Description Usage Arguments Value Author(s) References See Also Examples
View source: R/climdex.data.frame.R
Create input object for clim,ete index analyis from RMAWGEN output.
1 2 3 4 5 6 7 8 9 | climdex.data.frame(data, station, realization_TN,
realization_TX, realization_PREC, start_date =
"1981-01-01", end_date = "2010-12-31", climate_index =
"climdex.gsl", frequency = c("yearly", "monthly",
"daily"), freq = c("default", "monthly", "annual"),
date.series = seq(as.PCICt(start_date, cal =
"gregorian"), as.PCICt(end_date, cal = "gregorian"), by
= "days"), base.range = c(1990, 2002), n = 5, prefix =
NULL, ...)
|
data |
data.frame containing realizations of weather
variables, e.g. the one retured as |
station |
names of weather stations where to apply climate indices |
realization_TN |
realizations of daily minimum temperature (observed and simulated) time series on which climate index are calculated |
realization_TX |
realizations of daily maximum temperature (observed and simulated) time series on which climate index are calculated |
realization_PREC |
realizations of daily
precipitation (observed and simulated) time series on
which climate index are calculated. It is |
start_date |
start date |
end_date |
start date |
climate_index |
climate indices to be calculated.
The names must correspond to the name of the respective
function contained in the |
yearly |
logical voalue. If |
base.range |
see |
n |
see |
prefix |
name for time series on which climate indices are calculated. |
date.series |
see |
frequency |
string value. Default is
|
freq |
string value. Default is
|
... |
further arguments |
a climdex.data.frame
object (see the variable
climdex
in the examples.)
Emanuele Cordano, Annalisa Di Piazzaa
as.climdex.data.frame
,climdexInput.raw
,climdex.tn90p
,climdex.tx90p
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | rm(list=ls())
library(RClimMAWGEN)
# generated and observed daily temperature data for the considering period
# (1981-2010)(RMAWGEN output data structure)
data (generation_p1)
#collected generated (realizations) and observed data (realizations$Tx_mes, realizations$Tn_mes)
realizations <- generation_p1$output
realizations$Tx_mes <- generation_p1$input$Tx_mes
realizations$Tn_mes <- generation_p1$input$Tn_mes
# realization scanarios used for 'climdex.data.frame'
realizations_TN <- c("Tn_mes","Tn_gen00002","Tn_gen00003","Tn_gen00004")
realizations_TX <- c("Tx_mes","Tx_gen00002","Tx_gen00003","Tx_gen00004")
stations <- names(realizations$Tn_mes)
start_date = "1981-01-01"
end_date = "2010-12-31"
# The indices \link{climdex.tn90p},\link{climdex.tx90p} are considered in this example
climate_indices = c("climdex.tn90p","climdex.tx90p")
frequency = "monthly"
date.series = seq(as.PCICt(start_date, cal = "gregorian"),
as.PCICt(end_date, cal = "gregorian"), by = "days")
base.range = c(1990, 2002)
n = 5
prefix = NULL
climdex <- climdex.data.frame(data=realizations, station=stations,
realization_TN=realizations_TN,realization_TX=realizations_TX,realization_PREC=NULL,
start_date= start_date, end_date = end_date ,climate_index = climate_indices,
frequency = frequency,date.series = date.series,base.range = base.range,
n = n, prefix = prefix)
str(climdex)
## Function 'climdex.data.frame' can be also used with annual frequency
## The following lines are now commented because the elapsed time is too long!!
## Please uncomment to run the following lines to run the function.
# climdex_annual <- climdex.data.frame(data=realizations, station=stations,
# realization_TN=realizations_TN,realization_TX=realizations_TX,realization_PREC=NULL,
# start_date= start_date, end_date = end_date ,climate_index = climate_indices,
# frequency = "yearly",date.series = date.series,base.range = base.range,
# n = n, prefix = prefix)
#
# str(climdex_annual)
# Wilcoxon test between observed and generated climate indices
observed <- "T0129__Tn_mes__climdex.tx90p"
generated <- c("T0129__Tn_gen00002__climdex.tx90p","T0129__Tn_gen00003__climdex.tx90p")
wxt <- wilcox.test(x=climdex,observed=observed,generated=generated)
wxt
# Kolgomorov-Smirinov test between observed and generated climate indices
kst <- ks.test.climdex.data.frame(data=climdex,observed=observed,generated=generated)
kst
accepted(wxt)
accepted(kst)
|
Loading required package: climdex.pcic
Loading required package: PCICt
Loading required package: RMAWGEN
Loading required package: chron
Loading required package: date
Loading required package: vars
Loading required package: MASS
Loading required package: strucchange
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: urca
Loading required package: lmtest
List of 16
$ T0129__Tn_mes__climdex.tn90p : num [1:360] 0 0 22.6 50 0 ...
$ T0129__Tn_mes__climdex.tx90p : num [1:360] 6.45 0 6.45 30 3.23 ...
$ T0129__Tn_gen00002__climdex.tn90p: num [1:360] 29.03 0 9.68 6.67 6.45 ...
$ T0129__Tn_gen00002__climdex.tx90p: num [1:360] 22.6 21.4 0 10 22.6 ...
$ T0129__Tn_gen00003__climdex.tn90p: num [1:360] 12.9 50 32.26 0 6.45 ...
$ T0129__Tn_gen00003__climdex.tx90p: num [1:360] 0 25 25.8 23.3 0 ...
$ T0129__Tn_gen00004__climdex.tn90p: num [1:360] 16.13 3.57 32.26 13.33 6.45 ...
$ T0129__Tn_gen00004__climdex.tx90p: num [1:360] 6.45 0 22.58 0 16.13 ...
$ T0139__Tn_mes__climdex.tn90p : num [1:360] 0 0 25.81 53.33 6.45 ...
$ T0139__Tn_mes__climdex.tx90p : num [1:360] 0 0 3.23 30 6.45 ...
$ T0139__Tn_gen00002__climdex.tn90p: num [1:360] 38.71 0 0 10 9.68 ...
$ T0139__Tn_gen00002__climdex.tx90p: num [1:360] 25.81 28.57 0 3.33 32.26 ...
$ T0139__Tn_gen00003__climdex.tn90p: num [1:360] 9.68 50 25.81 6.67 3.23 ...
$ T0139__Tn_gen00003__climdex.tx90p: num [1:360] 0 21.43 32.26 20 3.23 ...
$ T0139__Tn_gen00004__climdex.tn90p: num [1:360] 16.13 3.57 29.03 3.33 6.45 ...
$ T0139__Tn_gen00004__climdex.tx90p: num [1:360] 9.68 3.57 29.03 0 9.68 ...
- attr(*, "row.names")= int [1:360] 1 2 3 4 5 6 7 8 9 10 ...
- attr(*, "class")= chr "climdex.data.frame"
$T0129__Tn_mes__climdex.tx90p_vs_T0129__Tn_gen00002__climdex.tx90p
Wilcoxon rank sum test with continuity correction
data: x[, observed] and x[, it]
W = 61843, p-value = 0.2848
alternative hypothesis: true location shift is not equal to 0
$T0129__Tn_mes__climdex.tx90p_vs_T0129__Tn_gen00003__climdex.tx90p
Wilcoxon rank sum test with continuity correction
data: x[, observed] and x[, it]
W = 58171, p-value = 0.01676
alternative hypothesis: true location shift is not equal to 0
Warning messages:
1: In ks.test(x = data[, observed], y = data[, it], ...) :
p-value will be approximate in the presence of ties
2: In ks.test(x = data[, observed], y = data[, it], ...) :
p-value will be approximate in the presence of ties
$T0129__Tn_mes__climdex.tx90p_vs_T0129__Tn_gen00002__climdex.tx90p
Two-sample Kolmogorov-Smirnov test
data: data[, observed] and data[, it]
D = 0.086111, p-value = 0.1385
alternative hypothesis: two-sided
$T0129__Tn_mes__climdex.tx90p_vs_T0129__Tn_gen00003__climdex.tx90p
Two-sample Kolmogorov-Smirnov test
data: data[, observed] and data[, it]
D = 0.13056, p-value = 0.004327
alternative hypothesis: two-sided
[1] "T0129__Tn_mes__climdex.tx90p_vs_T0129__Tn_gen00002__climdex.tx90p"
[1] "T0129__Tn_mes__climdex.tx90p_vs_T0129__Tn_gen00002__climdex.tx90p"
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