Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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Applications of "Dose Response" Outside of Biostatistics?
I was reading about a class of models called "Dose Response Models" (https://cran.r-project.org/web/packages/drda/vignettes/drda.pdf) - in the traditional sense, these are typically used to ...
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Limitations of the coefficient of determination in linear model
I have many time series, and I apply linear model "y=ax+b" to each time series individually. I want to select only those time series that has well-defined linear trend (i.e. "a"). ...
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Correlation between binary (true/false) variable(s) and a continuous variable (a real number)
I have four types of binary variables (true or false) and a single continuous variable. I want to analyze the correlation between any of those binary variables with the continuous variable. My dataset ...
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If I analyse the pre-post data, should the "pre" (time=0) be included or excluded from the reponse?
Let's assume I analyse repeated data, recorded at t0, t1...t3.
I want to analyse the response itself and then check various contrasts, for example change from baseline or consecutive.
If the model is: ...
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hierarchical multiple regression vs multilevel models in Randomized Controlled trials
I have examined the effects of an empowering intervention in three time-points (pre-post- 6-month followup) on teachers' burnout, who had been randomly assigned to experimental or control groups.
I ...
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Propagation of change through a Bayesian Network with continuous variables
I have created a Bayesian network from a number of continuous variables using the bnlearn package in R. One of the things that the package provides is the regression coefficients of each node vs. its ...
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Does it make any difference to analyse change from baseline (observational study) or post-hoc contrast post vs. baseline?
I have an observational, non-randomized longitudinal study with 3 time point + baseline (t0 ... t3). Analysing solely the post-values in such trials is meaningless. I want to analyze the change from ...
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What is the impact of duplicate data on the variance of regression coefficient? [duplicate]
What is the impact of duplicate data on the variance of the regression coefficient?.
Does increasing the size of data always certainly decrease the variance of the model coefficients?
Suppose I have ...
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Lasso regression prediction on test set is predicting towards the mean of the train set?
I am using lasso regression to predict age (continuous data) from a set having 2112 numeric features (indepedent variable).
The training dataset contains around 2773 participants. The mean of that ...
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Exists an option to avoid reference categories in logistic regression?
I was wondering if there exists an option to avoid reference classes in logistic regression by transformation estimaters (especially the intercept)? Normally the intercept contains the information of ...
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Ideal Settings for Longitudinal Models?
The way I see it, logically speaking - Longitudinal Data (e.g. medical patients being measured repeatedly over a period of time) can have one of two forms:
Case 1: All observations are measured ...
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Forecasting using regression coefficients
I have a regression-based model that is trained on market-level data that I'd like to use to make predictions on submarket level observations.
For example, I fit the following model on market level ...
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How to apply cross validation on pseudoinverse linear regression with Python?
I have the following regression problem. The dataset contains $N=20$ examples (rows) and each example is a matrix of size $M\times T=9\times60$. Mathematically,
$$(\mathbf{Y}_1,\cdots,\mathbf{Y}_N)=\...
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Find a linear scale factor and offset that minimizes total variance between two observed data sets
I have two discontinuous observational datasets that should roughly match up after linear scaling is applied to one and its offset is adjusted. They will not follow any kind of trendline, so ...
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Calculating global and local effects in a multiple regression using Cohen's d
I am calculating the effect size Cohen's d using linear regression. I am looking at the effect of disease on memory, and have also added age, education and sex as confounds to the regression. Cohen's ...
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How to Assess Multicollinearity for Independent Count and Rate Variables
How do I test an independent variable for multicollinearity if it comes from a Poisson or Negative Binomial distribution?
A common approach for testing the multicollinearity of a model's independent ...
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Is it possible to use linear regression to score a personality quiz? [closed]
I would like to be able to analyze the users input to decide which character they are in a show. Each answer option is going to relate to one character and it is going to be consistent. I am wondering ...
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Meta-regression for continuous data - effect size or otherwise?
I'm trying to parse the output of multiple studies to quantify how age impacts the prevalence of HPV in Europe. Thanks to an excellent suggestion in a previous question, it has been suggested that ...
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When we use k-means clustering with Light GBM, comparing with Random Forest
I am developping the prediction model with many parameters.
As I was not satisfied by the performance of Random Forest Regression, I tried to use
k-means clustering to regroup the similar variable and ...
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linear regression to find the highest value [closed]
I have X and Y columns, column x has client ID and column Y has their number of targets. Example given below. Is there a way to predict the highest number of target acheived any client ID using linear ...
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How do you interpret null coefficients in a fixed effects regression?
I'm trying to understand how the Covid vaccination status is affected by vaccination camps run by the factories (units) that employ them. My independent variables is the number of vaccination camps ...
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PSPP Errors Running Binary Logistic Regression [closed]
I am running binary logistic regression on a dataset and keep getting NaN and +Infinit errors in my output. NaN in the model summary and +Infinite in the Wald, Sig, Exp(B) and so forth columns of the ...
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Suggest ways to represent additive fixed effects?
The dataset I'm working with has additive fixed effect where each term is significant. I want to plot the model fit with one of the factors temp here on the x-axis ...
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Interpreting interaction effects for categorical reference group in regression
I am running a regression model in R including the following variables:
Intent = continuous DV
Attitude = continuous IV
Story = categorical
IV in 4 levels: Consumer, Heritage, Vision and Product
...
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Johansen procedure shows cointegration r=1, but ect is not significant?
I have 6 variables, all of them I(1). I tested for cointegration and got a significant result for r=1, so I decided to estimate a VECM. The problem is now that the ECTs of the VECM are not significant....
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R squared of subgroups
I am trying to predict a value using a linear regression, and I get an R squared of 0.63.
My data is composed of 5 different groups (each with different ...
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About the Numbers of Dummy Variables
I have a question on the choice of dummies in categorical regression. If there are 4 categories, can I just choose 2 dummy variables to represent them in the following way:
Category-Z1,Z2
c1-1,0
c2-0,...
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If there is a high correlation between two variables, should we expect the high linear regression coefficient?
I have a data set with multiple features, let suppose $x_1,x_2,x_3,x_4$ and my dependent variable is $y$, when I compute the correlation matrix for $y,x_1,x_2,x_3,x_4$ then imagine the correlation ...
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Mean centering and average marginal effects
I was reading Wooldridge's "Introductory Econometrics," in which he said:
"The centering of explanatory variables about their sample averages before creating quadratics or interactions ...
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Adding/removing covariates in lmer?
I am trying to add covariates in lmer, but I do have difficulty with figuring that that, can anyone help me out with this?
I have this happy_plot = lmer(happy_score ~ life_quality +(1|subject), data) ...
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Binary regressor - what happens if people move from one category to the other?
Suppose I have the following regression model:
Monthly alcohol consumed A as independent variable, regressed on a binary regressor S (S = 1 if smoker, S = 1 if non-smoker), and through OLS I get an ...
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Cox Proportional Hazards : Why not "Cox Proportional Survival"?
Recently, I thought of the following question: We are often taught about a "Cox Proportional Hazards Model" - this is able to model the hazard between different cohorts of patients, assuming ...
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QQ Plot help meaning [duplicate]
How can I interpret the following QQ Plot?
Can you explain it for example for the point 20 and 12?
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Difference between the siginificance in the summary table and the ANOVA table in R [duplicate]
In R when I am evaluating a linear model I have created, I often use the summary table and the ANOVA table. The first image below is the output from my summary table, the image below that is the ...
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95% confidence interval for the goodness of fit scores in regression
I see the following computation available online in classification setting.
...
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What does it mean when there is a pattern in residuals related to the dependent variable?
I created a linear regression model and realized I first need to check some assumptions.
Autocorrelation not existing -> valid, since it is a between-subject experiment.
Low collinearity between ...
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How can I do this ordinal regression?
How can I perform a ordinal regression analysis in R with the data shown in table 1? I have already tried to order my data (image 1). But now I have no idea what to do next.
1 - 6 represent class 0 - ...
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Leads and Lags in a Staggered Difference-in-Differences Model Stata
I'm trying to use: 1) lags to check whether the effect increases or decreases over time, and 2) leads to check for parallel trends.
I'm thinking that regressing each dummy (0 years after treatment, 1 ...
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What is the difference between regression and state-space models?
I would like to know the differences between a regression model with autocorrelated errors and state space models (time series). When should each be used?
According to this lecture, regression (linear ...
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Suitable econometric specification for my data
I'm playing around with my dataset and as a start, I want to understand whether the absenteeism and vaccination rates for employees across a large number of factories with separate units is dependent ...
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Is it possible to derive the joint probability distribution of squared OLS residuals under the classical linear regression assumptions?
Consider the linear regression model,
$$
\boldsymbol{y}=\boldsymbol{X\beta}+\boldsymbol{\epsilon},
$$
where $\boldsymbol{y}$ is an $n$-vector of responses, $\boldsymbol{X}$ is an $n\times p$ matrix of ...
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Gauss-Markov theorem explanation (linear regression)
I have attatched an excerpt from my linear modelling lecture notes, this is the statement of the Gauss-Markov theorem, trouble is it goes into no more detail after this (not even explaining what the ...
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Missing "None" class in outcome variable. Least bad way of handling missingness?
I'm dealing with a preexisting dataset with an outcome variable of suicide which entails the following classes, of which multiple can be selected, but they roughly escalate in severity.
Check if ...
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Error-Propoagation: Combine confidence intervals of two subsequent regressions
I have two subsequent regressions and want to combine the results of these two regressions. But I also want to show the increased uncertainty by somehow combining the confidence intervals of these two ...
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Alternatives to linear model regression
Let's say I have a univariate linear regression model LMR in which ...
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Partial effect of numerical variable for a fixed level of categorical variable in regression
I have a regression model (in R) as follows:
lm(price ~ time + color + brand)
where, price be the second hand price of sth (numerical), time be number of years ...
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Logistic regression simulation with respect to event occurrence (prevalence)
I am trying to simulate logistic regression data, but under the constraints of prevalence.
$$\text{logit}(y_i) = \beta_0 + \beta_1 X_1 + \beta_2X_2$$
For example, I want to create a dataset that has ...
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Correct loss function and metric for regression of count data in neural network
I am using a convolutional neural network to predict the number of occurrences of a certain pattern in time series data. Since there might be potentially any count of such patterns in a time series, I ...
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MANOVA invalid type (list) for variable R studio [closed]
I have dataframe contain 600 columns and 50 row and i want to perrform MANOVA
i tried the following code
...
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Simultanously fitting multiple regression models on one dataset
Suppose there are two groups of (x, y) pairs, and that x and y are linearly correlated but with different slopes and/or ...
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