A matrix (plural matrices) is a rectangular array of numbers, symbols, or expressions arranged in rows and columns. The individual items in a matrix are called its elements or entries.

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Extremely new to R: Support.CEs package - help with design matrix please!

I've only started using R one day ago, still so much to get my head around. I need to create a choice experiment, and I have been following the example of H.Aizaki I think I have created a successful ...
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14 views

Mantel Test data assumptions

Does the Mantel Test works with non-normal distributed samples? I couldn't find anything clear enough about it.
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52 views

Design of matrix of contrasts in R

I am doing some post-hoc comparisons (in lme4, but here I'll just present a simple linear model), and I am having a hard time making sure that I am building the right matrix of contrasts to test ...
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Inverse covariance matrix, off-diagonal entries

Let $\Sigma$ be a covariance matrix. According to the material in this link, If the elements of $\Sigma$ are all positive, most of the off-diagonal elements in $\Sigma^{-1}$ will be negative ...
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14 views

(R) All data going into 1st row of new column of data frame [migrated]

I've got a data frame, 1447 obs of 165 variables, and am looking to add columns by processing existing ones. I've got: ...
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10 views

Completing a matrix whose entries depend on time

Imagine you have a matrix, where each row represents an individual and each column represents a specific time interval. The entries of the matrix represent events occurring for a given person at a ...
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19 views

Order of Matrix Operations in Mahalanobis Calculations

I'm teaching myself to translate equations to code after many years of letting my math skills atrophy, and am trying to do it on my own as much as possible. I've run into a couple of difficult ...
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63 views

Restriction matrix for a VAR

In New Introduction to Multiple Time Series Analysis by Luetkepohl (2005), section 5.2.1, it says that one can specify linear restraints for a VAR, $Y = \beta X + U$, in the form $$ ...
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362 views

Does every semi-positive definite matrix correspond to a covariance matrix?

It is well-known that a covariance matrix must be semi-positive definite, however, is the converse true? That is, does every semi-positive definite matrix correspond to a covariance matrix?
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9 views

How to access all the columns of a matrix one by one for normalizing in R [migrated]

I have a matrix which looks like this: Col1| Col2| Col3 | Col4 | Col4 | .... | | | | | .... | | | | | .... and I ...
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37 views

What does Determinant of Covariance Matrix give?

I am representing my 3d data in covariance matrix. I just want to know what the determinant of a covariance matrix gives. If the determinant is positive, zero, negative, high positive, high negative, ...
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9 views

How to calculate the correlation between a variable and a matrix of variables

I'm trying to improve a factory quality control. I have some variables from the melting process (something like ten control variables) that changes trough time (a matrix of the values of those ...
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13 views

How to calculate the correlation between a variable and a matrix of variables

I'm trying to improve a factory quality control. I have some variables from the melting process (something like ten control variables) that changes trough time (a matrix of the values of those ...
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23 views

Rank problem in inversion of t(A) %*% A in R [migrated]

I need to get the inverse of the cross-product $(\mathbf{A}' \mathbf{A})$, and I run into numerical problems that don't make any sense to me. I actually need $(\mathbf{A}' \mathbf{W}^{-1} ...
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2answers
40 views

Finding independent “clusters” in a matrix

I've called my question "clustering" but I am not sure if that's the right term. Imagine my matrix looks like this: ...
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7 views

Calculation the Expectation of an Inverse Wishart matrix

I have $\boldsymbol{A} = \boldsymbol{G}^H \boldsymbol{G}$ is a Wishart matrix, i.e, $\boldsymbol{G}^H \boldsymbol{G} \sim \mathcal{W}_K (M, \boldsymbol{\Lambda})$ with $\boldsymbol{\Lambda} = ...
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22 views

Is it a Wishart matrix?

We know that an $m \times m$ random matrix $\boldsymbol{A} = \boldsymbol{H} \boldsymbol{H}^H$ is a (central) real/complex Wishart matrix with $n$ degrees of freedom and covariance matrix ...
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34 views

Different results for Singular Value Decomposition (SVD) using different tools

I am currently implementing Latent Semantic Analysis in Java using the EJML library for the preliminary Singular Value Decomposition (SVD). I am testing my code against the original term frequency ...
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40 views

Problem constructing model matrix to generate predicted values to plot Bayesian glmmBUGS output

My goal is to plot the predicted values generated from a Bayesian model using glmmBUGS run through R. I believe my problem stems from a lack of understanding in 1) how to properly construct a model ...
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17 views

Effective ways to display confusion matrices from different predictors in an academic publication?

I want to display the results of two different predictors' performance on a dataset. I have a confusion matrix for each of the predictors' results on the test cases. I want to present these confusion ...
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38 views

Matrix completion approaches for healthcare big data

I am working on a prediction problem that leverage sparse clinical datasets. Missing data rate is in the range of 80%. 1- I am wondering if there is any example of application of matrix completion ...
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23 views

Cholesky decomposition and confidence ellipsoid

I'm trying to construct an error ellipsoid from a covariance matrix (which exists for a 3D point) and then sample consistent xyz points in this region. (This question succeeds this one.) What I'm ...
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39 views

Is the information matrix equality valid for the Poisson distribution?

As far as I know it should, since the support of the Poisson is independent of its lambda parameter. The negative of the expected Hessian equals $\frac{n}{\hat{\lambda}}$, where $\hat{\lambda}$ is ...
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37 views

Build corpus with phrases

I have my documents as: doc1 = beautifull, very good, very bad, you are great doc2 = very bad, good restaurent, nice place to visit I want to make my corpus ...
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68 views

Difference in values of tf-idf matrix using scikit-learn and hand calculation

I am playing with scikit-learn to find the tf-idf values. I have a set of documents like: ...
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55 views

Difference in tf-idf values in R

I am playing around in R to find the tf-idf values. I have a set of documents like: ...
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144 views

Generating a correlated data matrix where both observations and variables are correlated

I am trying to generate a simulated data matrix that is correlated by both observation and variable directions. So far I know how to do this for variable x variable. ...
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Matrix rank and amount of regressors

A question states: $X$ is the vector of regressors stacked for 30 observations and $Rank(X)=5$. There are no lags of $y_t$ in the set $X_t$. Using the Durbin-Watson statistic, test the null hypothesis ...
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39 views

Which contingency method to use with a 3x3 table yet still account for expected or discrete choice?

Im not sure what test to conduct when I have 3x3 matrix of data and still account for availability. So, in a simple chi square you have an observed observation and then you are often able to ...
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78 views

LDA - Why differents formulas to calculate covariance and pooled covariance matrix

Reading materials from differents sites some questions have risen about covariance and the pooled covariance matrix calculation to implement LDA: Definitions Ci - covariance matrix of group i (C1 and ...
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57 views

Can you use the chi-squared test with tables of real numbers?

Consider a $N \times 2$ matrix (MATLAB notation): M = [1.2, 3; 1.4, 2; 1.8, 1; 2.0, 2]; That is a $4 \times 2$ matrix where the first column ...
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39 views

can matrix completion work in the presence of many missing values?

I have a matrix with about 550k elements (2500 x 220) with 100k values known and the rest are unknown. Would it make sense to use matrix completion in this case, or are there too many values which ...
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40 views

Is there a way to perform SVD in a sequential manner?

My neurology experiment has a spike detector outputting 40 sample long spike waveforms. I'm using a dictionary method for sorting the spikes in real time. To ...
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28 views

OLS Coefficient estimator; Transformation from Matrix to sum of matrices form

I do not understand why the following equality holds (taken from Cameron & Trivedi 2005: Microeconomtrics): ...
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120 views

Removing structure with a known functional form from the covariance matrix

I have a set of timeseries data $X^B = \begin{bmatrix} {X^B_1},{X^B_2},\dots, X^B_n \end{bmatrix}$ consisting of observations recorded at different spatial locations. There is crosstalk between the ...
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80 views

PCA Using prcomp in R

I'm trying to do principal component analysis (PCA) in R using the prcomp function. My input is a large matrix of 1,188 observations (rows) and 15,462 features (cols). I input this to the function ...
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63 views

Item-based collaborative filtering – Can you add demographic information to initial user×item matrix?

I am building an item-based collaborative filter recommendation system. I have a matrix of users and items, which in this case, are products that were either bought or not (i.e., binary: ...
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143 views

Why is the Pearson correlation 1 when only two data values are available?

I am trying to obtain a Pearson correlation between 6 different variables (represented by columns in the matrix below) with two datapoints each (rows). This is the matrix: ...
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42 views

JAGS array indexing

I have a relatively simple multivariate response problem that seems to causing me problems with array indexing. I've scraped/rewworked the model program down to the bare essentials and hopefully ...
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21 views

Derivation of a fixed effects estimator

I've come across parts of a derivation of a fixed effects estimator in a paper i don't understand. The log likelihood function is where $Y_i=(Y_{i1},...,Y_{iT})'$, and $X_i$ is a $T\times k$ ...
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24 views

Iterative solving of ML estimators

I have derived this likelihood function \begin{equation} \begin{split} &-\frac{1}{N}\log L(\eta,\beta,\mathit{\Omega})\\ &=\frac{1}{2}\log ...
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61 views

Understanding the derivation of a ML-estimator

I have the following likelihood function: I'm given this information about the $\Omega$ matrix ($\boldsymbol{1}$ is a $T \times 1$ vector of ones): I would like to be able to show that the ...
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35 views

Convergence of a matrix product

Let $A=o_{a.s.}(1)$; $A:k\times k$ matrix and $Vu=O_p(1)$; $V:k\times k$; $u: k\times 1$. Specifically, $Vu$ converges in distribution to $\mathcal N(0,I_k)$. Can we show that $VAu=o_p(1)$ or ...
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9 views

Too Large Values with Assembled Distance Transform

I am trying to apply ADT as a distance between two images or their DCT coefficents. The ADT is given as (image taken from "Bidirectional PCA with assembled matrix distance metric for image recognition ...
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61 views

Linearly dependent features

I have a matrix A of 1000 observations (rows) and 100 features (cols). I would like to find: Linearly dependent features so that I can remove them and simplify the problem. rank(A) gives me 88, ...
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Question on Matrix Operation Notation

I have confused myself in attempt to express a 2-step operation in matrix notation-- The data consists of 2 "chunks" of information--quantity of identical items sold in different stores and their ...
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36 views

Condition number of covariance matrix

I am interested in generating a covariance matrix of dimension say 100. I managed to get a correlation matrix with finite condition number. To construct a covariance matrix I need to have standard ...
2
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1answer
234 views

Plotting error ellipsoid from 3x3 covariance matrix in R?

I'm hoping to be able to take a 3x3 covariance matrix and turn this into an error ellipsoid but so far I haven't been able to achieve this. I'm very new to R (in fact turned to it to attempt to solve ...
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92 views

Normalize row or column while each row is an observation

Suppose I have a matrix compose of row as each observation, column as each property and I want to calculate the distance between each observation. In this case I think I should normalize each column, ...