The library ptools is a set of helper functions I have used over time to help with analyzing count data, e.g. crime counts per month.
To install the most recent version from CRAN, it is simply:
install.packages('ptools')
You can install the current version on github using devtools:
library(devtools)
install_github("apwheele/ptools", build_vignettes = TRUE)
library(ptools) # Hopefully works!
Here is checking the difference in two Poisson means using an e-test:
library(ptools) e_test(6,2) #> [1] 0.1748748
Here is the Wheeler & Ratcliffe WDD test (see help(wdd) for academic references):
wdd(c(20,20),c(20,10)) #> #> The local WDD estimate is -10 (8.4) #> The displacement WDD estimate is 0 (0) #> The total WDD estimate is -10 (8.4) #> The 90% confidence interval is -23.8 to 3.8 #> Est_Local SE_Local Est_Displace SE_Displace Est_Total SE_Total #> -10.000000 8.366600 0.000000 0.000000 -10.000000 8.366600 #> Z LowCI HighCI #> -1.195229 -23.761833 3.761833
Here is a quick example applying a small sample Benford’s analysis:
# Null probs for Benfords law
f <- 1:9
p_fd <- log10(1 + (1/f)) #first digit probabilities
# Example 12 purchases on my credit card
purch <- c( 72.00,
328.36,
11.57,
90.80,
21.47,
7.31,
9.99,
2.78,
10.17,
2.96,
27.92,
14.49)
#artificial numbers, 72.00 is parking at DFW, 9.99 is Netflix
fdP <- substr(format(purch,trim=TRUE),1,1)
totP <- table(factor(fdP, levels=paste(f)))
resG_P <- small_samptest(d=totP,p=p_fd,type="G")
print(resG_P) # I have a nice print function
#>
#> Small Sample Test Object
#> Test Type is G
#> Statistic is: 12.5740089945434
#> p-value is: 0.1469451
#> Data are: 3 4 1 0 0 0 2 0 2
#> Null probabilities are: 0.3 0.18 0.12 0.097 0.079 0.067 0.058 0.051 0.046
#> Total permutations are: 125970
Here is an example checking the Poisson fit for a set of data:
x <- rpois(1000,0.5) check_pois(x,0,max(x),mean(x)) #> #> mean: 0.541 variance: 0.532851851851852 #> Int Freq PoisF ResidF Prop PoisD ResidD #> 1 0 579 582.165795 -3.16579540 57.9 58.2165795 -0.316579540 #> 2 1 321 314.951695 6.04830469 32.1 31.4951695 0.604830469 #> 3 2 82 85.194434 -3.19443358 8.2 8.5194434 -0.319443358 #> 4 3 16 15.363396 0.63660381 1.6 1.5363396 0.063660381 #> 5 4 2 2.077899 -0.07789933 0.2 0.2077899 -0.007789933
Here is an example extracting out near repeat strings (this is improved version from an old blog post using kdtrees):
# Not quite 15k rows for burglaries from motor vehicles
bmv <- read.csv('https://dl.dropbox.com/s/bpfd3l4ueyhvp7z/TheftFromMV.csv?dl=0')
print(Sys.time())
#> [1] "2023-02-07 09:53:24 EST"
BigStrings <- near_strings2(dat=bmv,id='incidentnu',x='xcoordinat',
y='ycoordinat',tim='DateInt',DistThresh=1000,TimeThresh=3)
print(Sys.time()) #very fast, only a few seconds on my machine
#> [1] "2023-02-07 09:53:25 EST"
print(head(BigStrings))
#> CompId CompNum
#> 000036-2015 1 1
#> 000113-2015 2 1
#> 000192-2015 3 1
#> 000251-2015 4 1
#> 000360-2015 5 1
#> 000367-2015 6 1
Always feel free to contribute either directly on Github, or email me with thoughts/suggestions. For citations for functions used, feel free to cite the original papers I reference in the functions instead of the package directly.
Things on the todo list:
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