| stat.slide {pastecs} | R Documentation |
Statistical parameters are not constant along a time series: mean or variance can vary each year, or during particular intervals (radical or smooth changes due to a pollution, a very cold winter, a shift in the system behaviour, etc. Sliding statistics offer the potential to describe series on successive blocs defined along the space-time axis
stat.slide(x, y, xcut=NULL, xmin=min(x), n=NULL, frequency=NULL,
deltat=1/frequency, basic=FALSE, desc=FALSE, norm=FALSE,
pen=FALSE, p=0.95)
## S3 method for class 'stat.slide':
plot(statsl, stat="mean", col=c(1, 2), lty=c(par("lty"), par("lty")),
leg=FALSE, llab=c("series", stat), lpos=c(1.5, 10), ...)
## S3 method for class 'stat.slide':
lines(statsl, stat="mean", col=3, lty=1, ...)
x |
a vector with time data |
y |
a vector with observation at corresponding times |
xcut |
a vector with the position in time of the breaks between successive blocs. xcut=NULL by default. In the later case, a vector with equally spaced blocs is constructed using xmin, n and frequency or deltat. If a value is provided for xcut, then it supersedes all these other parameters |
xmin |
the minimal value in the time-scale to use for constructing a vector of equally spaced breaks |
n |
the number of breaks to use |
frequency |
the frequency of the breaks in the time-scale |
deltat |
the bloc interval touse for constructing an equally-spaced break vector. deltat is 1/frequency |
basic |
do we have to return basic statistics (by default, it is FALSE)? These are: the number of values (nbr.val), the number of null values (nbr.null), the number of missing values (nbr.na), the minimal value (min), the maximal value (max), the range (range, that is, max-min) and the sum of all non-missing values (sum) |
desc |
do we have to return descriptive statistics (by default, it is FALSE)? These are: the median (median), the mean (mean), the standard error on the mean (SE.mean), the confidence interval of the mean (CI.mean) at the p level, the variance (var), the standard deviation (std.dev) and the variation coefficient (coef.var) defined as the standard deviation divided by the mean |
norm |
do we have to return normal distribution statistics (by default, it is FALSE)? the skewness coefficient g1 (skewness), its significant criterium (skew.2SE, that is, g1/2.SEg1; if skew.2SE > 1, then skewness is significantly different than zero), kurtosis coefficient g2 (kurtosis), its significant criterium (kurt.2SE, same remark than for skew.2SE), the statistic of a Shapiro-Wilk test of normality (normtest.W) and its associated probability (normtest.p) |
pen |
do we have to return Pennington and other associated statistics (by default, it is FALSE)? pos.median, pos.mean, pos.var, pos.std.dev, respectively the median, the mean, the standard deviation and the variance, considering only non-null values; geo.mean, the geometric mean that is, the exponential of the mean of the logarithm of the observations, excluding null values. pen.mean, pen.var, pen.std.dev, pen.mean.var, respectively the mean, the variance, the standard deviation and the variance of the mean after Pennington's estimators (see pennington()) |
p |
the probability level to use to calculate the confidence interval on the mean (CI.mean). By default, p=0.95 |
statsl |
a 'stat.slide' object, as returned by the function stat.slide() |
stat |
the statistic to plot on the graph. You can use "min", "max", "median", "mean" (by default), "pos.median", "pos.mean", "geo.mean" and "pen.mean". The other statistics cannot be superposed on the graph of the series in the current version of the function |
col |
the colors to use to plot the initial series and the statistics, respectively. By default, col=c(1,2) |
lty |
the style to use to draw the original series and the statistics. The default style is used if this argument is not provided |
leg |
if leg=TRUE, a legend box is drawn on the graph |
llab |
the labels to use for the legend. By default, it is "series" and the corresponding statistics provided in stat, respectively |
lpos |
the position of the top-left corner (x,y) of the legend box in the graph coordinates. By default lpos=c(1.5,10) |
... |
additional graph parameters |
Available statistics are the same as for stat.desc() and stat.pen(). The Shapiro-Wilk test of normality is not available yet in Splus and it returns 'NA' in this environment. If not a priori known, successive blocs can be identified using either local.trend() or decmedian() (see respective functions for further details)
An object of type 'stat.slide' is returned. It has methods print(), plot() and lines().
Frédéric Ibanez (), Philippe Grosjean ()
stat.desc, stat.pen, pennington, local.trend, decmedian
stat.slide, lines.stat.slide, plot.stat.slide, print.stat.slide
ts
data(marbio)
# Sliding statistics with fixed-length blocs
statsl <- stat.slide(1:68, marbio[, "ClausocalanusA"], xmin=0, n=7, deltat=10)
statsl
plot(statsl, stat="mean", leg=TRUE, lpos=c(55, 2500), xlab="Station",
ylab="ClausocalanusA")
# More information on the series, with predefined blocs
statsl2 <- stat.slide(1:68, marbio[, "ClausocalanusA"],
xcut=c(0, 17, 25, 30, 41, 46, 70), basic=TRUE, desc=TRUE, norm=TRUE,
pen=TRUE, p=0.95)
statsl2
plot(statsl2, stat="median", xlab="Stations", ylab="Counts",
main="Clausocalanus A") # Median
lines(statsl2, stat="min") # Minimum
lines(statsl2, stat="max") # Maximum
lines(c(17, 17), c(-50, 2600), col=4, lty=2) # Cuts
lines(c(25, 25), c(-50, 2600), col=4, lty=2)
lines(c(30, 30), c(-50, 2600), col=4, lty=2)
lines(c(41, 41), c(-50, 2600), col=4, lty=2)
lines(c(46, 46), c(-50, 2600), col=4, lty=2)
text(c(8.5, 21, 27.5, 35, 43.5, 57), 2300, labels=c("Peripheral Zone", "D1",
"C", "Front", "D2", "Central Zone")) # Labels
legend(0, 1900, c("series", "median", "range"), col=1:3, lty=1)
# Get cuts back from the object
statsl2$xcut
R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(pastecs)
> png(filename="images/stat.slide_%03d.png" ,width=480, height=480)
> ### Encoding: latin1
>
> ### Name: stat.slide
> ### Title: Sliding statistics
> ### Aliases: stat.slide lines.stat.slide plot.stat.slide print.stat.slide
> ### Keywords: ts
>
> ### ** Examples
>
> data(marbio)
> # Sliding statistics with fixed-length blocs
> statsl <- stat.slide(1:68, marbio[, "ClausocalanusA"], xmin=0, n=7, deltat=10)
> statsl
[0,10[ [10,20[ [20,30[ [30,40[ [40,50[ [50,60[ [60,70[
xmin 0.000000 10.0000 20.0000 30.0000 40.000 50.000 60.0000
xmax 10.000000 20.0000 30.0000 40.0000 50.000 60.000 70.0000
nbr.val 9.000000 10.0000 10.0000 10.0000 10.000 10.000 9.0000
nbr.null 2.000000 0.0000 0.0000 0.0000 0.000 0.000 0.0000
nbr.na 0.000000 0.0000 0.0000 0.0000 0.000 0.000 0.0000
min 0.000000 160.0000 158.0000 96.0000 136.000 56.000 52.0000
max 12.000000 832.0000 1980.0000 1204.0000 2484.000 824.000 656.0000
median 4.000000 344.0000 732.0000 752.0000 260.000 340.000 120.0000
mean 4.777778 350.8000 810.1000 682.0000 494.400 364.800 219.1111
std.dev 4.790036 196.9708 619.8578 350.0616 711.051 234.915 202.4231
> plot(statsl, stat="mean", leg=TRUE, lpos=c(55, 2500), xlab="Station",
+ ylab="ClausocalanusA")
>
> # More information on the series, with predefined blocs
> statsl2 <- stat.slide(1:68, marbio[, "ClausocalanusA"],
+ xcut=c(0, 17, 25, 30, 41, 46, 70), basic=TRUE, desc=TRUE, norm=TRUE,
+ pen=TRUE, p=0.95)
> statsl2
[0,17[ [17,25[ [25,30[ [30,41[
xmin 0.000000e+00 1.700000e+01 2.500000e+01 3.000000e+01
xmax 1.700000e+01 2.500000e+01 3.000000e+01 4.100000e+01
nbr.val 1.600000e+01 8.000000e+00 5.000000e+00 1.100000e+01
nbr.null 2.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
nbr.na 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
min 0.000000e+00 1.580000e+02 1.040000e+03 9.600000e+01
max 8.320000e+02 4.240000e+02 1.980000e+03 1.204000e+03
range 8.320000e+02 2.660000e+02 9.400000e+02 1.108000e+03
sum 2.785000e+03 2.151000e+03 6.716000e+03 7.060000e+03
median 1.200000e+01 2.120000e+02 1.204000e+03 7.440000e+02
mean 1.740625e+02 2.688750e+02 1.343200e+03 6.418182e+02
SE.mean 6.066894e+01 3.704796e+01 1.668120e+02 1.078927e+02
CI.mean.0.95 1.293128e+02 8.760450e+01 4.631443e+02 2.403999e+02
var 5.889153e+04 1.098041e+04 1.391312e+05 1.280492e+05
std.dev 2.426758e+02 1.047875e+02 3.730029e+02 3.578396e+02
coef.var 1.394188e+00 3.897255e-01 2.776973e-01 5.575404e-01
skewness 1.248551e+00 4.181831e-01 8.469063e-01 -4.141160e-02
skew.2SE 1.106268e+00 2.780098e-01 4.638697e-01 -3.133978e-02
kurtosis 7.020336e-01 -1.852389e+00 -1.193537e+00 -1.424298e+00
kurt.2SE 3.218053e-01 -6.254351e-01 -2.983842e-01 -5.566203e-01
normtest.W 7.557989e-01 8.300443e-01 8.162688e-01 9.560890e-01
normtest.p 7.467702e-04 5.943036e-02 1.092401e-01 7.221449e-01
pos.median 8.600000e+01 2.120000e+02 1.204000e+03 7.440000e+02
pos.mean 1.989286e+02 2.688750e+02 1.343200e+03 6.418182e+02
geo.mean 3.935062e+01 2.522408e+02 1.307640e+03 5.181136e+02
pen.mean 3.351203e+02 2.683489e+02 1.340628e+03 6.787440e+02
pen.var 1.907110e+06 1.039770e+04 1.119418e+05 3.111941e+05
pen.std.dev 1.380981e+03 1.019691e+02 3.345771e+02 5.578477e+02
pen.mean.var 5.558396e+04 1.298434e+03 2.238628e+04 2.768567e+04
[41,46[ [46,70[
xmin 41.00000000 4.600000e+01
xmax 46.00000000 7.000000e+01
nbr.val 5.00000000 2.300000e+01
nbr.null 0.00000000 0.000000e+00
nbr.na 0.00000000 0.000000e+00
min 136.00000000 5.200000e+01
max 264.00000000 2.484000e+03
range 128.00000000 2.432000e+03
sum 1088.00000000 9.236000e+03
median 256.00000000 3.080000e+02
mean 217.60000000 4.015652e+02
SE.mean 27.29395537 1.048611e+02
CI.mean.0.95 75.78016880 2.174685e+02
var 3724.80000000 2.529043e+05
std.dev 61.03113959 5.028960e+02
coef.var 0.28047399 1.252339e+00
skewness -0.35978254 3.033043e+00
skew.2SE -0.19706101 3.150646e+00
kurtosis -2.10864917 9.865540e+00
kurt.2SE -0.52716229 5.277023e+00
normtest.W 0.78640769 6.024972e-01
normtest.p 0.06250056 9.610831e-07
pos.median 256.00000000 3.080000e+02
pos.mean 217.60000000 4.015652e+02
geo.mean 209.92260248 2.527195e+02
pen.mean 218.05538851 3.905421e+02
pen.var 4518.02570358 1.913652e+05
pen.std.dev 67.21626071 4.374531e+02
pen.mean.var 903.39387927 7.745030e+03
> plot(statsl2, stat="median", xlab="Stations", ylab="Counts",
+ main="Clausocalanus A") # Median
> lines(statsl2, stat="min") # Minimum
> lines(statsl2, stat="max") # Maximum
> lines(c(17, 17), c(-50, 2600), col=4, lty=2) # Cuts
> lines(c(25, 25), c(-50, 2600), col=4, lty=2)
> lines(c(30, 30), c(-50, 2600), col=4, lty=2)
> lines(c(41, 41), c(-50, 2600), col=4, lty=2)
> lines(c(46, 46), c(-50, 2600), col=4, lty=2)
> text(c(8.5, 21, 27.5, 35, 43.5, 57), 2300, labels=c("Peripheral Zone", "D1",
+ "C", "Front", "D2", "Central Zone")) # Labels
> legend(0, 1900, c("series", "median", "range"), col=1:3, lty=1)
> # Get cuts back from the object
> statsl2$xcut
[1] 0 17 25 30 41 46 70
>
>
>
> dev.off()
null device
1
>
