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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19 views

How to interpret categorical covariates for Cox regression

I am using Cox regression to model the deterioration of bridges using covariates such as salt (tons/mile), average daily traffic (ADT), average daily truck traffic (ADTT), span length, snow day per ...
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11 views

Non-significant regression model (MLR) with significant predictor

I'm having trouble with interpreting some results I've found. In order to test my hypotheses I've conducted four analyses. My hypotheses were: X1 positively predicts Y1 and Y2 X2 does not predict Y1 ...
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Update an existing ordinary linear regression with new data and another covariate

I am studying a case on a topic that was studied a while ago, related to food technology. To simplify the question, let's say that there are old OLS models that relate this property ($Y$) in certain ...
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If I want to adjust survival analysis for a covariate, like age, should I add it “smth+age” or add an interaction with it “smth*age”?

I have a survival analysis with a categorical predictor called "smth". I want to adjust it for age. I don't have any idea if they can interact or not but I guess they can. Now, about the the ...
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28 views

Am I correct in my understanding adjustment for covariate vs. stratyfing by it in the Cox regression?

I'm trying to understand the difference in a Cox model between adding a single categorical covariate like sex = {male, female} and doing stratification by it. I'm not saying about such trivial thing ...
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32 views

LASSO/Ridge regression with adjustment for a covariate

I'm trying to address the following analysis problem in high-dimensional biological data. The setup is bulk gene expression data where multiple cell types (tumor and immune cells) can contribute to ...
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21 views

Event-based markov chain with observed covariates at non-constant time intervals

I am working on a project where I need to identify the traffic state at intersections. More specifically, I want to classify the situation in 5 states: undersaturation (where the traffic needs to stop ...
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3-way mixed ANOVA with a covariate - how does SPSS adjust for the covariate?

I'm trying to run a 3-way mixed ANOVA (3x5x2, where the 2 is the between-factor) in SPSS. My dependent variable is accuracy, and thus cannot exceed 1. However, when I enter the covariate measures as ...
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11 views

Is the train/test split necessary for parametric models like logistic regression?

In order to have an estimate of the prediction error in a machine learning model, I am used to split my dataset into a training set and a test set. I will train my model on the training set and ...
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How can I investigate the effect of covariates in different data frames?

I have two data frames that measure the same sizes of ears of each person, one of the data frame contain measures from the bad ear and de other from the good ear. The Ear variable is a dummy and the ...
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Why do we care about removing correlated predictors in predictive modeling?

Many basic methods in predictive modeling/statistical learning are good at picking one of the correlated predictors while removing others, such as Lasso. However, isn't it the case that in general, ...
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In multivariate analysis using PERMANOVA and NMDS, is it appropriate to include both predictors in my model?

I am an undergrad student new to much of these statistical tests. I am using NMDS ordination and Permanova testing to analyze variation in cover type (using count data) at different distance classes (...
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31 views

“Double gradient” design for ecotoxicology of particulate substances

We are designing an investigation on effects of microplastic particles (MP) on experimental soil in growth chambers. Question: Is there a significant effect? And if so, what is the form of the ...
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21 views

Random vs deterministic predictors in regression

I am reading Elements of Statistical Learning (ESL) and trying to have more of a grasp of machine learning techniques. I am a little bit confused about when to treat predictors as fixed, and when to ...
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29 views

Variable controlling in linear regression and covariates

I am analyzing cardiac data and have interests in cardiac problems and exercise. I just want to focus on the exercise effect and found AGE is significant variable. I'd like to control AGE variable ...
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Unscaling predictor variables from a GLMM using predictorEffect()

I'm running GLMMS on scaled and centered data, which has worked well. However, now I am trying to visualize my data using the Effects package and I cannot find a way to backscale my predictor ...
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Robust regression for heavy-tailed random design

As far as I know, there are robust regression methods for outliers in response $Y$ and heavy-tailed error $\epsilon$. The settings for the design matrix (predictor) $X$ is either fixed design or sub-...
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Why is the explanatory variable non-stochastic or fixed in repeated samples?

I am studying econometrics. I have been learning about deriving the variance for the OLS slope statistic in a simple linear regression model. Why is the explanatory variable considered to be non-...
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21 views

Validation on independent biological dataset

I am working in the space of cancer statistics. I am looking for reasons why it is important to validate statistical observations in an independent biological dataset. Does anyone have a list or a ...
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11 views

Is it always a bad practice to make use of p-values for independence for feature selection?

I know that if I want to assess independence between categorical variables (e.g. results of a pharmacological intervention) I can use a Pearson chi-squared test, for example. Such tests will give me ...
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30 views

Definition of independent events in probability theory (Wasserman)

In Wasserman's "All of Statistics" p.26 he gives an example of an "independent event" as "flipping a fair coin twice", where the first flip has no effect on the second ...
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11 views

What is an appropriate method for comparing strength of predictors between two regressions on different populations?

I have two populations of interest: Population 1 and Population 2. I have conducted the same linear regression model (outcome Y and predictors A B C) on both populations, for a total of two models. ...
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Is it possible to use a dependent variable derived from one regression as an independent variable in a different regression?

For my thesis, I'm researching the effect of employee job satisfaction on customer satisfaction, mediated by service quality, using a company as a case study. I was planning to determine employee ...
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29 views

What is the exact formula how the CBPS package calculated the weights?

I am currently working with the CBPS package to estimate propensity scores. However, I am wondering about the weights, the package creates. The balance plot with incorporated weights looks great, but ...
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29 views

What is the general trend with Mallow's Cp when adding/ removing variables?

Mallow's Cp is a statistic used to help with model selection within linear regression. It tests all possible subsets of predictors from the true model to find which combinations are optimal. The ...
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28 views

Should only confounders (but not covariates) be added to a multiple regression?

Up until now, it was my understanding that multiple regression can be used to estimate the influence of an independent variable (IV) on an outcome (DV) while controlling for the influence of other ...
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GLMM with 1 predictor, still necessary to do Chi-Square with Anova in R e.g.?

I frequently use the Anova function in R to test if any predictors in a GLMM are significant. Is this necessary if I only have one predictor. Can I just use the significance of the predictor instead? ...
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1answer
18 views

Independent Variable effect's change Between Two Periods

I used the Cox proportional-hazards model, which investigates the relationship between the time of occurrence of an event and a set of explanatory variables in the presence of censored data. The goal ...
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1answer
17 views

How can I classify time-series given a predictor for each of them?

Say that I have two time-series and a predictor for each of them. I would like to build a classifer that given a window of future (and unseen) samples returns which series is more likely to have ...
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1answer
23 views

Ho to explore the interaction between dependent variable and covariate?

I'm conducting LMM by adding three dependent variables (A, B, C) and a covariate (E) as the fixed effect, and the random intercept for each subject as the random effect in the model, as shown here ...
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38 views

Predicted values well outside of the censored range

I am working with an endogenous independent survey variable, $x$, which has a value range from 0 - 10, where 0 is always and 10 is never. Because the question pertains to wrongdoing, the answer to the ...
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1answer
17 views

Can predictors with little relationship with target still be useful for a model?

For example, do interaction effects mean that it is still worthwhile to train a model with predictors which do not have much of a relationship with the target?
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How to model binomial data when the dependent variable is LITERALLY dependent on the independent variable

I am designing an experiment where a subject makes a choice between two options ("low value" (LV) and "high value" (HV)). The subject will experience both options during a trial ...
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10 views

Poststratified average predictive comparisons?

Does it make sense to do something like Multilevel Regression and Poststratification (MRP) for average predictive comparisons? I have survey data with stratified sampling where minority groups were ...
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21 views

Is the assumption of independence between the error terms of a predictive regression with non-stationary or highly persistent regressors unreasonable?

Let us define a predictive regression as follows \begin{equation} y_t=\beta x_{t-1}+\varepsilon_t,\quad\text{for}\quad t=1,\cdots,n \end{equation} where $y_t$ is usually very noisy (and stationary - i....
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Running ANCOVA with multiple covariates

I'm running an analysis to explore what factors predict adherence to an intervention study involving different types of activity (either learning a language, learning computer skills, handicrafts, ...
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1answer
61 views

Running ANCOVA with categorical covariates - how to set up dataset?

I am analyzing a dataset in which 80 participants rated 4 products (product A, product B, product C, product D) from -3 to 3 depending on how much they liked the product. Each participant provided ...
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30 views

Predictors that are integers and factor types in a linear model

I am running a hierarchical linear regression. My predictors consist of both continuous and categorical variables. R treats the continuous variables as integers, and I have converted the categorical ...
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1answer
38 views

How do I solve the best predictor of X+Y given X-Y?

Suppose that $X$ and $Y$ are independent, each distributed as $$f_U (u) = p^u(1−p)^{(1−u)}$$ with $u = 0,1$; $U = X, Y$. Find the best predictor and the best linear predictor of $X + Y$ given $X − Y$. ...
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17 views

Is using a categorical variable with one category representing a specific outcome in the response 100% of the time appropriate for the model?

Not sure of the best way to word the title but this is the scenario: I am running MLR and GLMs to predict a numeric count response. Lets say one of my predictors is binary with two levels, "Yes&...
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30 views

Accounting for non-independence of measurements in analysis of variance

I have 10 measurements for each of 50 patients, each obtained at 3 different time points (i.e. 500 + 500 + 500 values). The measurements are of the same quantity but were measured in different ...
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25 views

What statistical evidences are needed to prove/disapprove a regression

I will be very honest, This is related to an assignment but i am not asking for the answers. I am just asking for guidance since it is kind of throwing me off. Background The question that i am ...
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15 views

Defining my conceptual model

How can I define my conceptual model in terms of independent/dependent and moderating/mediating variables? My research topic is about anthropomorfism and how it can reduce food waste. My research is ...
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8 views

Interpretetion of linear predictor of a random variable that follows a gamma distribution

Assuming that: $0 < \nu, \alpha, y < \infty$ $$f_Y(y; \nu, \alpha) = \frac{y^{\nu-1}{\alpha}^{\nu}e^{-y\alpha}}{\Gamma (\nu)} \mathbb{1}_{Y \in (0, \infty)}$$ $$ = \exp \{ -y\alpha + \nu \log \...
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136 views

Multiple regression vs propensity score matching for covariates in observational study [duplicate]

I want to determine if smoking is related to this cancer in an observational study. I have data from 1000 subjects with following variables: ...
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15 views

GLM Model confuses Covariates with positive and negative influence

at the moment I am working on my thesis as a biology Student. I observed a number of Insects (dependent variable) at three different Traps(Lures) at six different Locations (=categorial explanatory ...
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1answer
28 views

Adding new variable with no historical data (only incremental new data) to existing regression model

I have trained a linear regression model on several hundred observations. I have recently begun collecting data on a new predictor variable and would like to retrain the model with data from this new ...
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54 views

GLM in R: Subset Covariate to specific factor Levels

I like to conduct a GLM in R like this: glm ( Y ~ X + Z + a + b + c + d, quasipoisson(link="log")) with Y as dependent Variable (Count Data, integer) X as ...
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28 views

Can binomial logistic regression be used with repeated measure predictors?

I was reading about binomial logistic regression assumptions and a basic requirement seems to be : independence of observations. So I was wondering if predictors could be repeated measures (pre-post ...
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31 views

Multiple regression: Is it acceptable to include a categorical covariate with few observations across levels?

I am interested in the influence of age and body mass index (bmi) on brain size in a patient group. I have the following multiple regression model (using R): ...

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