# Tag Info

## New answers tagged data-visualization

1

Regardless of what level you set as reference, the resulting model fit will be equivalent. You are probably interested in if there are there are mean differences in the substrate levels. After fitting the model you should run a Tukey Post-Hoc comparison to see which levels differ. TukeyHSD function in R. Again, it does not matter on what the reference ...

0

In regression, you're looking to find the $\beta$ that minimizes: $(Y - X_1\beta_1 - X_2\beta_2 - \text{...})^2$ LASSO applies a penalty term to the minimization problem: $(Y - X_1\beta_1 - X_2\beta_2 - \text{...})^2 + \alpha\sum_i{|\beta_i|}$ So when $\alpha$ is zero, there is no penalization, and you have the OLS solution - this is max $|\beta|$ (or ...

0

Nothing unique to R was done for graph. From my reading they a priori decided on the cut-point, which makes everything easy. If the cut-point was data based, then it would be more painful, but fairly easy with segmented package in R. Note: This isn't what they did, but what they should have done. The results should be identical. This appears to be a ...

1

I agree with exponential scale and plotting ratios if there is a reference method. Here are some extra, different thoughts: Not knowing the context, I think the chart scale is fine as is. I can tell what reading of each point approximately and I can also see the trends quite clearly. Transformation fixes some problems and creates some other (mostly on ...

2

From what you say it sounds like you want to use igraph and find groups of individuals using moduarity, you can look here for details. e.g. dat <- structure(list(ID = structure(1:4, .Label = c("A", "B", "C", "D"), class = "factor"), A = c(2L, 2L, 0L, 0L), B = c(2L, 2L, 0L, 0L), C = c(0L, 0L, 2L, 1L), D = c(0L, 0L, 1L, 2L)), .Names = c("ID", "A", "B", ...

2

The red decay is an exponential function with negative exponent (as you might discern from sashkello's link, which I highly recommend). The green is the complement of that (they add to 1). That is, the green curve will be of the form $f(t)=1−\exp(−\alpha t)$ as a function of $t$, for some constant $α>0$, which is related to the half-life ...

1

For a dataset like that, I would start with histograms. First, do simple ones of price ranges, one histogram per keyword. Double check any outliers to make sure the data collection is correction (ie, you don't accidentally get an iPhone mixed in with your iPhone cases). Make sure the distributions make intuitive sense and there's no strange patterns for a ...

2

I am responding to a request for alternative graphical techniques that show how well simulated failure events match observed failure events. The question arose in "Probabilistic Programming and Bayesian Methods for Hackers " found here. Here's my graphical approach: Code found here.

2

If you want to move outside R, consider d3.js: You can animate scatterplots easily, using this library and any modern browser. There is an interactive example in the online version of Scott Murry's " Interactive Data Visualization for the Web An Introduction to Designing with D3 " here.

3

In terms of data visualization more generally, you probably want to use something like arrows going from the first set to the second set (and this is what I assume you intend by 'vectors' in any case). You should also distinguish the two sets of data in some way. I used both colours and made the 'source' symbols open circles and the destination symbols ...

3

Your prior is a full distribution, not just 10 values. If I were using a $N(5,1)$ prior, I would probably plot it by evaluating the density function at a large number of values over small increments in the neighborhood of its mode, like this: xv = seq(0,10,.01) plot(xv, dnorm(xv, 5, 1), type='l') Generally, I'd be more likely to plot the posterior using a ...

3

Let's step back, and think how to represent data instead of how to visualize data. I love data visualization but I'd say that graph is not a suitable solution here. Let's evaluate your requests: I'd like to be able to plot the data in such a way that the viewer will be able to tell how the whole sequence increases [F]or my application, the first ...

0

After much searching, I have found exactly the software I am looking for: http://www.datplot.com/ Simple, GUI-driven software that allows you to import raw data and plot graphs, with dynamic scroll and zoom.

5

Your idea of two plots with different scales is better than a broken axis since it gives you a perceptual view of how disparate the values are. Below is a quick mock-up. For presentation, the two graphs should have more separation and some cue or text that one is a zoomed in view of the other. Log or some other transformation may still be best to show the ...

5

There are quite a few ways to work around it. Jittering the variables mildly to smear the lines apart First, since both age and the outcome are nicely discrete, we can afford to mildly jitter them in order to show some trends. The trick is to use transparency in the line color so that it's easier to discern the magnitude of overlapping. library(geepack) ...

2

x<-0:300 a<-dnorm(x,165,15) b<-dnorm(x,149,18) c<-dnorm(x,134,25) plot(x,a, type="l", lwd=3, ylim=c(0,1.2*max(a,b,c)), ylab="Probability Density") segments(150,0, 150, a[which(x==150)], lwd=3, lty=2) segments(180,0, 180, a[which(x==180)], lwd=3, lty=2) text(165, a[which(x==165)], "165", pos=3) lines(x,b, type="l", lwd=3, col="Red") ...

1

To make a statement about the steady state behaviour of each cluster you could compute the steady state distributions of each transition matrix by eigenvectors, then compare box-plots by cluster. You're likely to run into issues in the computation of steady state without applying some sort of smoothing first. How are you clustering the transition matrices? ...

1

Another useful tool, although just for Windows, is Spotfire -- I found it quite useful for quickly looking at various histograms and scatter plots for single and pairs of variables. A research tool that helps you rank single variables as well as pairs based on simple statistics -- Hierarchical Clustering Explorer from HCIL. It is nice for finding most ...

0

Associated concept is regression clustering: joint clustering using a set of functions the data can be optimally regressed to, and regression. See for example Estimation and Selection in Regression Clustering Guoqi Qian1,∗, Yuehua Wu2

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