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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] 0.1748748Here 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 3.761833Here 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: 125970Here 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 -0.007789933Here 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 1Always 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:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.