier as for example implemented in Weka Logistic classifier "Parameter -R ridge". The paper describing the underlying theory: Ridge ...
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Logit versus Probit [duplicate]
Possible Duplicate:
Difference between logit and probit models
I have data in which the response variable is binary. So, I fitted logit and probit models and obtained the results. How can I ...
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How to test proportions of a Categorical response variable for a Repeated measures design with Unequal Sample Size?
I have a question about the analysis of a unequal sample size repeated measures data with categorical response variable.
This experiment looks at 5 young and aged persons and for each of the ...
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residualize binary outcome variable
Does it make sense and what is the correct approach to residualize a binary variable? For a continuous variable y, I simply run a regression that predicts ...
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Logistic regression: how to choose negative examples for training set
I want to predict the probability of rain based on the measured weather parameters like temperature, humidity, etc. Let's not get into why I want to do that despite the fact that weather websites ...
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Do we need to apply the same transformation of predictors on a test dataset?
I first divide the dataset into training (75%) and test (25%). Then fit a logistic regression model on training data set. When fitting the model, I did some modification on independent variable, such ...
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