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Questions tagged [binary-data]

A binary variable takes one of two values, typically coded as "0" and "1".

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Coefficient of determination with binary prediction

The coefficient of determination $R^2$ is a popular measure of regression performance that compares the mean squared-deviation of predictions to the variance of the actual data. If instead of ...
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Extremely high logloss in binary classification problem [duplicate]

I have a binary classification problem that I am currently trying to tackle with xgboost. This is a low signal-to-noise ratio situation dealing with time series. Per this answer "Dumb" log-...
Baron Yugovich's user avatar
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Logloss worse than random guessing with xgboost

I have a binary classification problem that I am currently trying to tackle with xgboost. This is a low signal-to-noise ratio situation dealing with time series. My out of sample AUC is 0.65, which is ...
Baron Yugovich's user avatar
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How does conditional logit deal with binary predictors in choice sets of three or more alternatives?

I am interested in clogit, not multinomial logit, and the overall effect of the predictor on the choice of n alternatives that can only be identified by their attributes (i.e., the choice sets are ...
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Conducting a network meta-analysis in R - inputting dichotomous data

I am looking to conduct a meta-analysis in R using {netmeta}. However, my outcome of interest is a little bit complex and would appreciate advice in how best to enter it into the dataset. I am looking ...
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How do I measure the regularity of the distribution in a list of binary data?

Suppose I have a list list = [0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1], which gives information about whether a person was sick on a day (1) or not (0), since ...
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What is the appropriate statistical test in this scenario?

I work with a particular genus of plants where there have been many instances where what was once considered a species is now considered to be a synonym of another species. I am interested in ...
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Similarity measure for sparse, ordered, binary vectors, with more weighting to True values

I have two sparse, ordered, binary vectors. The size of the vectors is around ~100. I am under the impression that cosine similarity is useful for sparse, ordered, binary vectors. For my purposes, it ...
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Can you use a binary outcome variable in a longitudinal latent-growth mediation model

I am wanting to create a LGCM in which I am assessing whether two latent factors (the intercept and slope) mediate the relationship between a continuous predictor and binary outcome variable but am ...
user417995's user avatar
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Testing for difference in trends of binary-parameter proportion/fraction versus continuous parameter between two (or more) different populations

I have three samples of objects: sample X (total = 138), sample Y (same basic class as X, but located in a different environment; total = 186), and sample Z (total = 996). For each object, I have a ...
Peter Erwin's user avatar
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Handling Composite Variables in Latent Class Analysis

I am working with secondary survey data that includes 13 dichotomous variables. According to my theoretical model, I need to group these variables into composite variables. However, I understand that ...
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Quantifying a Sequence of Binary Outcomes

I'm compiling a dataset on animal color patterns. Blotches flank the body of the animal (Agkistrodon contortrix) and meet along its midline (i.e., spine). At midline, the blotches can join ...
Ross Couvillon's user avatar
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Correlation Coefficent is higher when likelihood of an event is lower, how does this occur?

I have different variables that I am interested in if they influence pass/fail rates. To see what variables I might use as a leading indicator, I've pulled different variables such as "tutoring&...
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How to draw a ROC curve given estimated probability that a unit is positive and actual observed class? [duplicate]

Assume that a classification model fitted to data available to you has provided for each statistical unit a probability $P(+|x)$ that the unit is positive. The following table shows all available ...
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Panel Data logistic regression

I am working with a panel dataset that describes company code as an id variable and fiscal year as a time variable (1500 firms from 2013-2022). The dependent variable is binary. First I checked for ...
Lou's user avatar
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Test for multicollinearity with binary and continuous independent variables

I have a question concerning multicollinearity: I have several independent variables. Some are binary and some continuous. The dependent variable is binary. Can I use the Pearson correlations to test ...
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Proving the equivalence of two distinct approaches to multiple regression for binary classification

I'm stuck with this peculiar problem that uses multiple linear regression in order to solve a binary classification problem (note: it's not considering the logistic version or any other GLM approach). ...
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Is there an way to model random effects in a design that is typically analyzed by the McNemar test?

My question is: if in a study with paired binary response data (where McNemar test is often used) we can use the exact binomial test to test the odds ratio, is it possible to model the same odds ratio ...
jeffalltogether's user avatar
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Predicting if individual has a trait based on their previous locations (distribution of trait at locations is known)

I have been thinking about a problem and I was wondering if anyone has the right name/method/textbook of how this problem should be approached, or if they would be willing to help with finding a ...
oventerrible's user avatar
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Binary response vs probability response

Consider these two scenarios: respondents are asked to choose between two options offered to them (the resulting data is binary 0 & 1) respondents are asked to give their probability of choice ...
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binary timeserie to binary timeserie regression [closed]

I'm looking for a way to determine which of my predictive variables (binary time series) best explains my response variable (also a binary time series). Basically, I ask people to listen to audio ...
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Confusion about the chi-squared test to compare 3 proportions

I have run an within-subjects experiment in which each participants answers to 3 binary questions (A,B and C). This means that all participants answered every questions. I want to compare the ...
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Can I use Tanh before softmax?

I am researching the use of neural networks for binary classification tasks of financial data. The output result is two-dimensional, such as [[0.5,0.5], [0.1,09]]. In the case of only 2000 small ...
Mar7's user avatar
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How to show that MLE of probit regression does not exist due to data separability

Claim The claim is the is the following: Assume we have the simple probit model $E(y_i|x_i ) = Φ(α+\beta x_i)$. Now suppose that $y_i = 1$ for all $x_i ≤ 10$ and $y_i = 0$ for all $x_i > 10$. Then $...
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Multiway McNemar-Type Test?

I need a test that would be similar to McNemar, but has e.g. 3 binary outcomes (0,1). Kappa also comes to mind for something that may be similar like interrator agreement. However, my hypotheses is ...
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Is relying on just the confusion matrix for highly imbalanced test sets to evaluate model performance a bad idea?

I have a binary classification model with a test set that is highly skewed, the majority class 0 is 22 times greater than the minority class 1. This causes my Precision to be low and Recall to be high,...
statsnoob's user avatar
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How to tweak my glmmTMB model to address several items? i.e. covariates, reference levels, random factors, and zero-inflation model

I recently ran a Zero-inflated negative binomial mixed model (ZINB hereafter) using the glmmTMB function from the glmmTMB ...
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Mixed model for repeated measures logistic regression?

I ran an experiment where participants were randomly assigned to one of two conditions (control vs. treatment). In both conditions they had to make 8 binary choices. That is they were presented two ...
Mina's user avatar
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Predict COVID spread using latitude and longitude and time

I have a data that has latitude and longitude of individuals and the timestamp of geographical locations. I want to predict the spread of COVID using R using latitude/longitude and the time as well. I ...
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Scaling the odds-ratio of a binary logistic regression

Just a quick one - to put it simply, I am conducting a study regarding age and marriage. I have found that with a binary logistic regression (dependent variable yes/no to being married), the odds-...
Vito's user avatar
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SPSS Multicollinearity Binary Logistic

Just a quick one - I am running a binary logistic regression in SPSS and I wanted to check the VIF for multicollinearity. How do you do this when the dependent variable is binary - is it a case of ...
Vito's user avatar
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Choosing the correct evaluation metric between F1-score and Area under the Precision-Recall Curve (AUPRC)

We're currently working on detecting specific objects (e.g. poultry farms, hospitals) from satellite images. We've modeled the problem as a binary image classification task (i.e. classifying images ...
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Interpreting Lagged Dependent Variable in Binary Logistic Regression

I am running a binary logistic regression to test the purchasing of a gym membership in 2021 against a series of controls (ie. income, gender). Included amongst these control is a lagged dependent ...
Vito's user avatar
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Simple structure for selecting machine learning model

I recently came up with the following structure to assess which models work best for some simple classification tasks. Assume that we have some labelled multivariate data, already splitted into X and ...
Marlon Brando's user avatar
4 votes
1 answer
273 views

Making sense of binary logistic regression results/Interpreting odd ratio in r

I am using binary logistic with two response level, Yes and No. The results are in log odd I learned and was advised earlier (question was on multinomial logistic regression) to exponentiate my ...
Amelia Nicodemus's user avatar
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KMO calculation for dichotomous variables

The phi coefficient is a measure of association for two dichotomous variables, and a Pearson correlation coefficient estimated for two dichotomous variables will return the phi coefficient. According ...
Allison Ma's user avatar
1 vote
1 answer
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Repeated measures ANOVA on proportion success or logistic multiple regression on total binary data?

I have 24 subjects. I have measured distance displaced between image frames at a framerate of 1Hz, so how far did the subject move in a 1sec interval? The continuous movement variable contains many ...
user409758's user avatar
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finding threshold of activation

Dear crossvalidated community, I am working on a serie of binary measures $y$ (0s and 1s), and an environamental variable $x$ (temperature). I suppose the existence of a $\bar x$ that acts like a &...
Fabio's user avatar
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Creating a binary vector with specific proportion of ones

I want to generate a binary vector of size $n=20$ with a proportion of ones equal to $\frac {3}{20}$ binary_vector <- rbinom(20, 1, 0.15) But why this does not ...
Happy Cretine's user avatar
1 vote
2 answers
28 views

How to compare two groups with a single binary measurement

I have two groups of people (for example male and female) and I performed an experiment on both groups with a single binary measurement. (for example I observed if each subject has performed a ...
meysam imani's user avatar
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21 views

Analyzing binary data with repeated measures across different levels in R

I have this data frame: https://i.sstatic.net/EGh1z.png It is the binary recorded responses (repeated measures) of subjects to 4 different tests under 2 environmental conditions (2x4 factorial design ...
Paris 's user avatar
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1 answer
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glmer problems in seeing all variables

I am trying to run a binomial glmm to understand the relationship between various concentrations of a compound sensed by different castes of ants. We have 5 different compound concentrations (a-e), ...
Allyssa Hinkle's user avatar
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Logistic regression data with reversed matches

I am new at using logistic regression for binary classification problems, but am interested in using it to help determine winners and losers in this year's March Madness tournament. I have data ...
Matt Cremeens's user avatar
3 votes
1 answer
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How to encode multiple Boolean targets for logistic regression

I'm working on the SAMHSA Mental Health Client-Level Dataset. I'm trying to train classifiers to predict the disorder given the rest of the columns. There are 13 binary disorder columns (bipolar, ...
Jackson Walters's user avatar
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Weighting F1 score in a way to preference FP minimization over FN

We have a use case where we are using F1 score to optimize threshold selection for a binary classifier. In this use case however, FP's present a higher risk impact than FN. We'd like to consider a way ...
F1_score_tuned_for_usecase's user avatar
3 votes
1 answer
80 views

Application of robust Poisson regression

I am applying a Poisson regression with robust standard errors to model a binary response variables. I was wondering what are the assumptions underlying this type of regression? Does robust Poisson ...
zhiheng yi's user avatar
1 vote
1 answer
93 views

Binary logistic regression: p-value of predictor containing all cases of response=1

I'm analyzing a dataset with a set of binary predictors and a binary response variable using logistic regression. The response variable equals 1 only if some variable $x=1$, so there is a clear link ...
Janda Kunegunda's user avatar
3 votes
2 answers
77 views

Am I finding redundant columns in my data using Factor Analysis

I have a pandas data frame with 50 columns and 10 rows. The columns represent events and the rows are days. If an event occurs in a day, then the corresponding cell is a "1", else, is a &...
slow_learner's user avatar
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25 views

Assessing Impact of Categorical Features in Classification with Multiple Models

I am working on a binary classification problem with a limited dataset (50 observations), aiming to understand the influence of categorical features on the target variable. I'm exploring several ...
Mamad Fasih's user avatar
3 votes
1 answer
57 views

Multivariate Normal Distribution. How do we apply this to dataset?

I am having a hard time understanding the concept of a multivariate normal distribution. From my understanding, it assumes each group is normally distributed, making one joint normal distribution with ...
Taewooo Kim's user avatar

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