Publications by Isomorphismes
extrapolation and interpolation The most important lesson I…
extrapolation and interpolation The most important lesson I learned from this book: regression is reliable for interpolation, but not for extrapolation. Even further, your observations really need to cover the whole gamut of causal variables, intersections included, to justify faith in your regressions. Imagine you have two causal variables, ...
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Here is how to improve your charts, graphs, maps, and…
Here is how to improve your charts, graphs, maps, and plots: Erase non-data ink. Erase redundant data ink. Maximize the ratio of data to ink. Show data variation, not design variation. The surface area of graphical elements should be directly proportional to the numerical quantities represented. (Don’t use 3-D bar charts, for example.) Don’...
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Central Limit Theorem A nice illustration of the Central Limit…
Central Limit Theorem A nice illustration of the Central Limit Theorem by convolution.in R: Heaviside <- function(x) { ifelse(x>0,1,0) } HH <- convolve( Heaviside(x), rev(Heaviside(x)), type = "open" ) HHHH <- convolve(HH, rev(HH), type = "open" ) HHHHHHHH <- convolve(HHHH, rev(HHHH), type = "open" ) et...
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"My interpretation of [Leland Wilkinson’s] grammar [of statistical graphics]: —Data is the most…"
“My interpretation of [Leland Wilkinson’s] grammar [of statistical graphics]: —Data is the most important thing, and the thing that you bring to the table. —Geometric objects … what you actually see on the plot: points, lines, polygons, etc. —Statistics transform the data in many useful ways. For example, binning and count...
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"My interpretation of [Leland Wilkinson’s] grammar [of statistical graphics]: —Data is the most…"
“My interpretation of [Leland Wilkinson’s] grammar [of statistical graphics]: —Data is the most important thing, and the thing that you bring to the table. —Geometric objects … what you actually see on the plot: points, lines, polygons, etc. —Statistics transform the data in many useful ways. For example, binning and count...
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Seriously … why don’t math classes use computers?…
Seriously … why don’t math classes use computers? Excel, simple Python scripts, Mathematica / Sage, everything beyond the TI-83. Kids could be creating totally sweet visuals instead of cribbing formulae. And thinking instead of copying. I can say from my experience teaching that giving kids some real data and having them muck around with i...
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Hey! I made you some Wiener processes!
Check them out. Here are thirty homoskedastic ones: > homo.wiener <- array(0, c(100, 30)) > for (j in 1:30) { for (i in 2:length(homo.wiener)) { homo.wiener[i,j] <- homo.wiener[ i - 1, j] + rnorm(1) }} > for (j in 1:30) { plot( homo.wiener[,j], type = "l", col = r...
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Once you’re comfortable with 2-arrays and 2-matrices, you…
Once you’re comfortable with 2-arrays and 2-matrices, you can move up a dimension or two, to 4-arrays or 4-tensors. You can move up to a 3-array / 3-tensor just by imagining a matrix which “extends back into the blackboard”. Like a 5 × 5 matrix. With another 5 × 5 matrix behind it. And another 5 × 5 matrix behind that with 25 more e...
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Error Handling in Lyx & Sweave: using Quantmod (and R, of course)
I do reports for clients with LyX and Sweave. It took me an extremely long time to get them working, but now that they’re working I can do more in an hour and thus charge more per hour. If you’re not familiar, here’s a rundown: LaTeX is the standard writing tool for mathematicians. I started using it for business reports some time ago becau...
3539 sym R (312 sym/2 pcs)
Earthquakes
> data(quakes) > head(quakes) lat long depth mag stations 1 -20.42 181.62 562 4.8 41 2 -20.62 181.03 650 4.2 15 3 -26.00 184.10 42 5.4 43 4 -17.97 181.66 626 4.1 19 5 -20.42 181.96 649 4.0 11 6 -19.68 184.31 195 4.0 12 > summary(quakes) lat long depth ...
610 sym R (843 sym/2 pcs) 6 img