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
0 votes
0 answers
18 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....
user avatar
  • 65
0 votes
0 answers
20 views

Back-transformation of confidence intervals

I have read that I can simply back-transform my confidence intervals from my (mixed) linear models, which seems very handy for model interpretation. What I don't understand is why log-transforming the ...
user avatar
  • 1
0 votes
1 answer
66 views

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

I am fitting a linear mixed model ...
user avatar
  • 467
0 votes
1 answer
23 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 ...
user avatar
0 votes
1 answer
89 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 ...
user avatar
2 votes
0 answers
44 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 ...
user avatar
0 votes
1 answer
80 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 $\...
user avatar
0 votes
0 answers
141 views

Back transforming from a log-transformed and subsequently standardized (outcome) variable

Due to skewness of my data I've performed a natural log transformation of my outcome variables, dealing with scores of 0 by adding 1 to all measurements. Then, to be able to compare relationships ...
user avatar
  • 119
0 votes
0 answers
12 views

calculate untransformed OLS coefficient

I used OLS model to understand the effect of promotion on units sold. I have transformed the dependent variable( square root) as well as the independent variables(standard scaler).The coefficients ...
user avatar
0 votes
0 answers
154 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 ...
user avatar
-2 votes
1 answer
37 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 ...
user avatar
0 votes
0 answers
58 views

Estimate baseline and relative change in multilevel Bayesian regression with logged and then scaled outcome

I'd like to interpret the results of a Bayesian regression with a log as baseline value (intercept-only model) and relative change (full model). To achieve this I log-transformed the outcome and then ...
user avatar
  • 2,313
0 votes
0 answers
89 views

Is this the correct way to compute confidence intervals on the original scale for GLM(M)s?

Suppose I have fitted a GLM and want to produce a confidence interval (or a prediction interval) on the original scale of the outcome. What I would do is estimate it on the link scale and then inverse ...
user avatar
1 vote
1 answer
94 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 ...
user avatar
  • 113
2 votes
1 answer
86 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/...
user avatar
0 votes
1 answer
29 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 ...
user avatar
  • 1
1 vote
1 answer
92 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 ...
user avatar
  • 83
0 votes
0 answers
54 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: ...
user avatar
2 votes
1 answer
367 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 ...
user avatar
  • 73
0 votes
0 answers
19 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 ...
user avatar
  • 2,597
0 votes
1 answer
180 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 ...
user avatar
0 votes
0 answers
57 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 ...
user avatar
1 vote
0 answers
53 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 ...
user avatar
  • 507
0 votes
0 answers
86 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 ...
user avatar
2 votes
1 answer
576 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 ...
user avatar
  • 95
1 vote
0 answers
127 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 ...
user avatar
1 vote
1 answer
127 views

Back-transformations

I have been having trouble grasping the idea of back-transforming data in R. Lets assume that I would like to perform an ANOVA on transformed data. I transform the response variable and all the ...
user avatar
  • 11
0 votes
1 answer
117 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 ...
user avatar
  • 155
0 votes
0 answers
15 views

Can we transform back calendar adjusted forecast?

I've monthly data, I adjusted it for monthdays and conducted a forecast. After that can I adjust it back and compare it with untransformed time serie?
user avatar
  • 277
0 votes
0 answers
183 views

Back-transformation of coefficients in a log-log model, and a model with a boxcox transformed y

There's a number of posts out there on back-transformations but none that I've been able to use to answer this problem, internet searches are returning surprisingly little on the issue of back-...
user avatar
  • 11
1 vote
0 answers
863 views

Pairwise contrasts for logistic regression in emmeans, back-transform or not?

I ran a mixed effects logistic regression in R (glmer). The model identified a significant three-way interaction that I am interested in decomposing using post-hoc multiple comparison in emmeans. In ...
user avatar
1 vote
0 answers
31 views

predicting waste per capita: is a Gaussian model correct?

I am trying to predict the daily amount of waste per person produced in the fishery sector. We surveyed fishing boats at the end of their fishing trip and the variables I have are duration of trip (...
user avatar
4 votes
1 answer
2k views

XGboost Regression Log Transform Target

I'm training a XGBoost Regressor to predict price which has a highly right skewed distribution. (all prices are positive) I took log transformation on the target thinking it would help to 1) stable ...
user avatar
  • 41
0 votes
0 answers
146 views

Back-transforming coefficients of glmer model fit using rescaled independent variables for prediction & plotting

I've fit a model using the glmer function from the lme4 package. Because my predictor variables are on very different scales, I ...
user avatar
  • 65
1 vote
0 answers
123 views

Back-transforming forecast points and error bounds from a VAR model applied to alr-transformed compositional time series

The data I have a k=3 compositional time series from which I am trying to forecast future values. The series is compositional in that at each time ...
user avatar
  • 113
1 vote
0 answers
51 views

How do I back-transfrom my estimates (and confidence intervals) from log10 variables (both independent and dependent) in linear model

I ran linear models for a bunch of variables in R. Both variables are log10 transformed (dependent and independent). I already took a look in some online documents and videos about this but i guess I'...
user avatar
2 votes
2 answers
745 views

How to get the actual mean absolute error in cross validation after transforming the target variable y?

For a target variable y, it is transformed using np.log1p. Then a random forest regression model is trained using the ...
user avatar
  • 135
17 votes
1 answer
1k views

Predicting y from log y as the dependent variable

In the book Introductory Econometrics by Wooldridge the chapter, which deals with predicting values of $\hat{y}$ (chapter 6.4 in the 5th edition) states the following: If the estimated model is: ...
user avatar
0 votes
1 answer
158 views

Which one is correct presentation for descriptive statistic of a transformed variable between retransformed mean (estimated SD), and median (qurtiles)

If a collected variable has a skewed distribution toward one side and transformed for statistical analysis, which one is better for descriptive statistics presentation between re-transformed mean and ...
user avatar
  • 31
4 votes
1 answer
401 views

When is performing back-transformation of inferences on transformed variables Ok, and when is it not Ok?

Caveat: This question may be a tad rambly, and I welcome comments with specific directions for me to improve it. During a too brief exchange with the worthy @NickCox I got to thinking about ...
user avatar
  • 26.6k
1 vote
0 answers
184 views

How to plot estimate + raw data of a Bayesian zero inflated poisson?

GENERAL QUESTION: How to back-transform estimates from a zero-inflated poisson to obtain the original scale in R? (I tried exponential like for poisson but the results are wrong) DETAILED ...
user avatar
  • 266
0 votes
0 answers
144 views

Why are the confidence intervals so large for the difference of differences?

I have run a generalized linear mixed effects model with the glmmTMB package to determine if there is an interaction between two categorical predictors, treatment and location, in predicting the ...
user avatar
  • 81
0 votes
0 answers
252 views

Back transforming the intercept of double log normal distribution

I apologise if this is a duplicate, i couldn't find a thread that seem to be talking about the same thing. I have a dataset with a bunch of duration of varying lengths in seconds, i log-transformed ...
user avatar
0 votes
0 answers
633 views

Back-Transformation for Ln(X+1) of zero rich data

I have seen and read several similar questions, but mine pertains specifically to zero rich data. I will be back transforming my data based on a first order Taylor series approximation. As outlined ...
user avatar
2 votes
2 answers
294 views

How to back transform a folded root?

I have some data where the response variable is a proportion, and I am experimenting with transformation using Tukey's family of folded powers, $f(p) = p^\lambda - (1 - p)^\lambda$, with values of $\...
user avatar
  • 599
0 votes
1 answer
70 views

Log transformed data

When you log transform data what is being tested on the original scale of the data? And why can we use our log transformed data to answer our scientific question? We are looking at data comparing ...
user avatar
0 votes
1 answer
281 views

Transforming non-normal to normal distribution and back-transform

I would like to transform non-normal distribution to normal distribution, and back-transform to its original state (or at least close to the original state). From this article, I've read that you can ...
user avatar
  • 901
1 vote
1 answer
1k views

How to back-transform negative Beta coefficients of linear regression after log transformation?

For a linear regression model, I have had to perform a natural log transformation of a response variable due to non-normal distribution in R. I am in the process of back-transforming the coefficients, ...
user avatar
0 votes
1 answer
364 views

slope in a log-transformation

Given y(x) = A*exp(x+c). I'm converting the y-axis into a log-scale. Then I calculate the slope at a certain point. Am I'm allowed to re-transform this value dy(x)/dx? Hence: exp(dy(x)/dx)?
user avatar
  • 2,237
2 votes
0 answers
77 views

How to recalculate Confidence interval after logarithmic and square transformations?

I'm performing test on one variance and in order to asses normality of X, I've transformed data with first natural logarithm transformation,then square transformation and calculated confidence ...
user avatar