| Title: | What the package does (short line) |
|---|---|
| Description: | More about what it does (maybe more than one line). |
| Authors: | Who wrote it |
| Maintainer: | Paul Smith <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.4.2 |
| Built: | 2024-09-06 16:17:49 UTC |
| Source: | https://github.com/waternumbers/anomalous |
An R implimentation of the segmented search algorithmpelt algorithm
capa(part, fCost, prune = TRUE, verbose = FALSE)capa(part, fCost, prune = TRUE, verbose = FALSE)
part |
the starting partition |
fCost |
the cost function |
prune |
logical, should pruning be used |
verbose |
logical, print out progress |
Basic R implimentation of pelt - not efficent
the optimal partition
An implimentation of the CROPS algorithm in 1D
crops( betaMin, betaMax, fCost, alg = pelt, betaP = Inf, min_length = 2, prune = TRUE, verbose = FALSE, maxIter = 100 )crops( betaMin, betaMax, fCost, alg = pelt, betaP = Inf, min_length = 2, prune = TRUE, verbose = FALSE, maxIter = 100 )
betaMin |
lower bound of penalisation window |
betaMax |
upper bound of penalisation window |
fCost |
the cost function |
alg |
algorithm either capa of pelt |
betaP |
penalty for adding a point anomaly - only for use with capa |
min_length |
minimum number of values in a collective anomaly |
prune |
logical, should pruning be used |
verbose |
logical, print out progress |
maxIter |
maximum number of algorithm evaluations to perform |
This will only work for cost functions where the beta is additive!!!
something...
This dataset is taken from Lai W, Johnson MJ, Kucherlapati R, Park PJ, Bioinformatics , 2005. The paper states that the original source of the data is from Bredel et al. (2005). The data is an excerpt of chromosome 7 in GBM29 from 40 to 65 Mb.
This version of the data is a copy of that in the changepoint package.
data(Lai2005fig4)data(Lai2005fig4)
A matrix of dimensions 193 x 5. The columns are Spot, CH, POS.start, POS.end, GBM31.
http://compbio.med.harvard.edu/Supplements/Bioinformatics05b/Profiles/Chrom_7_from40_to65Mb_GBM29.xls
Temperature sensor data of an internal component of a large, industrial machine. The data contains three known anomalies. The first anomaly is a planned shutdown of the machine. The second anomaly is difficult to detect and directly led to the third anomaly, a catastrophic failure of the machine. The data consists of 22695 observations of machine temperature recorded at 5 minute intervals along with the date and time of the measurement. The data was obtained from the Numenta Anomaly Benchmark, which can be found at https://github.com/numenta/NAB.
data(machinetemp)data(machinetemp)
A dataframe with 22695 rows and 2 columns. The first column contains the date and time of the temperature measurement. The second column contains the machine temperature.
Get the parameters for a partitioning result
param(res, fCost)param(res, fCost)
res |
the result of a partitioning algorithm |
fCost |
the cost function |
Not yet implimented for all cost functions
list of parameters
An R implimentation of the segmented search algorithmpelt algorithm
pelt(part, fCost, prune = TRUE, verbose = FALSE)pelt(part, fCost, prune = TRUE, verbose = FALSE)
part |
the starting partition |
fCost |
the cost function |
prune |
logical, should pruning be used |
verbose |
logical, print out progress |
Basic R implimentation of pelt - not efficent
the optimal partition
A simulated data set for use in the examples and vignettes. The data consists of 500 observations on 20 variates drawn from the standard normal distribution. Within the data there are three multivariate anomalies of length 15 located at t=100, t=200, and t=300 for which the mean changes from 0 to 2. The anomalies affect variates 1 to 8, 1 to 12 and 1 to 16 respectively.
data(simulated)data(simulated)
A matrix with 500 rows and 40 columns.
Daily average wind speeds for 1961-1978 at 12 synoptic meteorological stations in the Republic of Ireland (Haslett and raftery 1989). Wind speeds are in knots (1 knot = 0.5418 m/s), at each of the stations in the order given in Fig.4 of Haslett and Raftery (1989, see below)
This data is a copy of that contained within the gstat package
data(wind)data(wind)
data.frame wind contains the following columns:
year, minus 1900
month (number) of the year
day
average wind speed in knots at station RPT
average wind speed in knots at station VAL
average wind speed in knots at station ROS
average wind speed in knots at station KIL
average wind speed in knots at station SHA
average wind speed in knots at station BIR
average wind speed in knots at station DUB
average wind speed in knots at station CLA
average wind speed in knots at station MUL
average wind speed in knots at station CLO
average wind speed in knots at station BEL
average wind speed in knots at station MAL
data.frame wind.loc contains the following columns:
Station name
Station code
Latitude, in DMS, see examples below
Longitude, in DMS, see examples below
mean wind for each station, metres per second
This data set comes with the following message: “Be aware that the dataset is 532494 bytes long (thats over half a Megabyte). Please be sure you want the data before you request it.” The data were obtained on Oct 12, 2008, from: http://www.stat.washington.edu/raftery/software.html The data are also available from statlib. Locations of 11 of the stations (ROS, Rosslare has been thrown out because it fits poorly the spatial correlations of the other stations) were obtained from: http://www.stat.washington.edu/research/reports/2005/tr475.pdf Roslare lat/lon was obtained from google maps, location Roslare. The mean wind value for Roslare comes from Fig. 1 in the original paper. Haslett and Raftery proposed to use a sqrt-transform to stabilize the variance.
Adrian Raftery; imported to R by Edzer Pebesma
These data were analyzed in detail in the following article:
Haslett, J. and Raftery, A. E. (1989). Space-time Modelling with Long-memory Dependence: Assessing Ireland's Wind Power Resource (with Discussion). Applied Statistics 38, 1-50. and in many later papers on space-time analysis, for example: Tilmann Gneiting, Marc G. Genton, Peter Guttorp: Geostatistical Space-Time Models, Stationarity, Separability and Full symmetry. Ch. 4 in: B. Finkenstaedt, L. Held, V. Isham, Statistical Methods for Spatio-Temporal Systems.
data(wind) summary(wind)data(wind) summary(wind)