Linked Questions
16 questions linked to/from Transforming proportion data: when arcsin square root is not enough
243
votes
4
answers
375k
views
When (and why) should you take the log of a distribution (of numbers)?
Say I have some historical data e.g., past stock prices, airline ticket price fluctuations, past financial data of the company...
Now someone (or some formula) comes along and says "let's take/use ...
- 14.8k
13
votes
4
answers
1k
views
Where does the logistic function come from?
I first learned the logistic function in machine learning, where it is just a function that map a real number to 0 to 1. We can use calculus to get the derivative and use it for some optimization ...
- 37.3k
16
votes
1
answer
18k
views
What is the most appropriate way to transform proportions when they are an independent variable?
I thought I understood this issue, but now I'm not as sure and I'd like to check with others before I proceed.
I have two variables, X and ...
- 555
9
votes
1
answer
8k
views
Why do we log transform response ratios?
In meta-analysis, it seems a standard practice to take the natural log of the response ratio before evaluating it.
My question is why?
That is, if I have a treatment mean (Xe) and a control mean of ...
- 540
5
votes
3
answers
1k
views
How does logistic growth rate coincide with the slope of the line in the exponential phase of the growth?
For a logistic function $$f(x) = \frac{L}{1 + e^{-k(x - x_0)}},$$ people call $k$ the logistic growth rate. Now, I have encountered this statement: In the log scale the logistic growth rate coincides ...
- 53
3
votes
1
answer
2k
views
What order preserving transformation makes data more evenly spread, decreasing the peak, and fattening the tails of the distribution?
How can I transform a variable (non linear transformation) such that its values are more evenly spread, that is reduce the peak in the middle of the histogram and move more into tails?
- 7,291
3
votes
2
answers
501
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 $\...
- 639
3
votes
0
answers
2k
views
Box-Cox, log or arcsine transformation? [closed]
Box-Cox, log and arcsine transformations have the aim of make the data more Normal. My question is: how can I choose between each one of these transformations? Which assumptions do I need to have to ...
- 371
0
votes
1
answer
328
views
Different ways of making a set of numbers (all between $0$ and $1$) to sum up to $1$
I have a set of numbers $S$, and for each $s_i\in S$, $0\lt s_i \lt 1$. I would like to transform them so that they sum up to $1$.
An obvious way to do it is to calculate $t_i=\frac{s_i}{\sum_i{s_i}}$...
- 185
1
vote
1
answer
313
views
How can I interpret the coefficient of a logit transformed explanatory variable in linear regression?
I fit a linear regression model with continuous response. One of my predictor variables is given in percentage. So I transformed the predictor with logit transformation. My question is how can I ...
- 11
5
votes
1
answer
206
views
What is the intuition behind the odds scale?
What is an intuitive explanation of the odds scale?
In a logistic regression such as $$logit(p) = \beta_0 + \beta_1 x$$
we often interpret $\beta_1$ by looking at the odds ratio, $e^{\beta_1}$, which ...
- 367
0
votes
1
answer
369
views
Model proportions as independent variable for binary outcome
I've binary (disease) outcome: 0, 1 with certain independent variables in proportions, and other covariates as - age, sex.
There are Packages that model proportions as outcome variable but not other ...
- 113
0
votes
0
answers
420
views
Is there any alternative way to Box-Cox transformations to stabilize the variance of a time series?
My question is straightforward: Is there any alternative way to Box-Cox transformations to stabilize the variance of a time series?
1
vote
0
answers
197
views
Advantage of inputs/targets to be normally distributed?
Why is it advantageous for inputs/targets to most ML algorithms like neural nets to be normally distributed? I am not talking about mean normalization, but in some cases of skewed data, people perform ...
- 782
4
votes
0
answers
106
views
Is there a name for a $y=\sqrt[k]{x}$-like data normalization?
I'm normalizing multivariate numeric data that has both negative and positive values. For the sake of the question let's assume a range of e.g. $[-10000,10000]$ with a lot of values in $[-1,1]$. I've ...
- 3,901