| Title: | Stochastic Weather Generator |
|---|---|
| Description: | An adaptation of the multi-variable stochastic weather generator proposed in 'Rglimclim' to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on reanalyses. Multi-variable dependence is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See <https://hal.archives-ouvertes.fr/hal-02554676>. |
| Authors: | Julie Carreau, Nesrine Farhani |
| Maintainer: | Nesrine Farhani <[email protected]> |
| License: | GPL (>= 2.0) |
| Version: | 0.5 |
| Built: | 2024-12-08 06:58:11 UTC |
| Source: | CRAN |
An adaptation of the multi-variable stochastic weather generator (SWG) proposed in Rglimclim to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on ERA5 reanalyses. Multivariable dependance is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See Farhani et al 2020 https://hal.archives-ouvertes.fr/hal-02554676
The DESCRIPTION file:
| Package: | MetGen |
| Type: | Package |
| Title: | Stochastic Weather Generator |
| Version: | 0.5 |
| Date: | 2020-05-29 |
| Author: | Julie Carreau, Nesrine Farhani |
| Maintainer: | Nesrine Farhani <[email protected]> |
| Description: | An adaptation of the multi-variable stochastic weather generator proposed in 'Rglimclim' to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on reanalyses. Multi-variable dependence is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See <https://hal.archives-ouvertes.fr/hal-02554676>. |
| License: | GPL (>= 2.0) |
| LazyLoad: | yes |
| LazyData: | true |
| Depends: | R (>= 3.5.0), chron, glmnet, MASS |
| URL: | www.r-project.org |
| NeedsCompilation: | no |
| Packaged: | 2020-05-29 15:14:13 UTC; farhani |
| Repository: | CRAN |
| Date/Publication: | 2020-06-04 09:40:10 UTC |
The SWG is based on generalized linear models (GLMs) for each hydro-meteorological variable with a suitable probability distribution (Normal, Gamma or Binomial) and appropriate covariates. Covariates are considered in the GLMs to account for temporal and spatial variability. The inter-variable dependencies are taken into account by including a subset of hydro-meteorological variables (excluding the one being modelled) in the covariates of the GLMs. Large-scale atmospheric variables and deterministic effects (seasonal and diurnal cycles, geographical information and temporal persistence) are also included in the covariates.
Index of help topics:
MetGen-package Stochastic Weather Generator diurnal.effect diurnal and seasonal effects fit.glm Fitting Generalized Linear Models fit.lasso Lasso regression formating.var Format variables imputation.lagged Imputation lagged.effect lag effect movave.effect Moving average myclimatic_data climatic data mycovariates Covariates data projection.lagged climatic variable simulation rm.buffer Remove buffer sim.glm Simulation of glm spatave.effect Spatial average
1- This package helps to define the probability distribution and covariates for each hydro-meteorological variables using fit.lasso function.
2- The package can be used either in a gap filling mode in which missing values in observation period are imputed using imputation.lagged function, or in a projection mode in which the generator simulates values on a period with no observations to perform temporal extension using projection.lagged function.
Julie Carreau, Nesrine Farhani
Maintainer: Nesrine Farhani <[email protected]>
Chandler, R. 2015. A multisite, multivariate daily weather generator based on Generalized Linear 9 Models. User guide:R package.
Farhani N, Carreau J, Kassouk Z, Mougenot B, Le Page M, et al.. Sub-daily stochastic weather generator based on reanalyses for water stress retrieval in central Tunisia. 2020. (hal-02554676)
Create covariates to reproduce seasonal and diurnal variability basing on the time
of the year or of the day.
Seasonal effects are expressed by cos(2*pi*d/k) and sin(2*pi*d/k),
with d is the day of the year and k=(365, 183, 96 or 30),
Diurnal effects are expressed by cos(2*pi*h/k) and sin(2*pi*h/k),
with h is the hour of the day and k=(24, 12 or 6).
diurnal.effect(var.mat, period = 24) seasonal.effect(var.mat, period = 365.25)diurnal.effect(var.mat, period = 24) seasonal.effect(var.mat, period = 365.25)
var.mat |
data frame that contains chron variable, possibly including times, geographical information for the measurement site and climatic variables for each time step, |
period |
a numeric vector that contains the period of the cycle desired, could be (183, 365, 91 or 30) for seasonal variation or (24, 12 or 6) for diurnal variation |
The same data frame defined in the arguments is returned with supplementary number of columns that contains cycle effects. The number of additional columns depends on the period defined in arguments.
##Perform cycle effects seasonal_effects <- seasonal.effect(myclimatic_data, period=c(365,183,91,30)) diurnal_effects <- diurnal.effect(myclimatic_data, period=c(24,12,6))##Perform cycle effects seasonal_effects <- seasonal.effect(myclimatic_data, period=c(365,183,91,30)) diurnal_effects <- diurnal.effect(myclimatic_data, period=c(24,12,6))
fit.glm is used to fit generalized linear models, taking account of covariates specified in the argument list
fit.glm(var.name, dep.var=NULL, geocov=TRUE, large.var, seasonal = TRUE, speriod = 365.25, diurnal = TRUE, dperiod = 24, spatave=TRUE, lagspat, movave = TRUE, bwM = 48, lagmov, spatmovave= TRUE, bwSM = 48, lagspatmov, lagvar, add.cov= FALSE, others=NULL, fam.glm = "gaussian", data)fit.glm(var.name, dep.var=NULL, geocov=TRUE, large.var, seasonal = TRUE, speriod = 365.25, diurnal = TRUE, dperiod = 24, spatave=TRUE, lagspat, movave = TRUE, bwM = 48, lagmov, spatmovave= TRUE, bwSM = 48, lagspatmov, lagvar, add.cov= FALSE, others=NULL, fam.glm = "gaussian", data)
var.name |
character that forms the name of the climatic variable to be fitted |
dep.var |
character that forms the name of depending variables |
geocov |
logical value indicating whether geographical information is part of covariates set |
large.var |
character object that forms the name of the large scale variable |
seasonal |
logical value indicating whether seasonal effects are among the covariates defined for the variable that will be fitted |
speriod |
A numeric vector that contains the lenght of seasonal cycles defined for the variable of interest |
diurnal |
logical value indicating whether diurnal effects are among the covariates defined for the variable that will be fitted |
dperiod |
A numeric vector that contain the lenght of diurnal cycles defined for the variable of interest |
spatave |
logical value indicating whether spatial average effects are among the covariates defined for the variable that will be fitted |
lagspat |
Numeric vector indicating values of lags performed for the spatial average |
movave |
logical value indicating whether moving average effects are among the covariates defined for the variable that will be fitted |
bwM |
A numeric vector that contains the bandwidth defined for the moving average |
lagmov |
Numeric vector indicating values of lags performed for the moving average |
spatmovave |
logical value indicating whether spatial moving average effects are among the covariates defined for the variable that will be fitted |
bwSM |
A numeric vector that contains the bandwidth defined for the spatial moving average |
lagspatmov |
Numeric vector indicating values of lags performed for the spatial moving average |
lagvar |
Numeric vector indicating values of lags performed for the variable |
add.cov |
logical value indicatng whether we have an additional covariates defined for the variable to fit |
others |
character that forms the name of additional covariates specified for the variable to fit |
fam.glm |
family objects to specify probability distribution that will be used for the simulation mode ("gaussian", "gaussian-hetero", "binomial" or "Gamma") |
data |
data frame that contains a variable named "dates" in chron format, possibly including times, geographical information for the measurement site, climatic variable to be fitted and all different covariates that will be used to fit the variable of interest |
value returned is a variable fitted which inherits from the class "lm"
diurnal.effect, seasonal.effect, lagged.effect
##Create a new data that contains climatic series and all effects that will be ##used as covariates for the variable to fit mat_effects <- seasonal.effect(myclimatic_data, period=c(365,183)) mat_effects <- diurnal.effect(mat_effects, period=24) mat_effects <- lagged.effect(mat_effects, "temp",2, nstat=3) ##Add a large scale variable mat_effects$t2m <- rnorm(nrow(myclimatic_data), mean=25, sd=1) temp_fitted <- fit.glm("temp", dep.var = "Rh", geocov=TRUE, large.var="t2m", seasonal = TRUE, speriod = c(365, 183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave = FALSE, spatmovave= FALSE, add.cov = FALSE, others = NULL, lagvar=2, fam.glm = "gaussian", data=mat_effects)##Create a new data that contains climatic series and all effects that will be ##used as covariates for the variable to fit mat_effects <- seasonal.effect(myclimatic_data, period=c(365,183)) mat_effects <- diurnal.effect(mat_effects, period=24) mat_effects <- lagged.effect(mat_effects, "temp",2, nstat=3) ##Add a large scale variable mat_effects$t2m <- rnorm(nrow(myclimatic_data), mean=25, sd=1) temp_fitted <- fit.glm("temp", dep.var = "Rh", geocov=TRUE, large.var="t2m", seasonal = TRUE, speriod = c(365, 183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave = FALSE, spatmovave= FALSE, add.cov = FALSE, others = NULL, lagvar=2, fam.glm = "gaussian", data=mat_effects)
Fit a generalized linear model via penalized maximum likelihood. LASSO regression is applied to perform a preliminary screening for a large covariate set. It solves a regularized least squares problem recognized for its potential to perform simultaneously variable selection and parameter estimation. It might be used only for gaussian cases to yield parsimonious solutions, i.e. with few covariates.
fit.lasso(cov.mat, var.name)fit.lasso(cov.mat, var.name)
cov.mat |
data frame that contains only the variable to be fitted, and a large set of covariates that will be tested to choose the parsimonious ones relying on Lasso coefficient estimates |
var.name |
character object that forms the name of the variable to be fitted |
an object of class "cv.glmnet" is returned, which is a list with the ingredients of the cross-validation fit.
##Create a new data that contains climatic series and all effects that will be ##used as covariates for the variable to be computed mat_effects <- seasonal.effect(myclimatic_data, period=c(365,183)) mat_effects <- diurnal.effect(mat_effects, period=24) mat_effects <- spatave.effect(mat_effects, "temp", nstat = 3, na.proc = TRUE) mat_effects <- lagged.effect(mat_effects, "temp",2, nstat=3) mat_effects$t2m <- rnorm(nrow(myclimatic_data), mean=25, sd=1) fit.lasso(mat_effects[,3:length(mat_effects)], "temp")##Create a new data that contains climatic series and all effects that will be ##used as covariates for the variable to be computed mat_effects <- seasonal.effect(myclimatic_data, period=c(365,183)) mat_effects <- diurnal.effect(mat_effects, period=24) mat_effects <- spatave.effect(mat_effects, "temp", nstat = 3, na.proc = TRUE) mat_effects <- lagged.effect(mat_effects, "temp",2, nstat=3) mat_effects$t2m <- rnorm(nrow(myclimatic_data), mean=25, sd=1) fit.lasso(mat_effects[,3:length(mat_effects)], "temp")
Extraction of a variable of interest for each site separetly
formating.var(var.mat, var.name, nstat)formating.var(var.mat, var.name, nstat)
var.mat |
data frame containing chron variable, geographical information and different climatic variables for each time step and for each site |
var.name |
character object that forms the name of the climatic variable to be picked up from the data frame according to sites |
nstat |
numeric vector that contains the number of gauged sites included in the data frame |
a new data frame is returned with time steps and temporal series of the variable of interest arranged in columns according to sites
nstat <- 3 temp_mat <- formating.var(myclimatic_data,"temp", nstat)nstat <- 3 temp_mat <- formating.var(myclimatic_data,"temp", nstat)
Gap filling on time steps with no data during the observation period.
imputation.lagged(fit, var.name, maxlag, coord, cov = NULL, seasonal = TRUE, speriod = 365.25, diurnal = TRUE, dperiod = 24, spatave = TRUE, movave = TRUE, bw = 48, na.proc = TRUE, fam.glm = "gaussian", occ.cond = NULL, init.buff = 1440)imputation.lagged(fit, var.name, maxlag, coord, cov = NULL, seasonal = TRUE, speriod = 365.25, diurnal = TRUE, dperiod = 24, spatave = TRUE, movave = TRUE, bw = 48, na.proc = TRUE, fam.glm = "gaussian", occ.cond = NULL, init.buff = 1440)
fit |
Fitted model derived from |
var.name |
Charcter object that forms the name of the climatic variable to be imputed |
maxlag |
Numeric vector that forms the maximum amount of lag defined for the fitted model |
coord |
Data frame of two columns (x and y) that contains geographical coordinates of each site |
cov |
Data frame that contains chron object and all external covariates used to fit each climatic variables that will be imputed. These covariates must be available for a buffer period in addition of the period that will be imputed. |
seasonal |
A logical value indicating whether seasonal effects are among the covariates defined for the variable that will be imputed |
speriod |
A numeric vector that contains the lenght of seasonal cycles defined for the variable of interest |
diurnal |
A logical value indicating whether diurnal effects are among the covariates defined for the variable that will be imputed |
dperiod |
A numeric vector that contains the lenght of diurnal cycles defined for the variable of interest |
spatave |
A logical value indicating whether spatial average effects are among the covariates defined for the variable that will be imputed |
movave |
A logical value indicating whether moving average effects are among the covariates defined for the variable that will be imputed |
bw |
A numeric vector that contains the bandwidth defined for the moving average |
na.proc |
A logical value indicating whether NA values should be stripped before imputation |
fam.glm |
Family objects to specify probability distribution that will be used for the simulation in the missing period ("gaussian", "gaussian-hetero", "binomial" or "Gamma") |
occ.cond |
character object that specifies the occurrence variable if that exists |
init.buff |
A buffer time is an extra time added before simulation to keep the simulation on track. the init.buffer is numeric vector, defined according to the number of climatic observations per the day and the number of few days that we choose as a buffer time before starting the simulation |
An additional column in the cov data frame of the variable of interest with no more missing values
myclimatic_data$dates=myclimatic_data$JD ##random removal of 30 percent of climatic observations to comput artificially ##missing values n.miss=round(nrow(myclimatic_data)*0.30) ind_miss=sample(nrow(myclimatic_data), n.miss) myclimatic_data$temp[ind_miss]=NA ##Create a new data that contains climatic series and all effects that will be used ##as covariates for the variable to be computed temp.effects <- seasonal.effect(myclimatic_data, period=c(365,183)) temp.effects <- diurnal.effect(temp.effects, period=24) temp.effects <- lagged.effect(temp.effects, "temp",2, nstat=3) temp.effects$t2m <- rnorm(nrow(myclimatic_data),mean=25,sd=1) coord <- data.frame(x=c(9.92,9.93,10.04),y=c(35.55,35.62,35.57)) nstat=3 init.buff=48*7 ##48 time step per day and 7 days will be considered as buffer time ##fitted variable temp.fitted <- fit.glm("temp", dep.var = NULL, geocov=TRUE, large.var="t2m", seasonal = TRUE, speriod = c(365, 183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave = FALSE, spatmovave= FALSE, lagvar=2, add.cov = FALSE, others = NULL, fam.glm = "gaussian", data= temp.effects) temp.imputation <- imputation.lagged(temp.fitted, "temp", maxlag=2, coord, cov=mycovariates, seasonal = TRUE, speriod = c(365,183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave=FALSE,bw = 0, fam.glm = "gaussian")myclimatic_data$dates=myclimatic_data$JD ##random removal of 30 percent of climatic observations to comput artificially ##missing values n.miss=round(nrow(myclimatic_data)*0.30) ind_miss=sample(nrow(myclimatic_data), n.miss) myclimatic_data$temp[ind_miss]=NA ##Create a new data that contains climatic series and all effects that will be used ##as covariates for the variable to be computed temp.effects <- seasonal.effect(myclimatic_data, period=c(365,183)) temp.effects <- diurnal.effect(temp.effects, period=24) temp.effects <- lagged.effect(temp.effects, "temp",2, nstat=3) temp.effects$t2m <- rnorm(nrow(myclimatic_data),mean=25,sd=1) coord <- data.frame(x=c(9.92,9.93,10.04),y=c(35.55,35.62,35.57)) nstat=3 init.buff=48*7 ##48 time step per day and 7 days will be considered as buffer time ##fitted variable temp.fitted <- fit.glm("temp", dep.var = NULL, geocov=TRUE, large.var="t2m", seasonal = TRUE, speriod = c(365, 183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave = FALSE, spatmovave= FALSE, lagvar=2, add.cov = FALSE, others = NULL, fam.glm = "gaussian", data= temp.effects) temp.imputation <- imputation.lagged(temp.fitted, "temp", maxlag=2, coord, cov=mycovariates, seasonal = TRUE, speriod = c(365,183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave=FALSE,bw = 0, fam.glm = "gaussian")
Introduce lag effects for variables
lagged.effect(var.mat, var.name, maxlag, nstat = NULL)lagged.effect(var.mat, var.name, maxlag, nstat = NULL)
var.mat |
A data frame containing a chron variable and different climatic variables for each time step |
var.name |
Character object that forms the name of the climatic variable to be lagged |
maxlag |
Numeric vector that specifies the maximum amount of lag requested |
nstat |
Numeric vector that specifies the number of sites considered |
lagged effect for the variable var.name, will be observed, according to the amount of maxlag defined in the data frame introduced
data_lagged4 <- lagged.effect(myclimatic_data, "temp",4, nstat=3) data_lagged8 <- lagged.effect(myclimatic_data, "temp",8, nstat=3)data_lagged4 <- lagged.effect(myclimatic_data, "temp",4, nstat=3) data_lagged8 <- lagged.effect(myclimatic_data, "temp",8, nstat=3)
Averaging previous time steps using different bandwidth to perform temporal auto-correlation
movave.effect(var.mat, var.name, bw, nstat = NULL, na.proc = FALSE)movave.effect(var.mat, var.name, bw, nstat = NULL, na.proc = FALSE)
var.mat |
Data frame containing chron variable and different climatic variables for each time step |
var.name |
character object to define the name of the climatic variable that we will perform the moving average |
bw |
Numeric vector that forms the bandwidth to be used for the moving average |
nstat |
Numeric vector specifying the number of gauged stations used to obtain observation data |
na.proc |
A logical value indicating whether NA values should be stripped before the computation proceeds |
An additional column will be added in the data frame introduced containing the average of the variable of interest over previous time step specified by the bandwith for each site
temp_movave <- movave.effect(myclimatic_data, "temp", 48, nstat = 3, na.proc = TRUE)temp_movave <- movave.effect(myclimatic_data, "temp", 48, nstat = 3, na.proc = TRUE)
A synthetic climatic data that contains time series, geographical coordinates and different climatic variables for a subdaily resolution. Each row corresponds for a time step, and each time is available for a site.
myclimatic_datamyclimatic_data
A data frame with 4320 observations on the following 6 variables.
datesa chron object to define time steps
JDa numeric vector sepcifying julian days
coord.xa numeric vector to define the x coodinates of different sites
coord.ya numeric vector to define the y coodinates of different sites
tempa numeric vector for a climatic variable (temperature)
Rha numeric vector for a climatic variable (relative humidity)
Covariates data that contains time series, geographical coordinates and different climatic variables for a subdaily resolution. Each row corresponds for a time step, and each time is available for a site.
mycovariatesmycovariates
A data frame with 4320 observations on the following 18 variables.
datesa numeric vector for the time steps
coord.xa numeric vector to define the x coodinates of different sites
coord.ya numeric vector to define the y coodinates of different sites
t2ma numeric vector for a large scale variable obtained from reanalysis
cos.365da numeric vector for the seasonal variabiliy throughout 365 days
sin.365da numeric vector for the seasonal variabiliy throughout 365 days
cos.183da numeric vector for the seasonal variabiliy throughout 183 days
sin.183da numeric vector for the seasonal variabiliy throughout 183 days
cos.91da numeric vector for the seasonal variabiliy throughout 91 days
sin.91da numeric vector for the seasonal variabiliy throughout 91 days
cos.30da numeric vector for the seasonal variabiliy throughout 30 days
sin.30da numeric vector for the seasonal variabiliy throughout 30 days
cos.24ha numeric vector for diurnal variabiliy during a day
sin.24ha numeric vector for diurnal variabiliy during a day
cos.12ha numeric vector for diurnal variabiliy during half a day
sin.12ha numeric vector for diurnal variabiliy during half a day
cos.6ha numeric vector for diurnal variabiliy during 6 hours
sin.6ha numeric vector for diurnal variabiliy during 6 hours
Projection of climatic variables for a specific period defined by the user. It forms a free simulation from fitted model constraining by covariates that must be available for the period of projection.
projection.lagged(dstart, dend, fit, var.name, maxlag, coord, cov = NULL, seasonal = TRUE, speriod = 365.25, diurnal = TRUE, dperiod = 24, spatave = TRUE, movave = TRUE, bw = 48, na.proc = TRUE, fam.glm = "gaussian", occ.cond = NULL, init.buff = 1440)projection.lagged(dstart, dend, fit, var.name, maxlag, coord, cov = NULL, seasonal = TRUE, speriod = 365.25, diurnal = TRUE, dperiod = 24, spatave = TRUE, movave = TRUE, bw = 48, na.proc = TRUE, fam.glm = "gaussian", occ.cond = NULL, init.buff = 1440)
dstart |
Numeric that defines the first time step of the simulation |
dend |
Numeric that defines the last time step of the simulation |
fit |
|
var.name |
Character that defines the name of the climatic variable to be simulated |
maxlag |
Numeric vector that specifies the maximum amount of lag defined for the fitted model |
coord |
Data frame of two columns (x and y) that contains geographical coordinates of each site |
cov |
Data frame that contains dates and all external covariates used to fit each climatic variables that will be simulated. These covariates must be available for a buffer period in addition of the period of simulation that start at "dstart" and finish at "dend". |
seasonal |
A logical value indicating whether seasonal effects are among the covariates defined for the variable that will be simulated |
speriod |
A vector that contains the lenght of seasonal cycles defined for the variable of interest |
diurnal |
A logical value indicating whether diurnal effects are among the covariates defined for the variable that will be simulated |
dperiod |
A vector that contains the lenght of diurnal cycles defined for the variable of interest |
spatave |
A logical value indicating whether spatial average effects are among the covariates defined for the variable that will be simulated |
movave |
A logical value indicating whether moving average effects are among the covariates defined for the variable that will be simulated |
bw |
A numeric vector that contains the bandwidth defined for the moving average |
na.proc |
A logical value indicating whether NA values should be stripped before simulation |
fam.glm |
family objects to specify probability distribution that will be used for the simulation mode ("gaussian", "gaussian-hetero", "binomial" or "Gamma") |
occ.cond |
Character that specify the name of the occurrence variable if that exists |
init.buff |
A buffer time is an extra time added before simulation to keep the simulation on track. the init.buffer is numeric vector, defined according to the number of climatic observations per the day and the number of few days that we choose as a buffer time before starting the simulation |
Returns a data frame that contains the covariates data frame with an additional column that contains the simulation of the variable of interest. If the covariates data frame is not specified among arguments, the function will create its own data frame of covariates that contains deterministic effects: geographical, diurnal and seasonal effects.
##Create a new data that contains climatic series and all effects that will be ##used as covariates for the variable to be projected or simulated temp.effects <- seasonal.effect(myclimatic_data, period=c(365,183)) temp.effects <- diurnal.effect(temp.effects, period=24) temp.effects <- spatave.effect(temp.effects, "temp", nstat = 3, na.proc = TRUE) temp.effects <- lagged.effect(temp.effects, "temp",2, nstat=3) temp.effects$t2m <- rnorm(length(myclimatic_data),mean=25,sd=1) coord <- data.frame(x=c(9.92,9.93,10.04),y=c(35.55,35.62,35.57)) nstat=3 init.buff=48*7 ##48 time step per day and 7 days will be considered as buffer time dstart=as.numeric(mycovariates[(init.buff*nstat)+1,1]) dend=as.numeric(mycovariates$dates[nrow(mycovariates)]) ##fitted variable temp.fitted <- fit.glm("temp", dep.var = NULL, geocov=TRUE, large.var="t2m", seasonal = TRUE, speriod = c(365, 183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave = FALSE, spatmovave= FALSE, lagvar=2, add.cov = FALSE, others = NULL, fam.glm = "gaussian", data= temp.effects) temp.projection <- projection.lagged(dstart, dend, temp.fitted, "temp", maxlag=2, coord, cov=mycovariates, seasonal = TRUE, speriod = c(365,183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave=FALSE,bw = 0, fam.glm = "gaussian", occ.cond = NULL) ##Remove buffer temp.projection <- rm.buffer(temp.projection, nstat, bi.length=init.buff)##Create a new data that contains climatic series and all effects that will be ##used as covariates for the variable to be projected or simulated temp.effects <- seasonal.effect(myclimatic_data, period=c(365,183)) temp.effects <- diurnal.effect(temp.effects, period=24) temp.effects <- spatave.effect(temp.effects, "temp", nstat = 3, na.proc = TRUE) temp.effects <- lagged.effect(temp.effects, "temp",2, nstat=3) temp.effects$t2m <- rnorm(length(myclimatic_data),mean=25,sd=1) coord <- data.frame(x=c(9.92,9.93,10.04),y=c(35.55,35.62,35.57)) nstat=3 init.buff=48*7 ##48 time step per day and 7 days will be considered as buffer time dstart=as.numeric(mycovariates[(init.buff*nstat)+1,1]) dend=as.numeric(mycovariates$dates[nrow(mycovariates)]) ##fitted variable temp.fitted <- fit.glm("temp", dep.var = NULL, geocov=TRUE, large.var="t2m", seasonal = TRUE, speriod = c(365, 183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave = FALSE, spatmovave= FALSE, lagvar=2, add.cov = FALSE, others = NULL, fam.glm = "gaussian", data= temp.effects) temp.projection <- projection.lagged(dstart, dend, temp.fitted, "temp", maxlag=2, coord, cov=mycovariates, seasonal = TRUE, speriod = c(365,183), diurnal = TRUE, dperiod = 24, spatave = FALSE, movave=FALSE,bw = 0, fam.glm = "gaussian", occ.cond = NULL) ##Remove buffer temp.projection <- rm.buffer(temp.projection, nstat, bi.length=init.buff)
Remove buffer after using it for simulation
rm.buffer(simdata, nstat, bi.length = 480)rm.buffer(simdata, nstat, bi.length = 480)
simdata |
Data frame obtained after simulation using the function |
nstat |
Numeric vector specifying the number of sites used |
bi.length |
Numeric vector that specifies the period of buffer (number of days* number of climatological observations per day) |
Data frame derived from projection.lagged that start from dstart and finish on dend
function for predictions from the results of model fitting functions (fit.glm) or
(glm)
sim.glm(fit, datapred, fam.glm = "gaussian", occ.cond = NULL)sim.glm(fit, datapred, fam.glm = "gaussian", occ.cond = NULL)
fit |
climatic variable fitted returned from "glm" which inherits from the class "lm". |
datapred |
Data frame that contains dates, climatic variables and all covariates used to fit the variable to simulate |
fam.glm |
family objects to specify probability distribution used for the model ("gaussian", "gaussian-hetero", "binomial" or "Gamma") |
occ.cond |
character object that specifies the name of the occurrence variable if that exists |
Value returned belong to the same class of its first argument
temp_fitted=glm(temp~Rh, family=gaussian, data=myclimatic_data) temp.sim=sim.glm(temp_fitted, myclimatic_data, fam.glm = "gaussian", occ.cond = NULL)temp_fitted=glm(temp~Rh, family=gaussian, data=myclimatic_data) temp.sim=sim.glm(temp_fitted, myclimatic_data, fam.glm = "gaussian", occ.cond = NULL)
Performs average value of climatic variable over different sites
spatave.effect(var.mat, var.name, nstat = NULL, na.proc = FALSE)spatave.effect(var.mat, var.name, nstat = NULL, na.proc = FALSE)
var.mat |
A data frame containing a variable named "dates" in chron format and different climatic variables for each time step and for each site |
var.name |
Character object specifying the name of the climatic variable for which we will perfom spatial average |
nstat |
Numeric vector to define the number of sites used to obtain climatic series |
na.proc |
A logical value indicating whether NA values should be stripped before the computation proceeds |
An additional column will be added in the data frame introduced to contain the average of the variable of interest over the different sites for each time step
temp_spatave <- spatave.effect(myclimatic_data, "temp", nstat = 3, na.proc = TRUE)temp_spatave <- spatave.effect(myclimatic_data, "temp", nstat = 3, na.proc = TRUE)