Centering involves subtracting the overall sample mean score from the original score; standardizing does the same followed by dividing by the overall sample standard deviation.

learn more… | top users | synonyms

3
votes
0answers
35 views

R: Quadratic Regression with interaction: when to center?

I have a statistical question. I have data from an experiment with two conditions (dichotomous IV: 'condition'). I also want to make use of another IV which is metric ('hh'). My DV is also metric ...
3
votes
1answer
32 views

Intuition for near-decorrelation through centering

Consider a $p\times 1$ random vector $\mathbf u = (u_1,...,u_p)'$ with zero mean vector and variance-covariance matrix $$E(\mathbf u \mathbf u')\equiv ...
1
vote
0answers
19 views

Centering makes model insignificant

To determine whether multiple independent variables predict a dependent variable, I'm using a hierarchical model of three levels (first level: only control variables, second: control and independent ...
1
vote
0answers
35 views

Centering and scaling skewed distributions

I have a dataset where the features are skewed (non normal) distributions. My preprocessing pipeline consists of the following steps: Missing values imputation Centering and scaling (zero mean and ...
1
vote
2answers
76 views

Why standardization of the testing set has to be performed with the mean and sd of the training set?

In pre-processing the data set before applying a machine learning algorithm the data can be centered by subtracting the mean of the variable, and scaled by dividing by the standard deviation. This is ...
0
votes
1answer
17 views

How do you grand mean center continuous time-varying covariates?

I have a 2 level linear mixed effects model with Time nested within University. Time is treated as continuous and I am modeling this within SPSS. The outcome variable is the number of undergraduate ...
0
votes
1answer
47 views

When I center data, should I take the absolute value or keep the sign?

When I center data, is it the absolute value that you use or do you keep the sign? For example: you have data (4,5,6) and the mean is 5. After centering, is the data (-1,0,1) or (1,0,1)?
0
votes
1answer
15 views

remove the mean over multiple measurements

I have a set of multiple measurements for each subject (i.e. each subject is assessed several days). For each set of measurements (several days of the same subject) I am calculating the mean value of ...
0
votes
0answers
43 views

Pre-processing (center + scale, box-cox transformation) inside cross-validation?

I have extracted features and I have now a matrix where the rows are the data points and the columns are the features. Of course, I have to center and scale (zero mean and unit variance) each feature ...
1
vote
0answers
36 views

Ridge Regression Centering Proof [duplicate]

This is a ridge regression problem. The following two problems are equivalent: $(w_t, b_\lambda ) = argmin_{w,b}\{\sum_{i=1}^m (y_i-b-w^Tx_i)^2+\lambda w^Tw\} $ $(w_t, b_\lambda )= ...
3
votes
1answer
121 views

How does centering make a difference in PCA (for SVD and eigen decomposition)?

What difference does centering (or de-meaning) your data make for PCA? I've heard that it makes the maths easier or that it prevents the first PC from being dominated by the variables' means, but I ...
0
votes
0answers
32 views

Centering in the presence of interactions with categorical predictors?

I understand that always scaling covariates prior to regression analysis is controversial advice. See for example Andrew Gelman's blogpost and comments, or many crossvalidated questions such as this ...
2
votes
1answer
161 views

Why is using centered or uncentered data equivalent in ridge regression?

Why is using centered or uncentered data equivalent in ridge regression? In other words, given two ridge regression problems: \begin{equation} (b',c')=\operatorname*{argmin}_{b,c}\Big[ { \sum_i^{m} ...
1
vote
1answer
42 views

Centering variables in regression leads to the same model of original variables, why still doing that?

The regression model y= b0+ b1 x + b2 x^2 + b3 x^3 and the second regression model y = b0 +b1 (x-u) + b2 (x-u)^2 + b3 (x-u)^3 where u is the mean of x These two models lead to the same curves, or ...
0
votes
0answers
19 views

Should I use mean-centering on predictors for collinearity involving the intercept?

In one of my linear regression models, a predictor showed collinearity with the intercept based on condition indexes and variance decomposition proportions diagnosis. Then I found that this predictor ...
1
vote
0answers
44 views

Motivation to center continuous predictor in multiple regression for sake of multicollinearity?

I'd like to discuss the centering of continuous predictor variables in multiple linear regression with an interaction term for the sake of "relieving" multicollinearity. I've read about ...
3
votes
0answers
49 views

Centering for the regression. How to do it properly?

I have read a paper described an analysis of using support vector regression. In the paper it mentioned: It is worth mentioning that, in our implementation, we subtract the mean value of the ...
2
votes
1answer
184 views

Regression: centered vs. uncentered predictors [duplicate]

I'm trying to understand how/why centering predictors in a 2 predictor regression model would change the coefficients Lets say I have 2 centered predictors and an interaction term, predicting a ...
0
votes
0answers
90 views

Mean and residual centering, orthogonal polynomials and effect coding

I understand that in order to be able to interpret main effects / lower order terms in a more intuitive way in linear regression models with interaction effects one should either use mean or residual ...
2
votes
0answers
72 views

centering constraints on ti() terms in MGCV

I have some raw data on which I compute percent changes, and I calculate rolling averages of two different lengths on the percent changes. I want to use a tensor interaction of the two rolling ...
0
votes
1answer
326 views

centering and scaling dummy variables

I have a data set that contains both categorical variables and continuous variables. I was advised to transform the categorical variables as binary variables for each level (ie, A_level1:{0,1}, ...
0
votes
0answers
74 views

LDA scores too big

I'm trying to do dimensionality reduction with linear discriminant analysis (LDA) in MATLAB. I'm using this code to calculate the coefficients. But I'm confused whether (and when) should I center the ...
1
vote
0answers
75 views

Principal Component of non-centered data and PCA-Transformation

I am reading a chapter about principal component analysis (PCA). It states that for any random varible $X \in \mathbb{R}^p$ with $n$ observations, $E[X] = \mu$ and $Cov[X] = \Sigma$ the i-th PC is ...
0
votes
1answer
31 views

Clarification on Prediction with a Regression Model using Centered Variables

As I understand it, for a regression model, centering the variables around their means can be helpful since it makes the intercept term the expected value of $Y_i$ when the predictor variables are set ...
1
vote
0answers
110 views

Centering variables in R [closed]

Do centered variables have to stay in matrix form when using them in a regression equation? I have centered a few variables using the scale function with ...
3
votes
0answers
739 views

Standardized VS centered variables

I have found many useful posts about standardized independent variables and centered independent variables on stats.exchange.com, but I am still a bit confused. I am asking you an evaluation of what ...
2
votes
0answers
115 views

How do statistics packages (e.g., fitglm in MATLAB, SPSS/SAS/Stata) handle mean centering for higher order terms? [closed]

Many questions (e.g., Centering in linear regression & How to include a linear and quadratic term when also including interaction with those variables? ) have been asked about mean centering (aka ...
1
vote
3answers
352 views

Mean centering - before regression or observations that enter regression?

I am using Stata 13 to estimate a simple model with interaction terms. To give the coefficients a meaningful interpretation at zero, and to avoid multicollinearity, I am mean centering variables. I ...
0
votes
0answers
46 views

Multicollinearity Diagnostics of Mean-Centered Interaction Terms

Interaction terms in moderated regressions exhibit high multicollinearity due to the high correlations with their main effects in case of uncentered data; for Normal distributed variables this is not ...
1
vote
0answers
45 views

Is there moderator or not? Centering and unsignificant b

I am trying to check if the relationship between X and Y is a moderated by M. Here is my model: Y = b0 + b1*X + b2*M + b3*X^2 + b4*X*M + b5*X^2*M where X - independable variable, M - moderator, Y - ...
4
votes
1answer
182 views

How should I implement this interaction between a continuous and categorical predictor?

I have a continuous outcome variable. I understand that if I have a binary predictor, and a continuous predictor, and an interaction, then the model looks like this: $y_{i} = \beta_{0} + ...
0
votes
1answer
75 views

Centering in linear regression

I am trying to fit a quadratic to my model, I have tuples (x,y). The choices are, 1) lm(y~x+I(x^2)) 2) ...
0
votes
0answers
229 views

How does one interpret a demeaned log interaction term?

I am having problems interpreting my regression equation. I want to know the effect of an increase in variable $x$ on $y$ for different values of $z$, but as it's in logs and the interaction term is ...
2
votes
1answer
114 views

Can $x'x$ be written as correlation matrix?

$x'x=$ $$ \begin{bmatrix} \sum_{i=1}^{n}(X_{1i}-\bar X_1)^2&\sum_{i=1}^{n}(X_{1i}-\bar X_1)(X_{2i}-\bar X_1)\cdots & \sum_{i=1}^{n}(X_{1i}-\bar X_1)(X_{ki}-\bar X_k) \\ ...
6
votes
2answers
205 views

How is the first column of the matrix orthogonal to all the others

$$ \mathbf{X}_{n\times(r+1)} = \begin{bmatrix} 1 & (x_{11}-\bar x_1) &\cdots & (x_{1r}-\bar x_r) \\ 1 &(x_{21}-\bar x_1) &\cdots & (x_{2r}-\bar x_r) \\ ...
2
votes
0answers
135 views

Group mean centering predictors for crossed random effects

I'm fitting a mixed-effects model, in which I wish to test the effect of $X$ on $Y$, with crossed random intercepts and slopes for each subjects $S%$, and for each level of an additional grouping ...
4
votes
1answer
435 views

Zero-centering the testing set after PCA on the training set

I have a training set of data on which I do principal components analysis (PCA) and save the loadings/eigenvectors/coefficient matrix. I want to use the eigenvectors to transform my testing data into ...
1
vote
0answers
71 views

Does centering or mean normalizaiton alone every help in feature scaling?

In feature scaling, one way is to subtract the mean (centering) and then divide by the standard deviation for all data points. Suppose we just centered the data and didn't divide by the standard ...
0
votes
1answer
115 views

Should I center time variant predictors in repeated measures multilevel models?

I have a multilevel model built coinsidering repeated measures on students. Students performance may vary depending on study hours and tutoring hours before each exam. Should I center the predictors ...
1
vote
1answer
170 views

Imputation of missing data before or after centering and scaling?

I want to impute missing values of a dataset for machine learning (knn imputation). Is it better to scale and center the data before the imputation or afterwards? Since the scaling and centering ...
4
votes
2answers
921 views

The order of Data Centering and Data Transformation

Edit: I just read a related post (How to include $x$ and $x^2$ into regression, and whether to center them?) which mentions that centering a variable creates a new variable. However, as the comments ...
2
votes
2answers
306 views

Between-subjects effect becomes non-signicant after centering covariate. Should I center or not?

I've used a general linear model function to run an ANCOVA, involving a categorical predictor (with two levels), a continuous predictor, and their interaction effect. If I don't center the ...
0
votes
0answers
17 views

When should one centre variables in a cox proportional hazard model and when not? [duplicate]

I want to use a Cox proportional hazard model in order to analyze the prognostic value of a certain variable (I divided it in quartiles and so I have 4 categories) with respect to the time to event ...
0
votes
0answers
34 views

Is scaling data valid for Kruskal-Wallis test?

I have a dataset of fresh matter (FM) weights of salad measured in field over time (6 Dates, 40 Plants each). Those are measured independently, so at every date different plants were measured. As in ...
2
votes
1answer
54 views

Which mean to use for centering variables when sample definition varies

I am centering variables that enter interaction terms in my linear regression. To check the robustness of my results, I exclude certain cases from the original sample, and re-run the regression ...
9
votes
3answers
368 views

How to include $x$ and $x^2$ into regression, and whether to center them?

I want to include the term $x$ and its square $x^2$ (predictor variables) into a regression because I assume that low values of $x$ have a positive effect on the dependent variable and high values ...
0
votes
1answer
221 views

Do you ever center AND standardize variables in multiple regression?

Do you ever center AND standardize variables in multiple regression? It seems as if standardization would automatically center variables...is this true?
3
votes
3answers
3k views

Is centering a valid solution for multicollinearity?

Let's assume that $y = a + a_1x_1 + a_2x_2 + a_3x_3 + e$ where $x_1$ and $x_2$ both are indexes both range from $0-10$ where $0$ is the minimum and $10$ is the maximum. I found by applying VIF, CI and ...
0
votes
0answers
43 views

Centering variables before running PCA [duplicate]

I am learning about PCA, regarding PCA I need to know that given a dataset is it always necessary to use centering? what if I don't center the variables used in PCA?
0
votes
0answers
1k views

How does knnimpute of the preprocess function work?

I am new to R and I use a script I do not completely understand. It preprocesses a dataset for data mining. At one point, the data (stored in fil) should be ...