Questions tagged [data-transformation]

Mathematical re-expression, often nonlinear, of data values. Data are often transformed either to meet the assumptions of a statistical model or to make the results of an analysis more interpretable.

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Interpretation difference between log link and log transformation

I have a question about the interpretation difference between log link of GLM and log transformation of LM. I know that log transformation is for target variable but log link is for mean .But related ...
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Using R auto.arima and arima.sim for stock prices

I simulate around 16000 stock prices with using auto.arima and arima.sim and I have two questions. Do I need to use plain ...
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Log transform all DVs for multiple regression?

Hello brainier folk than me, I'm looking to perform a multiple hierarchal regression and considering log transforming at least one IV which is the main one of interest to improve linearity. Should I ...
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Mixed model assumptions of linearity

I want to use a mixed model to analyse the association of distance covered per minute (TD_min) with ball possession (phase), playing position (role). Data have been recorded for the same subjects ...
118 views

Regression in high dimensions ($p>n$) with nonnegative outcome

I need to perform regression in a setting where my outcome is positive ($Y > 0$) and there are more variables than rows ($p > n$). The goal of the analysis is to obtain predictions, which, of ...
113 views

Ordered quantile normalization properties explained

My question is about the properties and proper use of "Ordered quantile normalization" from the paper titled “Ordered quantile normalization: a semiparametric transformation built for the ...
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modeling an outcome that has a range from [-0.158 ; 1 ]

I am trying to model a quality of life score that has a range [-0,158 ; 1], where values equal to one indicate the patient is fully healthy, values equal to 0 indicate the patient is dead, values ...
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Box-Cox, Exogenous Variables and Time Series Models

I am building time series models using SARIMAX from Statsmodels (Python). The independent variables in my models include 3 to 5 exogenous variables that are other than the target variable I am trying ...
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Creating ordinal variable based on percentages

I have a database with the results of a test applied to students. Each item had 4 to 5 answer options, only one of which was correct. I also have another dataset with the percentages of response to ...
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Data Leakage Concerns

I've come across the concept of data leakage in which optimistically biased generalisation errors occur due to test data in some sense 'seeing' the training data. For instance, normalisation on an ...
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Hellinger or log transformation for community data?

I have a dataset of abundances of phytoplankton species from many years and two locations and I want to study the communities and look for any differences. The abundances are, of course, very ...
127 views

Negative binomial assumptions and errors

I'm conducting a negative binomial regression in SPSS using the GLM menu and I'm receiving the following error message: Warnings: The Hessian matrix is singular. Some convergence criteria are not ...
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Is (covariance) stationarity preserved under log or exponential transformation?

In this lecture note, it (proposition 2) says that strict stationarity is preserved under transformation. However, it doesn't give the proof of this statement. Second, what if the process is ...
218 views

What are the limits of inverse hyperbolic sine for regression?

I know that if your data contains zeros, log transforming your variable can be problematic, and all the zeros become missing. It is often suggested to use the inverse hyperbolic sine transform, rather ...
26 views

Would a log transformation on my features change the elastic net result

We built the elastic net model on a set of my features and control features. With that, we did various experiments to discuss the importance of the selected features. For example, we showed more of my ...
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How to interpret a transformed linear regression model

I'm playing with the trafo R-package and this small data. After using the assumptions function, I found the log shift opt transformation is the best for normalizing ...