# Tag Info

### Transformations for zero inflated non-negative continuous response variable in R

Your idea to work with logs here is heading in the right direction, but there's a better way to do it. It's generally best to work as close as possible to the original data when modeling. Your ...
1 vote

### Summarising and Visualising three attributes in R

A Wilkinson dot plot is a handy graphics for visualizing counts. It's a histogram of stacked dots. Here is a Wilkinson dot plot of the sample data, split by deprivation. It's easy to notice for ...

### Summarising and Visualising three attributes in R

Here is a possibility: plot hospital stay against age, with five different loess fit lines, one per deprivation level. In the plot below, I used your example data (actually, I replicated it five times,...

### log(x * constant) transformation

As noticed in the comments, $$\log(x \times c) = \log x + \log c$$ by the properties of the logarithms. So what you are doing is you shift the data by a constant. It doesn't change anything about ...

### log(x * constant) transformation

[Update] Now that we know the question is about visualization and not analysis, the mathematical properties of the logarithm seem less relevant than how people perceive log-scaled data. The goal of ...

### Is it logical to look for a correlation between average and percentage?

The biggest risk of looking at statistics of the aggregates is ecological fallacy (example here). Group-level aggregates do not necessarily are suitable for inferring individual-level characteristics. ...
1 vote
Accepted

### dataset in log & lin-lin regression function vs. dataset not in log & log-log regression function: Why different results in R?

That should not be happening. Perhaps your "already transformed" data takes a different type of logarithm. To take a toy example when I try ...
Accepted

### Distribution of an RBF-transformed normal variable

Expanding on whuber's answer (+1), I'd like to cover a more general case, as stated in the question. We have a standard normal random variable $X \sim N(0, 1)$. We transform this variable by passing ...
To get to the essence of this question, let's generalize it a little. Suppose $X$ is a random variable supported on a set (of real numbers) $\mathcal A$ where it has a density proportional to f_X(x)...