ComprehensiveTemperatureGenerator(RMAWGEN)
ComprehensiveTemperatureGenerator()所属R语言包：RMAWGEN
The comprehensive Temperature Generator
综合温度发生器
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The comprehensive Temperature Generator
综合温度发生器
用法----------Usage----------
ComprehensiveTemperatureGenerator(station = c("T0001", "T0010", "T0099"),
Tx_all, Tn_all, mean_climate_Tn = NULL,
mean_climate_Tx = NULL, Tx_spline = NULL,
Tn_spline = NULL, year_max = 1990, year_min = 1961,
leap = TRUE, nmonth = 12, verbose = TRUE, p = 1,
type = "none", lag.max = NULL, ic = "AIC",
activateVARselect = FALSE, year_max_sim = year_max,
year_min_sim = year_min, mean_climate_Tn_sim = NULL,
mean_climate_Tx_sim = NULL, Tn_spline_sim = NULL,
Tx_spline_sim = NULL, onlygeneration = FALSE,
varmodel = NULL, normalize = TRUE, type_quantile = 3,
sample = NULL, extremes = TRUE, option = 2,
yearly = FALSE, yearly_sim = yearly,
n_GPCA_iteration = 0,
n_GPCA_iteration_residuals = n_GPCA_iteration,
exogen = NULL, exogen_sim = exogen,
is_exogen_gaussian = FALSE, exogen_all = NULL,
exogen_all_col = station, nscenario = 1, seed = NULL)
参数----------Arguments----------
参数：station
see respective input parameter on setComprehensiveTemperatureGeneratorParameters
看到相应的输入参数setComprehensiveTemperatureGeneratorParameters
参数：Tx_all,Tn_all,mean_climate_Tn,mean_climate_Tx,Tx_spline,Tn_spline
see respective input parameter on setComprehensiveTemperatureGeneratorParameters
看到相应的输入参数setComprehensiveTemperatureGeneratorParameters
参数：year_max,year_min,leap,nmonth,verbose
see respective input parameter on setComprehensiveTemperatureGeneratorParameters
看到相应的输入参数setComprehensiveTemperatureGeneratorParameters
参数：p,type,lag.max,ic,activateVARselect
see respective input parameter on getVARmodel
看到相应的输入参数getVARmodel
参数：year_max_sim
last year of the simulation period. Default is equal to year_max
模拟期的最后一年。默认值是等于year_max
参数：year_min_sim
fist year of the simulation period. Default is equal to year_min
模拟期的第一年。默认值是等于year_min
参数：mean_climate_Tn_sim
monthly avaraged daily minimum temperatures for the simulated scenario and used by the random generator . Default is mean_climate_Tn
每月avaraged每日最低温度为模拟的情况下，和使用由随机数发生器。默认是mean_climate_Tn
参数：mean_climate_Tx_sim
monthly avaraged daily maximum temperatures for the simulated scenario and used by the random generator . Default is mean_climate_Tx
每月avaraged每日最高温度的模拟的情况下，和使用由随机数发生器。默认是mean_climate_Tx
参数：Tx_spline_sim
daily timeseries (from the first day of year_min_sim to the last day of year_max_sim) of averaged maximum temperature which can be obtained by a spline interpolation of monthly mean values (for the generation period). Default is Tx_spline.
每日的时间序列（从第一天的year_min_sim的最后一天year_max_sim）的平均最高温度，可以通过以下方式获得每月的平均值（的产生周期）样条插补。默认是Tx_spline。
参数：Tn_spline_sim
daily timeseries (from the first day of year_min_sim to the last day of year_max_sim) of averaged minimum temperature which can be obtained by a spline interpolation of monthly mean values (for the generation period). Default is Tn_spline.
每日的时间序列（从第一天的最后一天year_min_simyear_max_sim）平均的最低的温度，这可以通过以下方式获得每月的平均值（的产生周期）样条插补。默认是Tn_spline。
参数：onlygeneration
logical variable. If TRUE the VAR model varmodel is given as input and only random generation is done, otherwise (default) is calculated from measured data
逻辑变量。如果TRUE VAR模型varmodel作为输入，并只随机生成完成，否则（默认），从测得的数据计算
参数：varmodel
the VAR model as a varest2 or a GPCAvarest2 object. If NULL, it is given as input and only random generation is done, otherwise (default) is calculated from measured data
VAR模型作为一个varest2或GPCAvarest2对象。如果NULL，它是给定的输入，仅是随机生成的，否则（默认），从测得的数据计算
参数：normalize,sample,extremes
see normalizeGaussian_severalstations or setComprehensiveTemperatureGeneratorParameters
看到normalizeGaussian_severalstations或setComprehensiveTemperatureGeneratorParameters
参数：type_quantile
see type on quantile
typequantile
参数：option
integer value. If 1, the generator works with minimun and maximum temperature, if 2 (default) it works with the average value between maximum and minimum temparature and the respective daily thermal range.
整数值。如果为1，发电机工作减到最少和最大温度，如果2（默认值），它的工作原理与平均值之间的最大和最小的北部沿海区域和各自的每日热范围。
参数：n_GPCA_iteration
number of iteration of Gaussianization process for data. Default is 0 (no Gaussianization)
Gauss化的数据的过程的迭代数。默认为0（无高斯化）
参数：n_GPCA_iteration_residuals
number of iteration of Gaussianization process for data. Default is 0 (no Gaussianization)
Gauss化的数据的过程的迭代数。默认为0（无高斯化）
参数：exogen
matrix containing the (normalized or not) exogenous variables (predictors) for the recorded (calibration) period. Default is NULL
基质中含有的记录（校准）期间（或不归）外生变量（预测变量）。默认值是NULL
参数：exogen_sim
matrix containing the (normalized or not) exogenous variables (predictors) for the simulation period. Default is exogen
基质中含有（或不归）外生变量（预测变量）的模拟。默认是exogen
参数：is_exogen_gaussian
logical value, If TRUE, exogen_sim and exogen are given as already normalized variables, otherwhise they are not normalized. Default is FALSE
逻辑值，如果TRUE，exogen_sim和exogen作为已经标准化的变数，otherwhise他们不归。默认是FALSE
参数：exogen_all
data frame containing exogenous variable formatted like Tx_all and Tn_all. Default is NULL. It is alternative to exogen and if it not NULL,is_exogen_gaussian is automatically set FALSE
Tx_all和Tn_all格式的数据框包含外生变量。默认是NULL。它是替代exogen，如果它非NULL，is_exogen_gaussian自动设置FALSE
参数：exogen_all_col
vector of considered columns of exogen_all. Default is station.
矢量考虑列exogen_all。默认是station。
参数：nscenario
number of possible generated scenarios for daily maximum and minimum temperature
一些可能产生的情况下，日最高和最低温度
参数：yearly
logical value. If TRUE the monthly mean values are calculated for each year from year_min to year_max separately. Default is FALSE.
逻辑值。如果TRUE的月平均值计算每年从year_min到year_max分开。默认是FALSE。
参数：yearly_sim
logical value. If TRUE the monthly mean values are calculated for each year from year_min_sim to year_max_sim separately. Default is yearly.
逻辑值。如果TRUE的月平均值计算每年从year_min_sim到year_max_sim分开。默认是yearly。
参数：seed
seed for stochastic random generation see set.seed
种子随机随机生成set.seed
值----------Value----------
A list of the following variables:
以下变量列表：
input list of variables returned by setComprehensiveTemperatureGeneratorParameters
input的变量列表返回setComprehensiveTemperatureGeneratorParameters
var varest object containing the used VAR model (if useVAR is true), NULL (otherwise)
var的varest对象包含所使用的VAR模型（，如果useVAR是真正的），NULL（否则）
output list variables returned by generateTemperatureTimeseries (i.e. generated timeseries)
output的列表变量返回的generateTemperatureTimeseries（即生成的时间序列）
注意----------Note----------
It pre-processes series and generates multi-site temperature fields by using setComprehensiveTemperatureGeneratorParameters,getVARmodel and generateTemperatureTimeseries. Detailed examples can be viewed of this function in this presentation.
（作者）----------Author(s)----------
Emanuele Cordano, Emanuele Eccel
参见----------See Also----------
setComprehensiveTemperatureGeneratorParameters, generateTemperatureTimeseries ,generateTemperatureTimeseries.
setComprehensiveTemperatureGeneratorParameters，generateTemperatureTimeseries，generateTemperatureTimeseries。
实例----------Examples----------
data(trentino)
year_min <- 1961
year_max <- 1990
year_min_sim <- 1982
year_max_sim <- 1983
n_GPCA_iter <- 5
n_GPCA_iteration_residuals <- 5
p <- 1
vstation <- c("B2440","B6130","B8570","B9100","LAVIO","POLSA","SMICH","T0001",
"T0010","T0014","T0018","T0032","T0064","T0083","T0090","T0092","T0094","T0099",
"T0102","T0110","T0129","T0139","T0147","T0149","T0152","T0157","T0168","T0179","T0189","T0193","T0204","T0210","T0211","T0327","T0367","T0373")
generation00 <-ComprehensiveTemperatureGenerator(station=vstation[15],Tx_all=TEMPERATURE_MAX,Tn_all=TEMPERATURE_MIN,year_min=year_min,year_max=year_max,p=p,n_GPCA_iteration=n_GPCA_iter,n_GPCA_iteration_residuals=n_GPCA_iteration_residuals,sample="monthly",year_min_sim=year_min_sim,year_max_sim=year_max_sim)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-26 23:49:57
