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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Question about deciding whether something is a moderator or covariate

Based on what I know, a moderation analysis is simply looking at an interaction between an IV and a moderator. However, people often refer to moderators when they talk about ANCOVA (analysis of ...
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R Mediation with continuous predictor and binary logistic regression models

I am running a mediation model using the r mediation package, but I am not getting the correct output for my variable types. I have a continuous predictor, but the output is treating my predictor as a ...
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Bayesian Additive Regression Trees: Zero-inflated explanatory variable, will it influence the model and variable selection?

I am currently implementing BART to model the distribution of a marine species (using the embarcadero package). I am using environmental covariates, but also some prey data that are very-much zero-...
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Interpretation of the linear predictor of a OLS model on binomial data

If i have some simulated standard normally distributed data: $$µ_i = β_0 + β_1X_{i1} + β_2X_{i2} +···+β_kX_{ik}$$ where $$Y_i \sim N(\mu_i, 1)$$ Created with function: (with python in this case) ...
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Split linear predictors with link function

Is it possible to split linear predictors contribution up when talking glm of non-normal distributions? If: $$µ_i = g^{-1}(η_i)$$ and $$µ_i = g^{-1}(β_0 + β_1X_{i1} + β_2X_{i2} +···+β_kX_{ik})$$ Is it ...
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Nuisance variable in a repeated-measures one-way ANOVA

I need to perform a one-way repeated measures ANVOA, while accounting for a nuisance covariate that is hypothesized to confound by dependent measure. For each subject I have one value of the dependent ...
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Is Platt scaling applicable to a small sample size?

I am learning predictive modeling and recently came across the calibration technique called Platt scaling. I want to ask: Is this technique applicable to the small sample size such as my project (n=...
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Do you absolutely need a basis type "bs=" in a GAM model?

Under what circumstances would you NOT NEED to assign a basis (ex. basis="cr") type in a GAM model? For a simple model, to see what the effect of one environmental variable is on abundance, ...
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Interpretation problems of linear model with no predictors

Let $Y=X\beta$, where $X=(\begin{matrix}1 & 1 & 1\end{matrix})^T$, $Y=(\begin{matrix}6 & 5 & 4\end{matrix})^T$ and $\beta=(\begin{matrix}\beta_0 \end{matrix})$. Now $X\beta=(\begin{...
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how to implement a next position predictor machine learning model? [closed]

I am trying to implement next position predictor base on machine Learning algorithms.I want to know how can i start building my model ?
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Should I change the dataset to one with less zeros on the independent variable?

I'm running a spatial econometrics model and one of my explanatory variables is sewage coverage, which is 0 for 42% of the cities in the country of analysis. I've been told to reduce the focus of the ...
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Does my predictor in my multiple regression have too many variables?

So I am trying to work out what is the best predictor of a) awareness over environmental issues, b) concern over environmental issues and c) pro-environmental behaviour from a set of sociodemographics ...
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only one predictor significant in multiple regression

I have the following problem: I am using multiple regressions to test the influence of different factors on trust in 4 Actors, one multiple regression per Actor. To check which variables I include, I ...
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Assessing combined effects of binary predictor variables

I'm looking to analyse the effect of 20-30 binary predictor variables on a continuous response variable. I'm no statistician, so first off I'm not sure whether a regression with this many binary ...
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Difference-in-difference covariates affecting outcome but failing parallel trends

I am performing a difference-in-difference analysis and, as advised by this post, ran my regression with each of my covariates as the outcome. I did this for each covariate individually using the ...
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Is percentile a good method?

Hello I'm an undergraduate student doing research about prevalence of carpal tunnel syndrome among college students I want to follow the method of this research (prevalence of carpal tunnel syndrome ...
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Simulate normal data for different covariates with specified means and variances [closed]

I'd like to simulate, say 1000 cases, normal data (from normal distribution). For only one covariate (e.g., gender), the male group is from N(0,sd=1) and the female group is from N(1,sd=2), I can ...
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Prediction with different lags of independent variables

I am wondering if it is suitable to make 1-step ahead forecast with independent variables of different lags? Suppose that in practice, one wants to forecast the 1-step head (i.e. 1 month) value of ...
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How to choose covariates for synthetic control

Trying to construct a synthetic control and I've chosen a set of covariates that are correlated with my outcome variable and significant for P>|t| using OLS. Is choosing covariates for synthetic ...
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Why does $E(\epsilon_ix_i)$ have the best linear predictor and $E(\epsilon_i|x_i)$ have the best predictor

Why does $E(\epsilon_ix_i)$ have the best linear predictor and $E(\epsilon_i|x_i)$ have the best predictor? Each $x_i$ is a $1\times K$ vector. All of information I could understand is $E(\epsilon_i|...
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Is this the correct test to perform?

In my research I have 4 independent variables which are gender, BMI, department, and hours spent on computer and two dependent variables which are test scores for two different tests (SSS) and (FSS). ...
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What percentage of usable cases of a predictor is necessary for multiple imputation?

Van Buuren has a chapter Model form and predictors, where he makes the following recommendations: Remove from the variables selected in steps 2 and 3 those variables that have too many missing values ...
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What alternative techniques are available to perform variable selection with big data?

I have a 88x6500 dataset of people income, where 88 are time periods (quarters) and 6500 are people. My independent variable is X = income and my dependent variable is Y = average houses price in a ...
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Adding predictors to an intercept-only model

I'm currently testing out a logistic regression model I plan to use to analyse the results of an experiment I'm yet to conduct. I'm running it in R. My experiment is going to measure whether animals ...
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Using a feature as a target denominator

Could you please tell me what (bad) can happen if I use the same feature as the denominator in the target feature and as the predictor in a boosting regression? I think I should exclude it from the ...
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MLR - Eliminating multicollinearity when predictors are transformations of others

I am applying a multiple linear regression on a data set, where some of the predictors are "transformations" of others (however, I'm not entirely sure if they are linear transformations or ...
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Question about covariates (confounders)

Assume this relationships with mediation: X --> Y --> Z now assume that covariate C influences the relationship X --> Y, Does this imply that we can use it in the relationship Y --> Z too? ...
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What if proportional hazard assumption does not hold for a confounder in Cox PH model

If proportionality hazard assumption does not appear to hold for a covariate in the Cox PH model, is it a serious matter? The covariate is included in order to adjust for potential confounding. I am ...
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Is the use of cutoffs for dichotomisation of biomarkers really that bad?

Tissue microarrays are commonly used to assess potential prognostic biomarkers. For decades now, many authors (I would even say the majority) feel the need to categorise their continuous predictors, ...
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Can we predict a predictor of an outcome within a specific outcome group only?

I have number of predictors A, B, C,...X and outcome "CURE". Among 600 observations, 30% of patients had "CURE" X is a continuous variable from 0 to 30, normally distributed, ...
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Considering sample size as controlling factor in meta-regression

In a meta-regression, authors have 3 explanatory variables of interest for 2 countries. But all these three variables were not present in all those studies which were finally selected for meta-...
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One independent variable causes the other independent variable in regression

I´m investigating how the initial fluid in the retina is related to the visual acuity change over the treatment. Basically, I have three variables: the fluid inside the retina just before treatment ...
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proportional odds (PO) ordinal logistic regression model as nonparametric ANOVA that controls for covariates

I have continuous response variables that are parameters returned by a reinforcement learning+working memory model. My group variable includes three levels: healthy controls, unipolar depression, and ...
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Combining datasets by imputing variables unique to each set, incorporating set variation and treatment groups

I have two datasets collected from two runs of the same experimental set up (one dataset per run). The two datasets overlap for about 20 measured variables, but have 20 to 40 variables that are unique....
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Finding independent predictors with multiple logistic regression

I am conducting a study in order to find independent predictors of a binary outcome variable (A=110, B=51). The main goal of this work is to verify if any of 14 variables are independent predictors ...
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Interpreting changes in the distribution of logits when adding a dichotomous predictor

I am performing a logistic regression using continuous predictors and one dichotomous predictor. I am checking the linearity of the logit using the Box-Tidwell test. I know that the dichotomous ...
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Selecting a betareg model out of all possible combinations of the EVs

I'm working on a dataset with response variable in [0,1] and n=61 observations, and trying to fit a model with betareg(). After posting my former question, I ...
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Minimal sample size or missing data requirement for Multiple Imputation by Chained Equations?

I am wondering whether I should use Multiple Imputation by Chained Equations for my sample size. I have a small sample (n=50) and there is one important predictor which I hope to include in my ...
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In a chi-square test of association, what are the "dependent" variables?

The chi-square test of association is used to determine if there is an association between two categorical variables. In statistics, we call "dependent variable" a variable that is supposed ...
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How many observations of a binary predictor should there be for multiple regression?

I have a model with ~30 binary predictors. Each predictor can be present or absent (i.e., X = 1 or X = 0). There are ~1000 observations. The data is such that multiple predictors can be present in a ...
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Proving that residuals from logistic regression are orthogonal to the predictions

I am stuck on proving that residuals from logistic regression are orthogonal to the predictions. However, I am able to prove that residuals are orthogonal to the predictors since score vector ( ...
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Predictor With Lower Mean Absolute Error Ends Up Worse

I have been recently working on a problem to estimate the ETAs of vehicles using ensemble techniques such as LightGBM. As expected, the distance taken by the vehicle's route to its destination is a ...
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"Group mean centering" a dummy Variable in R for multilevel analysis: how can i do this?

have somebody an idea of how to group mean center a dummy Level 1 predictor in R? Enders & Tofighi (2007) describe a method to center a dummy variable through substracting the proportion of the ...
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Residualizing age as a co-variate

I am running SEM model and I want to control for age (as a covariate). So I was advised to run linear and quadratic regressions between age as a predictor and the other outcome variables, then save ...
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Adjusted between-group comparisons : single vs separate linear regression approach

Suppose I have 3 groups - namely, "3" , "4", and "5" - and I am interested in comparing group "3" with "4" and with "5" on a continuous ...
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How to further explore the effect of a covariate variable in a repeated measures ANOVA in SPSS?

Using SPSS, I've fitted a model with a repeated measures ANOVA with those predictors: two within-subject variables many "covariate" variables The effect I'm mostly care about is the ...
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Upper Bound for Size of Prediction Interval

I was thinking of this problem, and I'm not sure if I'm right with this approach. X is a R.V. with unknown distribution, bounded to the interval [a,b], with a < b and both finite. If I take a very ...
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Dealing with correlated covariates

I am assessing pairwise association between covariates. I have used the chi-square test to determine association between categorical covariates, pearson's correlation coefficient between numeric ...
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Does probability independence have anything to do with functional independence (on general case)?

On normal-linear case I know it holds for example if $cor(X_1,Y)=0$ then $g_{X_1Y}(X_1,Y)=g_{X_1}(X_1)g_{Y}(Y)$, where $g_{(.)}$ is the distribution of (.), and that imples to $\beta_1=0$ on: $Y=\...
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How to create the dependent variable for amount of deals?

I have a data base in which each line is a M&A event, with the following informations: (i) target company; (ii) country of target company; (iii) industry of target company; (iv) year of ...
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