Skip to main content

Questions tagged [back-transformation]

Back transformation refers to efforts to reverse the effects of a transformation of one or more variables. Usually, the transformation has been done to meet assumptions of a model, but interest is in the original variable(s).

Filter by
Sorted by
Tagged with
3 votes
1 answer
44 views

What should I back transform beta coefficients when my dependent variable is fractionally exponentiated in R

I have this mixed effects regression model. To create a normal distribution in continuous scale dependent variable, I fractionally exponentiated it: ...
Nim Goldshtrom's user avatar
3 votes
1 answer
186 views

Back Transforming log-log Model for Prediction

I have a model that is log-log and I would like to make raw predictions of $Y$ with it: $\ln(Y) = B_0 + B_1\ln(X)$ All answers and articles I have found concerning back transforming for prediction ...
Oberon Quinn's user avatar
0 votes
0 answers
11 views

Variance decomposition with Tweedie distribution – back transform necessary?

We are running a hierarchical random intercept model with a Tweedie distributed dependent variable (see below) and three levels of hierarchy. Our aim is to estimate how much of the total variance (in ...
james_westfield's user avatar
2 votes
2 answers
71 views

Back Transformation of Predicted Y from Log 10 Transformed Model Data

I am using a General Linear Model for Analysis in Minitab. I have two questions. I had two responses variables which I transformed using Log10 as there was evidence from the residual plots of some ...
GTK's user avatar
  • 31
1 vote
0 answers
29 views

Equivalent GLM's for common stabilising transformations

I'm familiar with applying a log-transform to a skewed outcome variable to improve model fit, but I've not thought further to link stabilising transforms to GLM's in general. Reading around it seems ...
LucaS's user avatar
  • 771
2 votes
1 answer
57 views

Backtransforming a probabilistic forecast?

Let's say that we have a probabilistic forecast for the future percentage return of an asset in the form of a probability density, $\hat{R}_{t+1}$. If our initial goal was to create a probabilistic ...
QMath's user avatar
  • 451
0 votes
0 answers
11 views

How to back transform a folded power transformation?

I work with a dependent variable with values from 0 to 1 and because I have a lot of 0 and 1 in this variable, i'm doing a folded power transformation with the following formular: y = x^0.5 - (1-x)^0....
jbopp's user avatar
  • 1
0 votes
0 answers
22 views

Can principal components changed by a normalization method be used to construct original data shape with SVD

I'm planning to use an algorithm called Harmony, designed for data normalization, particularly in the context of single cell data analysis. Harmony operates by taking principal components (PCs) as ...
MadmanLee's user avatar
  • 133
1 vote
1 answer
75 views

Confidence Intervals around Backtransformed Log-linear Regression

Suppose we are interested in the percentage effect of a binary $D \in \{0,1\}$ on an outcome $Y \in \mathbb{R}$, which might motivate a simple regression of the form: $$ \log Y = \beta_0 + \beta_1 D + ...
Adam's user avatar
  • 396
0 votes
1 answer
34 views

Interpreation - Log tranformed dependant variable and model with square term of predictor (inverted U)

I am estimating a model of the following form: log(y) = b1 x + b2 x^2 + b3 log(z1) + b4 z2 This is an econometric model with a focus on the impact of ...
user917983's user avatar
0 votes
0 answers
15 views

Should I back-transform estimate and 95%CI after a survival analysis with log-normal distribution?

I need to run a survival analysis on my data. Based on the AIC, the lognormal distribution is the most suited one. Can I report the estimates and 95% CI as they are, or do they need back-...
MWE_Manet's user avatar
1 vote
1 answer
277 views

Backtransform variables in ggeffects (logistic regression)

I am running a logistic regression. Two variables are transformed for linearity with the logit of the outcome. ...
SPet's user avatar
  • 33
0 votes
1 answer
126 views

Interpreting linear mixed effect model results with log transformed dependent variable and log transformed predictor w/ normal predictors as well

I have a linear mixed effect model that I built using longitudinal country level data to help me predict TB incidence based on country level diabetes prevalence, HIV incidence, prevalence of ...
TBResearch's user avatar
0 votes
1 answer
27 views

Prediction Interval for back-calculation when observation has variance

Given an existing regression curve, how do I properly account for the known variance of the dependent variable when back-calculating for the (nominally) independent variable? If I had an observation $...
azabell's user avatar
  • 21
0 votes
0 answers
269 views

regression question: backtransforming MAPE for log(y)

I'm fitting a linear regression between two variables and to reduce the problem of heteroskedasticity I have log-transformed the outcome variable y. However, this makes it difficult for me, a non ...
EAAndersson's user avatar
1 vote
0 answers
65 views

Back transformation of dependent variable after square-root transformation in a two-part model in MPlus

I want to find out if one behavioral intervention had also an effect on another behavior (let´s say smoking). Currently, I am testing the moderation with a two-part model in MPlus, where smoking is ...
Filipa's user avatar
  • 11
0 votes
0 answers
181 views

backtransform confidence intervals from a log-transformed linear regression model

I have a linear regression model with violated assumptions (linearity and Constant variance), thus I decided to take the log of the dependent variable and this step solved the problem. Afterwards, the ...
Ram6's user avatar
  • 1
1 vote
1 answer
222 views

Why do I get this error when trying to obtain back-transformed estimated marginal means using emmeans?

I am trying to examine the interaction between temperature (factor: 6 levels) and species (factor: 2 levels) in determining the duration of the first larval stage in insects using the following model ...
Insect_biologist's user avatar
0 votes
1 answer
676 views

Why use the bootstrap for a skewed distribution when you can use a transform?

Let's say you are working with a statistic (say, the mean of the population) of a skewed distribution with a long, long tail such that confidence intervals must be very skewed to achieve reasonable ...
Estimate the estimators's user avatar
0 votes
0 answers
30 views

Can you back-transform standard errors from log-transformed data? [duplicate]

I fit a model with transformed data with log10 transformation. I would like to report the results using the original scale and not the transformed means. I know you can back transform the log means by ...
balzy's user avatar
  • 139
1 vote
1 answer
4k views

What is the inverse normal transformation (INT) and what are the reasons behind using it?

I noticed a statistical method called inverse normal transformation in the following research article FTO genotype is associated with phenotypic variability of body mass index. I attached the ...
7-x's user avatar
  • 11
0 votes
0 answers
367 views

How do you reverse log, square root, and Ln data transformations?

If you have transformed your data, is it true that when reporting results, such as descriptive statistics (e.g. mean, median, range, variance, standard deviation etc.), you need to revert the data ...
NKGND's user avatar
  • 11
2 votes
0 answers
356 views

GLMM with scaled variable: do I need to back-transform?

I am fitting a GLMM to powerline collision data for a bird species- using distance to seasonal water, habitat and the presence/absence of line markers as predictors. Incident is a binary response (50 ...
Christie Craig's user avatar
0 votes
0 answers
110 views

Back Transformed Truncated Negative Binomial Model Results Less Than One

I'm using a truncated negative binomial model to describe my count data where all values are >=1. I have attempted to back-transform my model results using emmeans. However, all of my back ...
user364517's user avatar
1 vote
1 answer
295 views

How to transform prediction std of gaussian process back to origin

I am looking for a way of rescaling the predictions of my Gaussian Process Model back to the original scale. The data is scaled for training using a ...
sensation96's user avatar
1 vote
0 answers
120 views

Interpreting regression output estimates with normal and square root transformed predictors and some log transformed response variables

I understand this topic is well covered here, but having read several threads, I can't find an explicit or clear answer to my question. I looked at the following threads and can't glean an answer from ...
user197410's user avatar
1 vote
1 answer
107 views

GLMM Outputs from LMER: Random Effect Variance does not reside within 95% CIs in log-transformed response variables

I have produced a mixed-effects model (lmer) that is analysing the difference in area between three protocols of growing mini-brains. I have takes into account various fixed effects including the type ...
Sharna Lunn's user avatar
2 votes
0 answers
124 views

Interpreting Predictions from the Log-Linear Model (or Log-Log Model...)

I understand that when we fit an OLS regression to the log(y) (as either a log-linear or log-log model), the predicted value from that model [log(y).hat] cannot be simply exponentiated to solve for y....
Dan's user avatar
  • 85
1 vote
1 answer
1k views

R: emmeans back-transformation when using a constant value in the response formula

I am fitting a linear mixed model ...
learners's user avatar
  • 579
0 votes
1 answer
134 views

Converting scaled parameters to unscaled parameters in exponential regression

I would like to calculate two types of bivariate exponential models on scaled data (therefore both variables are expressed as z-scores): Model 1: $$ y=b_{0}*e^{b_{1}x} + \epsilon $$ Model 2 (is ...
BeniSportSci's user avatar
0 votes
1 answer
579 views

Back transform predict.gam() from nb link log model run?

I have model with 1 covariate. I would like to run y values from gam in another model. I used nb(log=link) in gam model. Because I used nb and link log in gam, do I need to back transform to use ...
Abott_Lore's user avatar
2 votes
0 answers
116 views

How to deal with output transformation at inference/prediction time?

Suppose A machine learning model (e.g. RandomForest) which uses $x$ as input and produces $y$. Now as part of preprocessing and feature engineering, I applied some ...
Atr Cheema's user avatar
0 votes
1 answer
538 views

How to correctly invert a confidence interval after a power transformation?

I have the following situation: I have some dataset in the form of samples $y_i, x_{i,j}$. I'm doing a GAM regression after a power transform (Yeo–Johnson - similar to Box-Cox). So I first learn the $\...
user344577's user avatar
0 votes
0 answers
827 views

Transformed Data with Boxcox and negative Lambda

unfortunately I'm a statistics beginner. I am trying to transform my data. Here, according to Boxcox, a transformation with negative lambda has resulted ( L =-0.25). But If I now run a T-tests with ...
Sophia H's user avatar
-2 votes
1 answer
202 views

Given marginal tables back solve for contingency table

How to programmatically solve problems similar to the below: Most basic example: Given two marginal tables, solve for 2x2 distribution. A Sum 0 3 1 7 B Sum 0 4 1 6 Solve for A B Sum 0 0 0 1 ...
user5309995's user avatar
1 vote
1 answer
197 views

Pearson's Correlation after power transformation of dependent variable

I have a simple model. y ~ x y is continuous (habitat gained per million $) x is continuous ...
Josh J's user avatar
  • 113
2 votes
1 answer
493 views

Back-transforming meta-analysis results in metafor

The R package metafor offers various ways of back-transforming the results of a meta-analysis with a transformed effect size/...
PyjamaNinja's user avatar
0 votes
1 answer
49 views

Interpreting log multiple linear regression, backtransformations?

I'm investigating adherence to a special diet (that is scored from 0-18) in relation to C-reactive protein level and am in the process of building multiple linear regression models: To achieve a ...
Murphy's user avatar
  • 1
1 vote
1 answer
222 views

Bias correction for regression with t-distributed error

I have a GAM /regression model which is originally defined as: log10(Y)~s(log10(X1))+s(log10(X2))+s(log10(X3)) #using R mgcv The response needs to be back ...
MriRo's user avatar
  • 103
0 votes
0 answers
167 views

Interpreting multiple regression with transformed response variable

I have build regression model focused on association between physical activity and fat mass. The model is as follows: ...
David Janda's user avatar
2 votes
1 answer
930 views

Bayesian lognormal model: how to correctly back-transform the estimates?

I have a Bayesian model of the form: $$ \begin{align} y & \sim logNormal(\mu, \sigma)\\ \mu_n & = \alpha + \beta_0 c_n + \beta_1 d_n + \beta_2 c_n d_n \end{align} $$ Where: $y$ is a ...
jjj's user avatar
  • 73
0 votes
0 answers
20 views

First difference in logs transformation produces biased results on back-transformation [duplicate]

I have a strongly trended series where the trend appears to be exponential and I believe the errors tend to be proportional to the current value. In order to convert it to a stationary series for ...
andrewH's user avatar
  • 3,157
1 vote
1 answer
2k views

Reversing Log-transformed target after training : r2 score interpretation

I have been running a log-transform on my target values because the distribution appears to be highly right skewed as you can see in the picture. After having called ...
James Arten's user avatar
0 votes
0 answers
73 views

Multiple linear regression - assumption of normaly distributed estimates

In linear regression, there is said that normal distribution of residuals is required. Other than that I have found that it can be violated if the dataset is large enough. But what is large enough? Is ...
David Janda's user avatar
1 vote
0 answers
120 views

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 ...
user45523's user avatar
  • 547
0 votes
0 answers
206 views

Back-transformation in pairs function (lmer analysis)

I'm running lme4 and Im working with a dataset where the variables a,d,e are logtransformed. I wonder how to handle back-transformation concerning the "pairs" function. How do I get the ...
user11916948's user avatar
2 votes
1 answer
2k views

Back transformed Median of log normal data, not equal to median of original data

l was working with these data: 160,320,160,160,320,320,160,320,160,320,160,320. I needed to calculate the median so decided to calculate while the data was log transformed and I would just back ...
Mick's user avatar
  • 95
2 votes
0 answers
271 views

Backtransform a log-odds transformation of the dependent variable in a Fixed Effects panel regression

I'm modeling a fixed effects (within) panel regression for a fractional dependent variable (DV) bounded between 0 and 1. My aim is to model the relationship so that I can predict the frational DV on ...
Beethoven_90's user avatar
1 vote
1 answer
939 views

How to back-transform ANOVA data?

I have been having trouble grasping the idea of back-transforming data in R. Let's assume that I would like to perform an ANOVA on transformed data. I transform the response variable and all the ...
John's user avatar
  • 11
1 vote
1 answer
576 views

Standardize-back the Standard deviation

I run an lmer model using standardized data like scale(y) ~ 1 + (1|categorical) Now, I have a standard deviation for the random effect in normalized world but I ...
Lefty's user avatar
  • 499