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 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 ...
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Understanding the Difference Between Independent and Dependent Variables
I'm new to statistics and I'm struggling to grasp the distinction between an independent and a dependent variable. For instance, if I want to examine the correlation between daily COVID-related deaths ...
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Inclusion of three predictors that explain each other's variance
I would like to examine the relationship of a variable Y with a social construct.
There are three tests (A,B,C) that more or less characterise different aspects of the construct
(f.e., A= laboratory ...
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Inclusion of interaction term and interpretation
I'm planning to run a large amount of regressions to test for an association between medical conditions. As my dependent variable is always age-dependent, I include age as a covariate. For example, I ...
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Regression with common predictor for independent variable
Assume I have a regression, where y and one of the dependent variables (may) share a predictor. For example, if I wanted to check for a correlation between occurrences of heart attacks (age-dependent) ...
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Does mediation tell you effect of predictor above that of the mediator?
I am currently conducting a set of analyses examining the relationship between two predictors and an outcome. For example, the relationship between motivation (predictor 1), revision (predictor 2), ...
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How to choose appropriate parameter vector for linear regression?
If we have a dataset $D := f(x_i, y_i)^n_{i=1}$ where $x_i = [x_{i_1}, x_{i_2}, ... , x_{i_p}]^T$ is a p-dimensional
predictor and $y_i \in R$ is the response to $x_i$.
Now, shall we select as our ...
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Can an explanatory variable be both endogenous and exogenous?
I have two dependent variables that I will use in two separate models (namely math_score & language_score) and one independent variable (relative_power) that I will use in both models using Stata ...
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Interaction between IV and covariates in Linear Mixed Effects Model
I was wondering if it will violate any assumptions of linear mixed effects (LME) models if I were to include interaction terms between the covariates and IVs in my model.
For example, the model that I ...
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Regression with proportion values in the independent variables
I want to perform a regression where my independent variables all sum to 1. The independent variables are proportions of money invested in different categories.
What I have done:
When checking the ...
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Removing variance in $y$ explained by $x$
If I have three variables:
$x$
$y$
$z$
...how would I go about calculating an "adjusted $z$" measure that has the variation in $z$ that is explained by $x$ and $y$ removed?
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Is it necessary to standardize predictors when model averaging without interactions?
I am using GLMMs to model the influence of weather variables on bird counts, and using model averaging to generate parameter estimates for each predictor. Standardizing (centralizing) predictors is ...
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The independent variable is a share of a part constituting the dependent variable
I am conducting research on nonprofit cultural organisations for my master thesis.
I have 100 units of analysis (organization's financial statements) coming from 23 organisations. Therefore, I have ...
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In the problem of best linear predictor, why is $E(XX')$ positive definite equivalent to $E(XX')$ being invertible?
I came across the following statement in a textbook when discussing the classic best linear predictor problem in statistics. It says $E[XX']$ being positive definite is equivalent to it being ...
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Mixed models with missing fixed level on some clusters
There are a lot of publications on missing data and unbalances in mixed models but I missed the issue when unbalances or missings relates to levels of a categorical variables.
Let's assume we want to ...
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Do I have to transform a heavily skewed numeric predictor into a categorical predictor in a binomial mixed model?
I am working on a mixed model with a categorical response variable and several categorical and numeric predictors. One of my numeric predictors is heavily skewed. Should I transform it into a ...
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R packages lavaan and MeMoBootR don’t converge
**Does anybody have experience running mediation analyses with both lavaan and MeMoBootR, and can explain why the results of each (indirect effects) might not converge with the same outcome and ...
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Should I include a constant term when testing the significance of variables against a null model?
I have a one-hot vector $y \in \{0,1\}^{n}$ giving the case/control status of a group of genetic samples. I also have a genetic vector $G \in \{0,1,2\}^{n}$ and a vector of covariates $K \in \mathbb{R}...
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RM ANOVA with a CONTINUOUS between subjects predictor
I'm conducting an experiment where I have 2 IV's on a DV. I used Repeated measures ANOVA on Jamovi to analyze my data but I have a question regarding a continuous between-subjects variable. I measured ...
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If you turn a covariate into a outcome variable what would happen to the study?
I have a question; I have mood as a covariate in one of my projects. It's an important covariate. But I had to remove covariates due to insufficient numbers, so I proposed turning mood into an outcome ...
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Rank deficiency in spline with a predictor that "becomes" categorical when larger than a cutoff
My question is a follow-up from this post. I have a dataset where a predictor can have 2 states - if it's in state 1, then the value is always the same. If it's in state 2, then the value is ...
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A predictor that "becomes" categorical when larger than a cutoff
I have a dataset where a predictor can have 2 states - if it's in state 1, then the value is always the same. If it's in state 2, then the value is continuous and changes. An example to this can be ...
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Effect or Predictor(s), is it always clear which is which?
How does one determine if a variable is an effect or a predictor if all the variables are measured and none or manipulated or otherwise fixed? For example, does reduction in vascular diameter cause ...
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How do you compare the strength of predictor variables?
We have been assessing the prognostic utility of MRI markers in a patient population. I identified markers-of-interest based on Cox regression analysis. Each marker had aHR of 5.5, 3.7, 2.8, etc, ...
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Best Linear Predictor - problems with the derivation
I'm studying econometrics from the Hansen's book (2022).
I'm trying to understand the mathematics behind the following steps:
Let $X=(1,X_2,...,X_k)$ be the vector of covariates, and $\beta =(\beta_1,\...
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What statistical analysis method to use? Multiple regression?
My research is looking at ethnic differences in teenage mental health during the pandemic. Specifically I have 2 independent variables: ethnicity (categorical - 2 groups: white and non-white) and time ...
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How to parameterize covariates in parametric time to event model (lognormal distribution)
I have a parametric time to event model for survival data and I found that a lognormal distribution has the lowest objective function value for a base model. Below is the hazard function for the ...
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What are the indications that one should be using interaction variables in their linear regression model?
I am on page 87 of ISLR 2nd edition.
What are the indications that one should be using interaction variables in their linear regression model? Basically: When do you know "ah, I should be using ...
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Confounding variable and effect on the model
I have an experiment where I want to determine which variables are associated with an outcome. One important predictor is a volume. In my study I am trying to determine if a subset of this volume can ...
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Can I use two 7 point ordinal items as 2 independent variables?
The research question is if there is a correlation between language barriers and DASS-21 scale (dependent, continuous) I have 3 items that I am using as independent variables.
The first item is ...
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Do my independent variables in a logistic regression need to reach a certain benchmark of distribution?
First some information: I want to do a generalised mixed-effects logistic regression with my own data set (in R). The dependent categorical variable is dichotomous and most of my independent variables ...
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Go to models for assessing accurate slope and intercept of model for simluation [closed]
What are your go to models for assessing as ACCURATELY as possible the slope and intercept of given predictor and predicted random variables? The goal is to use simulated predictors + outputted ...
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Dividing a data set and extracting multiple means and standard deviations that predict the same response in a regression model?
I need to create a regression model where I calculate the response (final battery state of charge (SoC) of an eletric vehicle) based on the predictors (SoC_start, dist_travel, mean speed (x̄) and ...
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Include constant to estimate propensity scores?
Should we include the constant when we estimate propensity scores?
We model the probability that $D_i=1$ using a set of controls $x$
$$ p (D_i=1 | X=x) = \alpha + x \beta + \varepsilon $$
to created ...
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setting hypothesis based on 2 independent variable
Normally, we have one main IV and other covariates and show relationship with DV. Based on the main IV we conceptualize the research question and hypothesise. Say, Income (IV), Mental Health (DV) plus ...
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Prove the formula $\sigma^2(1+x^T_{n+1}(X^TX)^{-1}x_{n+1}+\sigma^2_u \cdot\mathrm{tr}((X^TX)^{-1}))+\sigma^2_u\beta^T\beta$ for the variance of error
Suppose we have a model $y=X\beta+\varepsilon$, where $y$ is an $n\times 1$ vector, $X$ is an $n\times k$ matrix of parameters of full rank $k$, $\beta$ is a $k\times 1$ vector and $\varepsilon$ is an ...
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Modeling with a profile of predictors
How would I best build a model wherein the predictors are a profile of percent membership in a size category where all the memberships added equal 1 (100%)?
What would be sensible to capture this &...
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Does multicollinearity among control variables matter?
I am conducting a regression analysis between $X$ and $Y$, where $X$ is the main independent variable. However, I want to control for several variables that are related to $Y$. For example, my ...
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Is it acceptable to create a dummy variable out of a quantitative variable?
I have a variable that takes the value of 5% or 10% throughout the data set. Is it okay to transform this variable into a dummy variable such that 10% (high) = 1 and 5% (low) = 0.
I am running a ...
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Can ANOVA be used with a categorical outcome and continuous predictor?
Of course ANOVA can be used with a continuous y and a categorical x.
If on the other hand my ...
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Is instrumental variables estimator applicable when a covariate is Spearman $\rho>0$ but not Pearson correlated with the residuals?
Is instrumental variables estimator applicable when a covariate is Spearman but not Pearson correlated with the residuals?
Does Spearman non-zero correlation with residuals imply loss of consistency?
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Adding covariates to a model with an outcome that has been normalized using the same variables
I'm interested in predicting cognitive scores (e.g. a memory test score) using multiple regression. The cognitive scores have been z-score normalized to age, sex, and years of education. Is it ...
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Understanding FE explanatory power
I am trying to understand what is going on in terms of the additional variation explained by my fixed effects.
The set up is as follows. I have a a data set of roughly 3929 firm acquisition events ...
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How to understand the significance and effect of each term in a non-linear model with interaction terms?
Let's say we have a model as given below:
$Y_1 = \beta_0 + \beta_1 X_1 + \beta_2 X_2+ \beta_3 \frac{X_2}{X_1} + \beta_4\frac{X_2^2}{X_1}$, $R^2= 0.98$
Here, $X_1$ & $X_2$ are positive integers.
...
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Can a Variable Be Both Dependent and Independent?
We can see that the GDP growth, represented by "y" is the dependent variable and independent variable. I would like to perform quantile regression in Eviews, with ...
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is it statistically correct to combine paired and unpaired samples together and perform tests?
I have samples from left hand and right hand of same individuals (11 patients, 22 measurements) and left (4) and right (2) hand from different individuals (6 patients, 6 measurements). Is it possible ...
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Help on GARCH-X model theory
I need to understand how a GARCH-X model (GARCH with explanatory variable) works.
What I've understood so far is:
we have a simple GARCH(1,1) model:
If I add to the conditional variance equation an ...
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Interpreting intercept with a categorical predictor
I have a categorical predictor (segment) and continuous DV (income). 'segment' is a factor with 6 levels. I ran a simple regression in R and got the following results:
Deviance Residuals:
Min 1Q ...
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How to model a covariate for age in my cox prediction model
I'm currently doing a prediction model using Cox regression on a dataset coming from an ongoing clinical database and containing information about patients who all have the same genetic disease.
In it,...
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Interpretation of scaled covariates in cumulative link models
As for cumulative link models, Christensen (2016) suggests that when the assumption of proportional odds is not met, it is necessary to scale one or more covariates.
I'll give you an example just to ...