A common model for counts data is the Poisson. There are cases however that we only record positive counts, ie there is a truncation of 0. This is the truncated Poisson model. To study this model we only need the total counts and the sample size. This comes from the sufficient statistic principle as the
Didn't know this... a data 0 2 4 7+ 25 34 12 5 It's becoming clear that I have learned R in the most unstructured way...I always do it in two stages :ashamed:
(The R code for Barnard’s exact test is at the end of the article, and you could also just download it from here) About Barnard’s exact test About half a year ago, I was studying various statistical methods to employ on contingency tables. I came across a promising method for 2×2 contingency tables called “Barnard’s exact test“. Barnard’s test is a non-parametric alternative to Fisher’s exact...
Let’s say that you’re fitting a cumbersome model so time is not to waste over a PC staring at the screen half anxious-half bored…
Then, you can always leave and go on with meetings and all your daily routine and have R notify you the results! How?
We will illustrate the situation above using some Bayesian Model 
Instead of the factor() function which usually applies after defining a vector there’s the gl() base function to do this in one step, eg
freq <- c(204,6,1,211,13,5,357,44,38,92,34,49)
row <- gl(4,3,length=12)
col <- gl(3,1,length=12)
> col
1 2 3 1 2 3 1 2 3 1 2 3
Levels: 1 2 3
tt <- data.frame(freq,row,col)
> xtabs(tt)
col
row 1 2 3
1 204 6 
blotter is an R package that tracks the P&L of your trading systems (or simulations), even if your portfolio spans many security types and/or currencies. This post uses blotter to track a simple two-ETF trading system. The contents of this post b...
Estimating a proportion at first looks elementary. Hail to aymptotics, right? Well, initially it might seem efficient to iuse the fact that . In other words the classical confidence interval relies on the inversion of Wald’s test.
A function to ease the computation is the following (not really needed!).
waldci<- function(x,n,level){
phat<-sum(x)/n
results<-phat + c(-1,1)*qnorm(1-level/2)*sqrt(phat*(1-phat)/n)
print(results)
}
An exact confidence interval is 
Well testing a bunch of samples for the largest population mean isn’t that common yet a simple test is at hand. Under the obvious title “The rank sum maximum test for the largest K population means” the test relies on the calculation of the sum of ranks under the combined sample of size , where 
R Lesson 2 text gsub ("second", "third", text) SEARCH-REPLACE-SUBJECT "This is a first example sentence." "And this is a third example sentence." > gsub ("n", "X", text) "This is a first example seXteXce." "AXd this is a secoXd example seXteXce." > gsub ("is", "was", text) "Thwas was a first example
(This post documents the first day of a class on R that I took at ESU C&T. I is posted here purely for my own use.) R Lesson 1 > 2+3; 2/3; 2^3 5 0.6666667 8 --- Fundamentals - Functions > log(x=1000, base=10) 3 --- (Formals describes the syntax of other
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
