climdex.data.frame: ClimDex Data Frame
In RClimMAWGEN: RClimMAWGEN (R Climate Index Multi-site Auto-regressive Weather GENeretor): a package to generate time series of climate indices from RMAWGEN generations.
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
View source: R/climdex.data.frame.R
Description
Create input object for clim,ete index analyis from
RMAWGEN output.
Usage
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, ...)
Arguments
data
data.frame containing realizations of weather
variables, e.g. the one retured as output by
ComprehensiveTemperatureGenerator
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 NULL if
missing.
start_date
start date yyyy-mm-dd of weather
time series
end_date
start date yyyy-mm-dd of weather
time series
climate_index
climate indices to be calculated.
The names must correspond to the name of the respective
function contained in the climdex.pcic R package
yearly
logical voalue. If TRUE (Default)
the index is calculeted yearly per each year, otherwise
the index is calculated monthly, i.e. per each month
base.range
see climdexInput.raw
n
see climdexInput.raw
prefix
name for time series on which climate
indices are calculated.
date.series
see climdexInput.raw. If
missing, it is automatically calculated from
start_date and end_date
frequency
string value. Default is
c("yearly","monthly","daily"). Set one of these,
if the climate indices are referred to each year, month
or day respectively.
freq
string value. Default is
c("default","monthly", "annual"). It has the same
role of "frequency" and is used in several
cilmdex.pcic indices. If it is omitted (Default)
the frequency is obtaind by frequency argument.
See
climdex.tn90p,climdex.tx90p
.
...
further arguments
Value
a climdex.data.frame object (see the variable
climdex in the examples.)
Author(s)
Emanuele Cordano, Annalisa Di Piazzaa
References
See Also
as.climdex.data.frame,climdexInput.raw,climdex.tn90p,climdex.tx90p
Examples
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)
Example output
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"
RClimMAWGEN documentation built on May 2, 2019, 3:38 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s) References See Also Examples
View source: R/climdex.data.frame.R
Description
Create input object for clim,ete index analyis from RMAWGEN output.
Usage
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, ...)
|
Arguments
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 |
Value
a climdex.data.frame object (see the variable
climdex in the examples.)
Author(s)
Emanuele Cordano, Annalisa Di Piazzaa
References
See Also
as.climdex.data.frame,climdexInput.raw,climdex.tn90p,climdex.tx90p
Examples
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)
|
Example output
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"
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.