Questions tagged [predictor]

Refers to the variables used in a model to predict a response. This tag can also be used for $X$ variables in explanatory & descriptive modeling, not just predictive modeling. This same construct goes by many names in different contexts, including: independent variable, explanatory variable, regressor variable, covariate, etc. This tag can be used for any of these synonymous terms.

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How to convert pairwise dissimilarity matrix into continues predictor in R

I have collected plant species data and AGB from 25 different sites and I want to do regression analysis of Beta-diversity (i.e. dissimilarity among 25 sites) as a predictor and AGB as response ...
Gossaye H's user avatar
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Reporting results for time-invariant predictors in latent growth curve models

I am results for reporting a time invariant predictor of the intercept and slope factors within a latent growth model, adjusted for covariates such as age and sex. My dependent variables and ...
Aepkr's user avatar
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Regression with depended predictors, animal study sample size [closed]

I have a few questions for help, One of my predictors is for treatment and control. Another predictor is dosage. Treatment group has 4 dose levels which control group only has one. Can I still use ...
aqen's user avatar
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Impact of correlation method using mice::quickpred()?

I use Multiple Imputation with MICE in R for dealing with missings in my survey data. I have two huge question marks in my head at the moment: As I have quite a lot of predictors (actually all ...
rNewbie's user avatar
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Logistic regression with highly left-skewed data for the independent variable

I am using the in-built GLM function in R to identify the model that best predict frogs' occupancy based on survey data. One of the independent variable (saturation) is highly skewed, as 36 of the 57 ...
Marco Lassandro's user avatar
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What's the difference between Mediators, Co-Variates, Moderators and Confounders terms?

I was wondering if someone could shed some light on the difference between the above-mentioned terms since I see them used frequently in many research publications I've read. If I have an outcome ...
David Musoke's user avatar
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Testing Exogeneity of regressors

Good evening, I have a problem with solving this exercise: I could calculate the F-Statistics with the weak instrument test on my own. But I don't know how to test for exogeneity with two endogenous ...
Mrs. Friday's user avatar
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Count data and proportion covariates: best practices

I'm working with spatial data and I have the following log-linear model for count data. Let $y \sim Poisson(\lambda_{i})$ such that $$ \log \lambda_{i} = \text{x}_i^\top\beta_{} + \epsilon_{i} $$ such ...
BelwarDissengulp's user avatar
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Do I have to use the exact same variables in each step, if I have a two-step propensity score match followed by a regression?

I am using the propensity score match to grow my sample based on a smaller dataset of existing units that received the treatment. The match will find more likely units from its large population that ...
LifelongLearner2's user avatar
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Prediction Machines Word Problem (Conditional Probability?)

I am new to statistics but had what I think is a pretty simple question: Prediction machine 1 correctly guesses the outcome of binary (yes/no) events 60.4% of the time. Prediction machine 2 correctly ...
Eigeas's user avatar
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Question regarding control variables in a serial mediation model

i encountered questions regarding running a serial mediation model in mplus. my IV is ERS(excessive reasssurance seeking) DV is depression (DEP), my proposed two mediators are INU (internet use) and ...
Claire Cheng's user avatar
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What is the number of "explanatory variables" in the adjusted R2 formula

I am trying to assess the goodness of fit of a surface I've developed (i.e., model predicts a variable y based on 2 variables x1 and x2). My model has 5 parameters which are estimated (and is likely ...
Erik's user avatar
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Can a Dependent Sample T-Test be Used on Sample Group (A) of 100 Electrical Devices, Tested at time, t1, shuffled, and Tested at time, t1?

Description: I have a group of 100 electrical parts being testing for Forward Voltage, at time, t_1. This is my sample group, S1. This same group is undergoing a stress test that may or may not affect ...
randomguyz's user avatar
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3 answers
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When should one control for covariates?

Suppose that one wants to estimate the effect of X on Y in the following causal diagram Should one take Z as a covariate (and why/why not?) For example, suppose that one wants to estimate the effect ...
Sam's user avatar
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Logistic regression sample size calculation and 10:1 rule of thumb

I need to calculate the sample size for a proposed logistic regression. For the dependent variable, outcome is coded as disease or no disease. The major predictor is a continuous variable. There are ...
aqen's user avatar
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How to mathematically prove the "transitive property of nested predictors"?

QUESTION I am studying the structure of experiment data sets, and I want to propose a rule that I call the "transitive property of nested predictors". The general idea is that… if there are ...
Chris Science's user avatar
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What references assert that the effect size of a nonsignificant covariate may be high?

An answer to a FAQ notes: The effect size of a covariate may be high, even if it is not significant. Does anyone know of papers I can reference to support that assertion? FWIW, I do understand why ...
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Can logistic regression predictor variables be nominal?

Can logistic regression predictor variables be nominal (like the charlson comorbidity index which ranges from 0-6)?
learning_890's user avatar
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Interpreting Regression coefficients where Y is an index

I am having trouble with the correct interpretation of my regression coefficients. My independent variable is fund allocation which is a continuous variable. I have two output variables on which I ...
user584534's user avatar
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1 answer
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With logistic regression, how does one choose a number of predictors when preregistering a study?

Harrell's Regression Modelling Strategies suggests that the number of predictors should not exceed $m/10$, $m/15$ or $m/20$.* For logistic regression $m$ is $\textrm{min}(n_1, n_2)$, where $n_1$ and $...
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exact match on covariates or include in a model?

The scenario is I have a binary outcome, a treatment group variable (3 groups) and 3 covariates. It is possible to exact match all 3 covariates on 1:N from group 1:2 and group 1:3. Would you perform ...
brucezepplin's user avatar
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Does adding a random intercept for subject address confounding variables within subjects (e.g. sex or age)?

Let's say I am interested in identifying associations between a blood protein and disease activity, but I have multiple measurements per subject. Based on a literature review, I expect sex differences ...
HarD's user avatar
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Estimate one regression coefficient for different variables

I would like to know whether it is possible to compute one identical coefficient for three different variables in a regression. So instead of estimating $\beta_1$, $\beta_2$, $\beta_3$ here: $$ Y=\...
ttttt's user avatar
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Target variable is defined by combination of input features

I am trying to create a classification model which predicts whether or not a customer comes back to make a second transaction (after having made an initial transaction). I have details on date of ...
piper180's user avatar
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Is it possible to apply a Kruskal-Wallis to data without clear dependent/independent variables? [closed]

I'm trying to find a NHST for my data that, as far as I know, are only compatible with a Kruskal-Wallis test. However, my variables aren't really identifiable as either dependent or independent; ...
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Effect of two independent variables on a dependent variable, each containing several factors in Likert scale form

For example, the first independent variable consists of 30 factors, the second independent variable consists of 21 factors, and the dependent variable consists of 21 factors. There are four options ...
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Interpretation of ANCOVA output

I am struggling to interpret my ANCOVA output - I have looked at similar questions and can't figure it out. I am looking to determine whether there are gender differences in the impact of a specified ...
bsh7194's user avatar
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Choice of Variables for Linear Regression

Say, I have a dependent variable $Y$ that I want to model with a linear regression and independent variables $X_i$. I am assuming that two of my independent variables $X_1$ and $X_2$ have an effect on ...
johannes's user avatar
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Modeling considerations when data spans different events (time) and exhibit a (relatively) low mean and high variance

I have weekday data ($n = 1551$) from the past 5 years (2019-2023) with attendance at a large restaurant. I am just getting started, and for each weekday I calculated the mean and the variance as per (...
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Experimental design study on arousal/attention

I hope this question is simple enough, suits this forum and does not consume much of your time. Essentially, want to make sure I have the appropriate design that answers my research question without ...
Jose Teles's user avatar
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1 answer
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Interpertation of a conditional quadratic latent growth curve model (i.e., with predictors)

I have a conditional quadratic latent growth curve model and am wondering how to interpret the results. My predictor of interest is significantly associated with the slope factor (B = -0.45, p = .001) ...
Aepkr's user avatar
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How to multivariate regressors work?

while I seem to understand that there is a general matrix-based formula that allows us to solve for multivariate regressors, when looking at the non-matrix solution for a bi-variate $\beta$ I realised ...
fruitless fruit juice's user avatar
5 votes
5 answers
446 views

Structural Equation Model design

Is it necessary in structural equation modeling (SEM) to incorporate all potential independent variables that could affect the dependent variable? Or is it acceptable to examine the influence of only ...
Marjaan's user avatar
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4 votes
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Can cross-validation be involved in model-building rather than validation?

I have a general idea in mind that would go like this: randomly split the data into training/testing build a model on the training data by choosing from among candidate predictors evaluate it on the ...
Dave's user avatar
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LCA with covariates: is it still worthwhile to use 1 step approach?

Dear statisticians' community, I am trying to compute a Latent Class Analysis through Stata and/or R. I built a 5 classes LCA model using poLCA on R and added a set of covariates. It seems from the ...
Irene 's user avatar
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Test(s) for comparing predictor quality for different features?

I applied a predictor to different numeric features from the same group of subjects (n>1000). The features have different ranges. Is it possible in this case to apply a statistical test to make a ...
user1448268's user avatar
2 votes
1 answer
58 views

Controlling for a variable when estimating interaction effects

Consider the relationships among four variables: sex, activity, height, and ...
bluepole's user avatar
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Are there any viable alternatives to linear mixed models when the independent (predictive) variable lacks follow-up data?

I am attempting to conduct a longitudinal analysis on a dataset with one independent variable and five dependent variables. We aim to determine whether the independent variable can predict changes in ...
Ali Reza Keshavarz Bahaqiqat's user avatar
2 votes
1 answer
84 views

Censoring proportions and covariate defining when simulating survival data using simsurv package in R

I'm trying to identify how the cox proportional hazards model behave with different censoring proportions and sample sizes. (Censoring proportions = 20%, 50%, 70%). For that I need to simulate ...
Nipuni Opatha's user avatar
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273 views

What is covariate imbalance?

Covariate imbalance refers to an unequal distribution of independent variables (covariates) among different groups in a dataset. Is the above definition correct? So, would the following be an example ...
Anne Maier's user avatar
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Can I run a PGLS model with explanatory variables in different units?

I am looking at the effect of altitude on a trait and I want to include the effect of the interaction between latitude and altitude, using a PGLS. Can I include altitude (measured in metres) as one ...
PowellHall's user avatar
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Label encoding performing well despite data being non-ordinal

So I'm currently training a model where the dependent variable is continuous and 9/11 of the independent variables are categorical, some of these categorical variables have upwards of 10,000 classes ...
SDU100's user avatar
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1 vote
1 answer
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What does small optimism in predictor effect mean?

I'm reading the following paper by Burke et al. Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes The paper discusses the minimum number of samples ...
Connor's user avatar
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Direction of main effects in cox regression and negative binomial regression changes when adding variables

For a research I am conducting I am looking at the effect of two categorical variables (dummy with values 0 and 1 for each group) on the speed and quantity with which customers make future purchases. ...
user234's user avatar
4 votes
1 answer
75 views

Controlling for a confounding variable in regression analysis

This question has come around due to a comment from a reviewer on a journal submission, but it has me interested and I want to see the general discussion on the subject. I have a study where I'm ...
Rhys Maredudd Davies's user avatar
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53 views

SEM : covariates and methodological questions regarding model simplification

I am new to SEM and have some methodological questions. Here is the model I would like run (LV = latent variable and OV = observed variables). First, from the example I saw on the web, gender, age ...
Adeline Lacroix's user avatar
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1 answer
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Interpretation of lavaan growth covariate parameters (effects on intercept and slope)

I am trying to interpret the output of the growth command with regressors. Here's the command, and the covariates are dummy coded 0/1: ...
Jeanne Sinclair's user avatar
2 votes
1 answer
55 views

how to interpret interacting plot of predictors or how to find which predictors are interacting

I am trying to see which predictors in my dataset are interacting with each other to see if their inclusion can improve model prediction. I followed these steps to do my analysis: First I used ...
Katherin Wright's user avatar
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How to know if a string variable (state) is a good predictor of another variable (social_class)

I have a dataset of users, states, and social classes (each user lives in a state and has a certain social class). The dataset consists in only 2 columns: state and social class, and each row ...
ETF's user avatar
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How do I choose one covariate out of many covariates that might have similar effects?

I plan to run a logistic regression model to understand the influence of temperature on the occupancy of a hare species. However I can't decide on which aspect of temperature should I consider as my ...
Yadav Ghimirey's user avatar

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