Questions tagged [multivariate-regression]

Regression with more than one response (dependent) variable.

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6 views

Exclusion of Spuriousness With More Dimensions?

The usual way of excluding spuriousness in multivariate correlation is to increase the number of samples. However, if increasing the dimensions of measurements produces an unusual magnitude of ...
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32 views

Multivariate regression (multi-target regression) dataset where some regression coefficients are known to be zero

I'm looking for a sample dataset for multivariate linear regression - also known as multi-target or multi-output. Preferably with more than 10 inputs and more than 10 outputs. There don't seem to ...
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Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math

Are multiple and multivariate regression really different? What is a variate anyways?
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117 views

Linear regresson with four values as input and two values as output

I have the following problem for a personal project of mine: I am solving a system of two differential equations that has 4 changing parameters. The output are two vectors of numbers. Let's say I am ...
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1answer
128 views

What is MCRMSE (mean columnwise root mean squared error)?

The MCRMSE evaluation metric was used in the Kaggle Competitions Africa Soil Property Prediction Challenge(6 years ago) and OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction(On-going) ...
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1answer
26 views

Are the outcome and predictor variables in a logistic/linear regression interchangeable?

Consider the following example. I am studying the mutation burden across three subtypes of cancer. In my dataset, I have individuals without cancer (controls) and individuals with cancer (cases); the ...
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1answer
2k views

Multivariate ARIMA with regression

I have a dataset covering daily data for 3 years (3x365 rows) for multiple attributes TotalPhoneCall (main attribute that I want to predict), Christmas day, weekend, weekday, Easter, 4th_july, etc.(...
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1answer
165 views

Adjustment for multiple comparison in bayesian multivariate regression model (using brms)

I am investigating age and timepoint effects on different (correlated) EEG parameters in a repeated measurements structure. I chose to use the brms R package to fit a bayesian multivariate model with ...
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18 views

Wald test / seemingly unrelated regression on models with clustered standard errors in R

I am trying to conduct a Wald test (aka seemingly unrelated regression) on multivariate models with clustered standard errors in R. This is easy to do in Stata, but I cannot figure out how to do it in ...
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2answers
68 views

Multivariate multiple regression in R with mice [closed]

I have imputed a data set with mice and want to run a multivariate multiple linear regression on the imputed data. Below is a description of what I have done. ...
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Multivariate Weighted Linear Regression

Very simple. I am looking for a package that does Multivariate Linear Regression with weights on the observations. Does anyone know of a package that does this? I am shocked that I have not been ...
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2answers
314 views

How to model an “order-invariant” function by neural networks

I want to approximate a multi-variable function $f(x_1,x_2,x_3,x_4,x_5,y)$ from data by neural networks, and $f$ satisfies $f(x_1,\ldots,x_5,y)=f(x_{i_1},\ldots,x_{i_5},y)$, where $(i_1,\ldots,i_5)$ ...
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SEs in multivariate regression

To avoid confusion, my question refers to multivariate regression as multiple dependent variables for the same set of independent variables. As far as I understand (see e.g., this question), the ...
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1answer
73 views

Model selection for multivariate mixed models

I would like to perform a multivariate mixed model but am a bit confused about model selection for such models. I wonder if I could get some help here. When fitting a univariate mixed model, to avoid ...
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4answers
18k views

Neural network for multiple output regression

I have a dataset containing 34 input columns and 8 output columns. One way to solve the problem is to take the 34 inputs and build individual regression model for each output column. I am wondering if ...
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25 views

Multivariate Linear Regressions with heterogeneous covariance matrices

Let $\mathbf Y$ be $N\times K$ response matrix and $\mathbf X$ be $N\times (p+1)$ design matrix (including the intercept), consider the multivariate linear regression, $$ \mathbf Y = \mathbf{XB+E}\,, $...
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How to compute sobol indices from a multi-variate regression model?

I have a dataset that consists of 300 input variables, 200 response variables, and 15000 samples. These response variables are basically a profile of 200 different values of the same response and they ...
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1answer
40 views

Corrections for logistic regression (multiple comparisons?)

I'll cut to the chase. Here's what I'm doing: looking at the influence of water temperature and salinity on the presence of water-borne parasites for a certain estuarine bird population birds are ...
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1answer
20 views

p-value of multivariate response in partykit::ctree

I wonder if anyone can help me to understand the two questions regarding partykit::ctree: what's the difference between "quadratic" and "maximum&...
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26 views

Test for comparing corresponding coefficients in multivariate regression

This question is related to this but I am hoping for more tangible techniques than a general discussion. For simplicity, suppose I am trying to linearly model two correlated responses $Y_1$ and $Y_2$ ...
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mcglm matrix linear predictor - Error in base::tcrossprod(x, y) : non-conformable arguments

I'm trying to fit a multivariate multiple regression model using the mcglm package (version 0.6.0) and have faced problems when specifying components of the matrix ...
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103 views

Including both binary and Gaussian responses in one multivariate mixed model in MCMCglmm

I would like to fit a multivariate mixed model to a dataset that contains: a binary Direction variable showing the direction the individuals moved toward, a continuous PropRangeChange_s variable ...
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39 views

RStan v.s. simple lm for multivariate regression [closed]

I want to fit a multivariate linear regression, with $Y_1, \dots,Y_4$ as the response and $X_1,\dots,X_n$ as the explanatory variables. $X_1$ and $X_2$ are two components of a mixture, and $X_3,\dots ,...
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1answer
329 views

Which statistical test to use for multiple IV's and DV's

I am conducting a study over post-concussive symptoms. I have two groups, a non-concussed group and a group who has been concussed. I will have three IV's (depression, anxiety, and neuroticism) and ...
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1answer
50 views

When will lasso and regression tree perform differently?

If I want to use lasso and regression tree respectively to generate the important variable lists. I'm wondering when will they generate lists with a huge difference? Under what signal structure?
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68k views

Multivariate multiple regression in R

I have 2 dependent variables (DVs) each of whose score may be influenced by the set of 7 independent variables (IVs). DVs are continuous, while the set of IVs consists of a mix of continuous and ...
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2answers
155 views

How do I make sense of 200 regression models?

I'm dealing with a dataset that has about 300 input features, and about 200 response variables and consists of 25000 samples. These response variables are basically a profile of 200 different values ...
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1answer
118 views

Setting priors for bivariate regression

I would like to perform a bivariate MCMC regression with boldness scores as the continuous response variable, aggression ranks as the ordinal response variable, trial numbers as fixed effect and ...
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1answer
55 views

How does SPSS calculate composite variable for MANOVA/Multivariate multiple regression?

I have analysed my data using multivariate multiple regression (8 IVs, 3 DVs), and significant composite results have been found. Before reporting my findings, I want to discuss in my results chapter ...
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Difference between several univariate regressions and one multivariate regression in a machine learning context

I know there are several other questions asking for the advantages of multivariate regression over several univariate regressions (e.g. this). I understand that the dependent variables can be ...
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41 views

Multivariate regression with a mix of ordinal and continuous dependent variables [closed]

I would like to run a multivariate mixed regression MCMC model with two response (independent) variables, namely x and y. x is continuous while y is ordinal. There is one predictor variable that is ...
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1answer
29 views

Is multivariate regression the right test?

Data Amount spent on product type A Amount spent on product type B 5 age groups External factor X External factor y I have data in the form of monthly sums over 60 months for all of those data-...
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1answer
194 views

Confidence interval for covariance in multivariate linear regression

Let $X\in R^{n\times q}$ and $Y\in R^{n\times p}$ be our data matrices, and we assume that they are related by a linear model $Y = BX + \Xi$, where $B$ is fixed and unknown and each row $\Xi_i \sim \...
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How to calculate the ratio of observations to model parameters in a VAR(p) model

In machine learning, it's pretty common to use the general rule of thumb that one should have at least 10X as many observations as there are model parameters to avoid overfitting. As discussed here ...
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Liklihood ratio test and linear mixed effects regression

I have a data set which includes sex, age, and 5 polygenic scores as independent variables, with 16 dependent variables. I have constructed univariate linear mixed effects regression models and ...
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Comparing two approaches to modeling dependence in bi-variate Gaussian regression

Presume we would like to model the dynamics of two related variables as a bi-variate Normal, while also accounting for the effect of other covariates (via regression). E.g. I would like to model ...
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1answer
75 views

Forecasting a time series $(x_t,{\bf Y_t})$ where all we care about is forecasting $x_t$

Consider a multivariate time series $(x_t,{\bf Y}_t)$ $1\le t \le n$ taking values in $\mathbb{R}^{d+1}$, and suppose that we wish to forecast $x_t$ using its own path as well as the "exogenous" ...
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1answer
31 views

Best way to find correlation between categorical response variables and continuous explanatory variables

Sorry if this is a really simple question, but I'm very new to multivariable statistics and I'm trying to find a best method to deal with my ecological dataset. I recorded the environmental ...
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How can the cost function dimensionality be understood?

Most tutorials on the internet demonstrate the cost function with the m * x + b = y function, which is fine and understandable for a start.. They presents the ...
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How to interpret the hinge function for multivariate adaptive regression splines?

I do not understand why we need a reflected pair (t-x)+ for MARS. Could you please interpret more about the hinge function?
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Does multivariate regression produce the same results as multiple, single regressions? [duplicate]

I've read a variety of answers on this topic and as far as I can see, the consensus is that multivariate regression is different from multiple, individual linear regressions. I also understand that in ...
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in r, comparing two regression models with same number of independent variables for significant difference in prediction

I have two models that have the same dependent variable. Each model has 3 independent variables and differ on 1 of those variables (a continuous variable). I am not trying to select for which model ...
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31 views

What does multivariate regression get us that several univariate regressions do not? [duplicate]

("Multivariate" regression in this post means a multidimensional response variable.) I have been playing with multivariate regression for the past two days, and I have noticed something that did not ...
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Geometric interpretation of the difference between the means (ANOVA)

First of all, a disclosure: I'm a medical doctor trying to understand statistics for research. Coming from a non-mathematical background I can do many mistakes. I've read some traditional books of ...
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56 views

How do we know adding an uncorrelated variable to a regression will not change existing coefficients?

Say I have a regression with 3 independent variables and I decide to introduce a 4th variable and rerun the regression. A previous post states that the coefficient on an original variable will change ...
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24 views

Is single-output regression the same as univariate?

Similarly, is multi-output regression the same as multivariate regression?
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628 views

Multiresponse Poisson regression in R

Can I apply generalised linear regression to a multiresponse setting? I mean the regression $Y = \beta X + \epsilon$ where $\beta$ is a parameter matrix and $Y$ is a response vector, in my case ...
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0answers
21 views

Whither multivariate regression?

I've been handed someone else's data (again). It's measurements of six proteins' levels from 20 human brain samples, 5 each of different stages of a disorder. I'd like to run a multivariate regression,...
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1answer
348 views

Variance of linear combinations of Normal RVs in linear regression

I am working with a standard multivariate linear regression model ($Y = X \lambda + \epsilon$, $Y$ and $\epsilon$ of length $n$, $X$ is $n$ x $m$, $\lambda$ of length $m$). $X$ is said to be "column-...
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1answer
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What to do with statistically insignificant dummy/categorical variables? [duplicate]

From the research I've done the common answer is that you can not remove insignificant dummy variables from a regression. I'm having a hard time finding academic papers or books that back up this ...

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