Thanks, guys.
On Sat, Jun 8, 2013 at 2:17 PM, Neal Fultz nfu...@gmail.com wrote:
rowSums and Reduce will have the same problems with bad data you alluded
to earlier, eg
cg = 1, hs = 0
But that's something to check for with crosstabs anyway.
This wrong data thing is a distraction here. I
rowSums and Reduce will have the same problems with bad data you alluded to
earlier, eg
cg = 1, hs = 0
But that's something to check for with crosstabs anyway.
Side note: you should check out the microbenchmark pkg, it's quite handy.
Rrequire(microbenchmark)
Rmicrobenchmark(
+
In our Summer Stats Institute, I was asked a question that amounts to
reversing the effect of the contrasts function (reconstruct an ordinal
predictor from a set of binary columns). The best I could think of was to
link together several ifelse functions, and I don't think I want to do this
if the
Hi Paul,
Unless you have truly offended the data generating oracle*, the
pattern: NA, 1, NA, should be a data entry error --- graduating HS
implies graduating ES, no? I would argue fringe cases like that
should be corrected in the data, not through coding work arounds.
Then you can just do:
x -
I would do this to get the highest non-missing level:
x - pmax(3*cg, 2*hs, es, 0, na.rm=TRUE)
rock chalk...
-nfultz
On Fri, Jun 07, 2013 at 06:24:50PM -0700, Joshua Wiley wrote:
Hi Paul,
Unless you have truly offended the data generating oracle*, the
pattern: NA, 1, NA, should be a data
I still argue for na.rm=FALSE, but that is cute, also substantially faster
f1 - function(x1, x2, x3) do.call(paste0, list(x1, x2, x3))
f2 - function(x1, x2, x3) pmax(3*x3, 2*x2, es, 0, na.rm=FALSE)
f3 - function(x1, x2, x3) Reduce(`+`, list(x1, x2, x3))
f4 - function(x1, x2, x3) rowSums(cbind(x1,
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