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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 check covariate is balanced?

I try to check if the covariate is balanced by computing the absolute pooled standardized difference, but I don't know how I could get the sample variance for my treated and control covariate? any ...
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Contribution of a predictor in Nonparametric regression

Is there an equivalent to a beta weights in a nonparametric regression? I am using the NP package in R and running a local linear regression where my bandwidth estimates are produced using least ...
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What types of problems can an unbalanced design cause?

I have a between subject factor called group (with three levels) and a within subjects factor called stimulus (with two types of stimulus). Du to limitations out of my control, there are an unequal ...
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What are criteria to select the variables to include in a multiple Cox regression model

I have a group over about 500 subjects and want to perform a cox-regression to find predictors of an event taking into account the time to event. I have 12 potential predicting variables that I want ...
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How to calculate Mean adjusted by Covariate?

I need to calculate the mean of a variable, adjusted by another variable. Both variables are ratio scaled. I found this online: https://ideas.repec.org/c/boc/bocode/s344803.html which does what I want,...
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Does it make sense to include a *time-invariant* covariate in a simple pre-post analysis?

Background All participants were subjected to a single intervention and a questionnaire was issued before and after the intervention. This is a simple pre-post design. Question I can analyse these ...
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Interaction not significant anymore in ANCOVA, but in ANOVA

I have a significant interaction $A\times B$ in my regular mixed ANOVA, but when I add a covariate, the effect $A\times B$ is no longer significant, but the interaction effect with the covariate $A\...
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32 views

Why covariates are used in Latent class analysis (LCA)

I am trying to learn this relatively new statistical technique LCA. In literature i saw that for some LCA models, covariates are used. I am having a difficulty to understand why additional covariates ...
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Represent Mean-Squared-Prediction error as function of covariance (or Fisher) matrix

Given a simple linear model: $$ y_i = x_i^T \beta + \epsilon_i $$ For simplicity, $\epsilon_i$ is Gaussian iid with variance $\sigma_e^2$, then the solution for $\hat{\beta}$ is given via Ordinary ...
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How to show that the error variance of the best linear predictor is inferior to the proportional predictor?

Let's consider the 1D case. How do we prove that the error variance of the Best Linear Predictor (BLP) is inferior than the Proportional Predictor (i.e. the Linear Predictor without the intercept)? ...
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24 views

Survival prediction

I want to estimate the hazard ratio for a case-control study, and I want to test how the value of a continuous covariate affects survival. Does the covariate have to be normally distributed?, that is ...
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Linear regression: how to add covariate that is a difference between conditions from another model?

I have a 2x2 factorial design (e.g., with conditions A, B, C, D) with behavioral T/F answers and physiological data. I set up a binomial logistic regression for the behavioral data. For the ...
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Dealing up with collinearity predictors' choice in xreg using auto.arima

I'm trying to do a regression with arima errors in R, with xreg in auto.arima following https://otexts.com/fpp2/ by https://robjhyndman.com/ but I have some questions about the predictors' choice in ...
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accounting for age effects: two age groups and within-group age differences

I am analyzing data from a study with two groups of about 25 healthy participants each, one young (mean age ~25 [~18-~30] years) and one older (mean age 59 [45-75] years). The study was set up mainly ...
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Controlling for confounding variables in linear mixed effects models (lmer)

I'm using lmer to test how multiple variables (in this case, treatment, species, and sex) influence avian behaviour. ...
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Difficulty working out which statistical test to perform [closed]

I am writing a research paper looking at how reported PTSD scores vary in those who have lost a loved one to homicide. I am investigating whether there is a difference in the scores at different ...
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R auto.arima with transformed covariates

I have a non-stationary output time-series (oil prices) that is to be forecasted with 20 different input time series. The series are all non-stationary. I am considering two approaches. Approach 1) ...
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When to include covariates when I hypothesize there's a difference between groups?

If I am hypothesizing that there is gender difference in a certain domain, and that another variable would moderate the gender difference, should I include covariates in the analysis? The key of this ...
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p-value of Covariate differs between Regression and ANCOVA

I'm using SPSS to run a regression analysis in order to predict navigational performance (continuous dependent variable) from self-assessment scores (continuous predictor), sex and age group (both ...
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How to model change in independent variable for Cox Proportional Hazards Model?

I'm interested in calculating a time to event model (Cox Proportional Hazards Model) and one of my independent variables has multiple measurements. I'm planning on using a time varying covariate for ...
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How to control for age in analyzing longitudinal data using mixed effects regression

I am analyzing a longitudinal dataset. Elderly subjects perform a cognitive test once a year, for five consecutive years. I want to know if there is a decline in their performance through the study ...
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1answer
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Multiple nominal categories predicting a continuous variable

This is a simplified version of the problem that I'm having, but I'm really looking for the appropriate type of analysis for the question in which I'm interested. Set up: I have a large data set in ...
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Censored Dummy Regressor

I have a dataset that contains factors corresponding income ranges of sampled persons, like people with factor 1 earn between 10,000 to 20,000, 2 between 20,000 to 30,000 . I could just make dummies ...
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Adding covariates to a log-ratio transformation

Let us say I am interested in predicting the evolution of market shares over time with a model that includes not only past lags of the variable, but also some covariates that are non-compositional, as ...
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Model comparison for predictors with differently shaped distributions

I want to use model comparison to evaluate the claim "Predictor A outperforms Predictor B in modeling Dependent X". The (potential) problem is that within my sample, Predictor A and Predictor B have ...
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Proving that $ (\hat{\beta} - \beta)' (X' X) (\hat{\beta} - \beta)$ is independent with SSE

Exercise: Prove that $ \mathbf{(\hat{\beta} - \beta)' (X' X) (\hat{\beta} - \beta)}$ and SSE are independent for a Least Squares Regression Model. Attempt: Note that by $'$ I denote the transpose ...
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Are dichotomous predictor variables appropriate in a quantile regression analysis?

I want to model the changes in a dependent variable with non-normal distribution (e.g. abundance of micro-organisms) as a function of changes in dichotomous independent variables (e.g. gender, sick or ...
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Autocorrelation in a predictor variable

Suppose that my main purpose is to model (using GLM e.g.) an annual count data by using two predictors one of which is mean annual water level measurement which, in itself, is auto-correlated (i.e. ...
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Is ANCOVA suitable/scientifically sound for my case?

I have a question regarding the suitability of co-variate. I have three groups with different levels of production in pcs (low, medium, high), material waste in % and number of machine setup changes. ...
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Zero-inflated highly skewed predictor variables

I've thoroughly searched this website and multiple others and can't seem to find an answer to my question. This is also my first post so I hope I've followed all the rules. I apologise for the length, ...
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Linear Unconditional X-Y, Non-Linear Conditional X-Y

Intuitively, I can imagine that an unconditional (i.e., unadjusted for any covariates) Y~X relation can present as a linear relation, whereas a conditional Y~X|Z relation can present as a non-linear ...
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Bayesian Statistical Conclusions: We Implicitly Condition On the Known Values of Any Covariates, $x$?

My Bayesian data analysis textbook says the following: Bayesian statistical conclusions about a parameter $\theta$, or unobserved data $\tilde{y}$, are made in terms of probability statements. ...
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Independent variable derived from the dependent variable in regression modeling

We wish to model ratings (1-9) based on several predictors. We hypothesize that the effect of some predictors may vary across the ratings. That is, some predictors might distinguish higher ratings (e....
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Different significance in GLMM interactions

I am confused about different significance results obtained in GLMM's. First, my set of relevant variables: ...
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1answer
110 views

Binary predictor with highly skewed distribution

I am running a linear regression model and I have a binary predictor that has a highly skewed distribution. For example, one category represents 96% of the data. In terms of frequency, the other 4% ...
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Is it possible to “tune” a trained model in one population so it can be used for a different population (i.e., by swapping variables)?

Say, I have trained a model to classify patients into cardiovascular disease (CVD) and non-CVD. The model building process is as follows: There is a gold standard to compare the model with. The ...
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Show outcomes decline over time [modeling stage] [closed]

I am conducting a medical work implying to [Show that after Establishment of the procedure, Outcome (post-procedure Risk factors) decline over time]. I am from math Background, so i Interpret as ...
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Get the variables that most seperate two groups letters

Problem Description I have a dataset, for which it's quite complex to explain what the data contextually means. However, I hope that I can explain the goal with some fictitious data. Let's say we ...
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Including predictors with HLM

I have data from healthy controls and patients that I plan to use in a multi-level model. I am comparing how mindfulness improves performance on cognitive tasks in healthy controls and patients. ...
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1answer
29 views

Coordinates as model features

My goal is to predict the taxi demand depending on location and time in NYC. Hence, among other variables my dataset contains coordinates. My question is, can I use them as a predictor for my models? ...
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1answer
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Checking for multicollinearity in selection of variables for regression model

For the selection of variables for a regression model, I did a pairwise correlation matrix between the different predictors and the response variable. From the pairwise correlation matrix, I realise ...
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1answer
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Interpretation of main effects in mixed regression with covariate

I am trying to analyse the effects of 3 different psychological interventions on my outcome (imagery vividness) on 3 timepoints in a linear mixed regression model in SPSS. I also want to add a ...
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Adding Input Variables

My goal is to predict taxi demand in NYC depending on location and time. I have a dataset with ~18 million observations. With that said, I could add a large number of predictors. But when would I ...
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Interpretation of residuals in linear regression

Assume I have the following regression model: $Y=\beta_0 + \beta_1*X_1 + \beta_2*X_2 + \beta_3*X_3 + \epsilon$ If I run the same model without introducing the variable $X_3$, can I interpret the new ...
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222 views

What model to use with repeated measures of count data with a factor and a covariate?

My design involves three dependent variables. They are count data: frequencies of certain words used in a 20-min conversation. They're also repeated measure: the same subjects engaged in three 20-min ...
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GWAS: how do we know we have the most appropriate model?

[Note: crossposted on Biostars here] My question is about how to know when the optimal statistical model has been selected for a GWAS (genome wide association study). I appreciate that statistical ...
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48 views

How to compare two groups on one continuous variable, in function of one ordinal variable

I've got two groups (independent variable), one continuous dependent variable, and one 4-level ordinal variable (socioeconomic status). I would like to compare the two groups but (within each group) ...
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How to chose covariates to adjust for in a regression analysis

I am running a regression analysis where my primary interest is to see if the outcome differs by the group (treatment vs. control). However, I have some 80 other clinical and socio-demographic ...
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How to interpret the output from an all-subsets regression?

I have a dataset in which I'm looking to explore the relationships between nine input variables (X1 - X9) and five outputs (Y1 - Y5). For some of the outputs I hypothesise that certain inputs will be ...
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How to check if a polynomial regression has any predictive value? [closed]

After fitting the polynomial data to a given curve, how can I check which of the many curves has the most predictive value ?