Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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probability of interest [closed]
The model that I created in R is:
fit <- lm(hired ~ educ + exper + sex, data=data)
what I am unsure of is how to fit to model to predict probability of interest where p = pr(hiring = 1).
Edit:...
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What type of regression model do I use?
$y = \mathbf{X} \beta$ + $\epsilon$ + $m$
Where y, $\epsilon$, and $m$ are $n \times 1$ column vectors, $\beta$ is $p \times 1$ and $\mathbf{X}$ is $n \times p$.
$y$ is a noisy time-series signal ...
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Interactions make terms significant in regression when they should not be
I am writing code to prepare for running a logistic regression on real data. I have sample data for all my IVs but not for the outcome variable. There are many strong dependencies among the IVs but I ...
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Algorithm convergence with logistic classifier
I am doing a college classification project, in which I am required to classify some handwritten digits. Assume that my input is a N*D where D is the number of features in each input sample and I need ...
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Confidence interval for proportions
I have some data like this:
id pop var
1 593 51
2 592 31
3 346 20
4 1214 70
5 1063 66
6 1370 71
each ...
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Logistic Regression model keeps kicking out 1 dummy
I have a dataset in which each row belongs to one of 8 categories. I'm running a logistic regrssion on it using R. I created dummy variables for each of these categories. In my logistic regression ...
0
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2
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logistic regression always yielding increasing f'n when should sometimes be decreasing (using R)
I'm modeling a set of outcome data the depends on two parameters:
time, T
-100 < A < 100
I've done logistic regression using R with the command:
...
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4
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Sensible to include ratio as a variable in logistic regression?
I'm creating a generalised linear regression using a binomial link function for
two variables A and B. From looking at the data it appears that A/B may have
discriminatory effect. Is it sensible to ...
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2
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Logistic regression: big difference in predicted values and highly significant, but poor goodness of fit
I have a logistic regression model with a dichotomous response variable and predictors coded from $1$ to $10$ and from $0$ to $18$.
When I fit the model, I get these results:
...
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poisson vs logistic regression
I have a cohort of patients with different length of follow-up. So far I´m disregarding the time aspect and just need to model a binary outcome-disease/no disease. I usually do logistic regression in ...
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Whether to use linear regression or not
Effects of growth hormone (GH) replacement with recombinant human GH on bone and mineral metabolism were studied in 36 GH-deficient children. Several outcomes, including serum ionized calcium levels, ...
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When the response variable is a proportion (r/n) in proc logistic, how to express the model in terms of $\pi, \alpha, \beta$?
A (SAS documentation file) (page 1906) on "The LOGISTIC Procedure" gives the following procedure-
proc logistic;
model r/n=x1 x2;
run;
Here, n represents the ...
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Adding not chosen alternatives as data to logistic regression model
I am interested in predicting shopping behaviour in a shopping center. I have a database with the chosen alternatives (shop) and variables describing that alternative (like type and size) and the ...
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Choosing between logistic regression and Mann Whitney/t-tests
I have a dichotomous variable $A$, which does not have an a priori determined proportion of 0's and 1's, and a continuous variable $b$.
In scenario 1, I decide to designate $A$ as the independent ...
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Shifted intercepts in logistic regression
I have a question about the effects of shifting the intercept in a logistic fit on the mean of a particular transformation of the scores.
Here is the notation I will be using for the question. The ...
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"Wrong Sign" On Regression Coefficients - Hierarchical Multiple Linear Regression
I am analyzing my data on the relationship between spirituality and negative emotional states (depression, anxiety, and stress) using a hierarchical multiple linear regression. Everything seemed to be ...
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Why am I getting a zero parameter estimate and SE for one of my variables in a logistic regression?
I am doing a binary logit analysis, in which I'm trying to fit a model to my data to explain why some towns adopt open space subdivision ordinances (OP), using a handful of discrete and continuous ...
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How to calculate 95% CI for OR for a different reference category without running the SAS logistics again?
My question is about calculation of confidence interval (CI) for odds ratio (OR) from a SAS output of a logistic regression model for a different reference category without running the SAS program ...
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Which model to use with repeated measures data that contains multiple binary dependent variables
What model should I use??? I have daily repeated measures data. It has multiple dependent presence absence variables, (of which, I have collapsed into a CA with continuous variables of CA1 & CA2....
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Predicting ordered logit in R
I'm trying to do an ordered logit regression. I'm running the model like so (just a dumb little model estimating number of firms in a market from income and population measures). My question is ...
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Is there a better alternative to a logit / probit regression when all dependent variables are dichotomous?
I'm working on a clinical trial dataset with binary response. All independent variables are also binary. My first impulse was to simply run a standard logit / probit regression and be done with it. ...
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Polynomial kernel in logistic regression?
So I have put together a nice logistic regression program that works quite well. Now, I have used two dimensions to test it and see how it works, and guided by some online tutorials, have increased ...
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What is the difference between a "link function" and a "canonical link function" for GLM
What's the difference between terms 'link function' and 'canonical link function'? Also, are there any (theoretical) advantages of using one over the other?
For example, a binary response variable ...
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Can I interpret the inclusion of a quadratic term in logistic regression as indicating a turning point?
In a Logistic Regression with linear and quadratic terms only, if I have a linear coefficient $\beta_1$ and quadratic coefficient $\beta_2$, can I say that that there is turning point of the ...
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How to diagnose multicollinearity using the output of vif function in R?
I am running a logistic regression in R and am attempting to determine if multicollinearity is a problem with my model.
When I run vif() on my final model, I get <...
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How to encode factors as dummy variables when using stepPlr?
When using the step.plr() function in the stepPlr package, if my predictors are factors, do I need to encode my predictors as dummy variables manually before ...
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Logistic regression on a dataset with duplicated records
I have a large data set with multiple records per phone number. Each record has two variables - the number of past attempts in the past 60 days (number of times it was called) which range from 0 to 30 ...
- 7,940
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Logistic growth inflection point
I have a logistic growth curve as follows:
$y = \frac{1}{(1 + ae^{-bx})}$, where x is the independent measure (x-axis) and a and b are paramaters. The inflection point of this equation is when y = 0....
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How to determine the best relationship (linear, log, etc.) between input predictor variable(s) and output variable for multiple linear regression?
I am trying to determine the most accurate relationship between two variables (each predictor versus the output eventually). I want to know if the relationship is linear, or log-linear, or log-log, or ...
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Regression with repeated measures in Matlab
Is there a way to perform multiple logistic regression on repeated measures data using Matlab?
I have a data set containing a daily measurement recorded from 20 participants for 60 days. I am ...
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Reporting contrasts between binary probability parameters in Bayesian data analysis - odds ratios or difference in probability?
In a bayesian data analysis, if one is modeling differences in binomial/bernoulli probability parameter differences between populations, is it still standard to report the difference in the binary ...
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Which logit or probit model should I use for multiple response / dependent variables?
I have $300$ time series objects that constitute the $300$ columns of matrix $X$. This matrix has $5$ rows and represents $5$ days of time series information for each $300$ columns.
I set up a $300\...
user14281
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Validating a logistic regression for a specific $x$
I have a logistic regression model for 0/1 binary response data that is built from samples $(x_1,Y_1),\dots,(x_m,Y_m)$, where $x_1,\dots,x_m$ are, fixed, nonrandom, real values and $Y_1,\dots,Y_m$ are ...
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Hypothesis test for odds ratios
I have two possible exposure variables (A and B) for use in a statistical model predicting a binary health outcome. I have fitted models with each variable separately and now know that one variable ...
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Transform continuous variables for logistic regression
I have large survey data, a binary outcome variable and many explanatory variables including binary and continuous. I am building model sets (experimenting with both GLM and mixed GLM) and using ...
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Logistic regression classifier with non-negative weights constraint
My feature data is defined in such a way that I believe all weights must be non-negative.
I am looking for a reference discussing how to optimize the weights of a logistic regression classifier with ...
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Marginal effects with logistic generalized additive model in R [closed]
I am currently working with a logistic semi-parametric model in R using the mgcv package. The output from the model gives the standard log-odds coefficients; however, reviewers have requested ...
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How to deal with quasi-complete separation in a logistic GLMM?
Update: Since I now know that my problem is called quasi-complete separation I updated the question to reflect this (thanks to Aaron).
I have a dataset from an experiment in which 29 human ...
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Combination of multinomial logit model and logit model
I want to analyze the determinants of credit constraints of a firm. I have information for both formal and informal credit. I have 6 categories of credit-constraint statuses of a firm for formal ...
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Question about weighted logistic regression
I'm helping a friend perform a logistic regression on field data pulled from several archeological sites. By chance the overwhelming majority of records in the analysis dataset were collected from a ...
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Is it necessary to report the bivariate correlations when reporting logistic regression?
I am writing up a logistic regression that looks at parenting factors (ie., parental depression, anxiety and stress) as predictors of the presence of a child anxiety disorder diagnosis.
What is the ...
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Report coefficients or odds ratio in ordinal logit/probit?
I'm doing ordinal logit/probit only to analyse the direction of causality (e.g. if some variable makes it more likely to observe a low scale or a high scale). No interpretation is needed beyond this.
...
user14281
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Common parameters for conditional likelihood
I am trying to understand the concept of conditional likelihood in the context of logistic regression.
One paper I am reading defines $L(\theta; y|x) = f(y|x; \theta)$, then goes on to point out ...
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How to estimate the deposit mix of a bank using interest rate as the independent variable?
Let's say a bank has 5 different types of deposits. One type is certificates of deposits (CD), and the other 4 types are different checking and savings account products with various interest rates ...
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Negative coefficient in ordered logistic regression
Suppose we have the ordinal response $y:\{\text{Bad, Neutral, Good}\} \rightarrow \{1,2,3\}$ and a set of variables $X:=[x_1,x_2,x_3]$ that we think will explain $y$. We then do an ordered logistic ...
user14281
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Using multinomial regression's coefficients to derive predicted outcomes in C#
I am attempting to use C# (and the alglib library) to calculate the predicted probability that an outcome ends up in one of five classes. I have managed to calculate parameter estimates (i.e. slope ...
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Does learning rate have additional meaning in logistic regression?
I try to implement logistic regression with auto-correcting learning rate and I am puzzled by the outcome.
At some point the cost of the function gets bigger than previously (to focus on some numbers ...
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How do you get lmFuncs functions of the rfe function in caret to do a logistic regression?
I've been experimenting with the rfe function in the caret package to do logistic regression with feature selection. I used the <...
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How to interpret the odds ratio in a logistic regression with proportion as a response variable
I have a glm model for some data with a proportion as the outcome variable as follows:
...
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