# Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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### Adjusting for age and gender in ANOVA

I am performing an ANOVA to compare the means of three groups. However, I need to adjust for the effects of age and gender in the ANOVA. I'm not quite sure how to go about it in R. Conventionally, I ...
2k views

### Setting max_depth greater than the number of features in a Random Forest

I was using random forest regression to predict the price of a house. There are only 3 features in data set. Initially when I had set max_depth=2 the result was ...
137 views

### Generalized least squares error estimation

First of all, I have to admit that I am not statistician so some of my nomenclature could not be very rigorous and maybe a bit confusing; pleas ask me to clarify if necessary. The Problem Let's say ...
124 views

### Environmental variable vector in NMDS

I am using Non-metric MultiDimensional Scaling (NMDS) on a Bray-Curtis dissimilarity matrix. Then, I am trying to link the resulting NMDS axes (let's say "components") to environmental ...
7 views

### R Library for Ordinal Regression with Split-Plot (Cluster?, Repeated Measures?) Structure

I have a data set with the following structure. The response is ordinal. There is an experimental factor (with two treatment levels, each treatment level applied to a different sample of subjects). ...
63k views

### Why L1 norm for sparse models

I am reading the books about linear regression. There are some sentences about the L1 and L2 norm. I know them, just don't understand why L1 norm for sparse models. Can someone use give a simple ...
45 views

### Econometrics meaning of structural versus regression model

I want to make sure my understanding is correct. Particularly in econometrics, when authors write down a model: $Y_i = \beta_0 + \beta_1 X_i + \epsilon$ Can I think of this as a 'structural model'- or ...
39 views

### Appropriate goodness-of-fit test for the negative binomial regression

I have used the following Pearson $χ2$ test and the deviance test to assess the negative binomial regression using R as ######################################### ...
29 views

### Including a weighting variable in a linear regression

I'm looking at how temperature affects length. My length variable is the mean length calculated for every year, it is derived from ~10,000 data points. Not every year had the same sampling effort (e.g....
9 views

### Regression analysis before an optimization problem with an unkown function

I have data consisting of invested hours in different training seminars $T_{1},T_{2},...,T_{50}$ and the performance on an exam $P_{exam}$. Currently I know how much 1 hour of a training seminar costs ...
144 views

### How to model a conditional probability without estimating joint PDF?

I've around 3e3 two-dimensional data points, x, d 1 1, 0.1 2 3, 0.1 3 2, 0.2 4 1, 0.5 range(x) = [-600, 600], range(d) = [0, 1] We are trying to model ...
6 views

### What is the conditional variance of sample predictor on population predictor?

I am using the book introduction to Statistical learning with applications in R chapter 3. I've been able to find the conditional expectations, as well as the unconditional variance, but I've read ...
16 views

### Fitting a multivariate linear regression with different residual variance for each outcome (using a mixed effects model in R)

In a small simulation, I am fitting a multivariate normal model to predict two outcomes Y1 and Y2, while also modelling the covariance between them. This can be done through a mixed effects model (...
132 views

### Lasso and its dual: rates of regularisations

Let us consider the following lasso estimator: $$\hat{\beta}_{L} = \arg\min \, \frac{1}{n}\sum_{i}^{n}||y_{i} - \textbf{x}_{i}\beta||_{2}^{2} + \frac{\lambda_{n}}{n}\sum_{j=1}^{p}|\beta_{j}|$$ For ...
47k views

### Why would anyone use KNN for regression?

From what I understand, we can only build a regression function that lies within the interval of the training data. For example (only one of the panels is necessary): How would I predict into the ...
6k views

### Updating linear regression efficiently when adding observations and/or predictors in R

I would be interested in finding ways in R for efficiently updating a linear model when an observation or a predictor is added. biglm has an updating capability when adding observations, but my data ...
122 views

### How to generate data that have given conditional mean and conditional quantile using R?

Suppose I want to generate independent data $(y_{i},x_{i})$ such that the conditional mean of $y_{i}$ given $x_{i}$ is a quadratic function in $x_{i}$ and the $.25$ conditional quantile of $y_{i}$ ...
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### Can a range of priors being used for a linear regression be applied to a logistic regression?

I have trial level data from a study in which participants responded to a series of stimuli. I have a predictor of interest. For the sake of this example, let's call it the size of the stimulus. There ...
2k views

### Hidden state models vs. stateless models for time series regression

This is a quite generic question: assume I want to build a model to predict the next observation based on the previous $N$ observations ($N$ can be a parameter to optimize experimentally). So we ...
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### Assumption of linearity between variables and log odds in logistic regression

I know that in logistic regression we assume a linear relationship between the independent variables and the logits. Can you explain why is this a reasonable assumption?
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### What does linear stand for in linear regression?

In R, if I write lm(a ~ b + c + b*c) would this still be a linear regression? How to do other kinds of regression in R? I would appreciate any recommendation ...
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### How to achieve linear relationship between predictors and logit of outcome?

Prior to conducting a logistic regression of the 0/1 likelihood of a nest hatching or failing as a function of 9 continuous predictors, I plotted each of the standardized (mean = 0, SD = 1) predictors ...
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### Unstandardized estimated variance from regression

I am running a multivariate regression where both y1 and y2 are both predicted by x. For ...
13k views

### Fitting models in R where coefficients are subject to linear restriction(s)?

How should I define a model formula in R, when one (or more) exact linear restrictions binding the coefficients is available. As an example, say that you know that b1 = 2*b0 in a simple linear ...
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### Homoscedasticity and independence of errors

In linear regression I often see homoscedasticity and independence of errors listed as assumptions (for example on wikipedia). But I would think that independence of errors would imply ...
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### How do I determine when a (non-independent) time series approaches a horizontal asymptote?

I have time series data with many data points per subject over time. I want to determine the marginal time interval within which my dependent variable (dv) falls within given "equivalence" ...
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### Adding a Dummy Variable to glm in R?

I'm running a glm in R with two categorical variables, one of which is binary, the other of which can take on five values. I would like it so that my model returns an intercept value that reflects the ...
1k views

### Regression Lines With Same Intercept

So, I struggle with Regression a lot. I just found out how to get 2 lines with the same slope, but I cannot manage to get 2 lines with the same intercept. I read about ANCOVA a lot (because I thought ...