MetGen (version 0.5)

imputation.lagged: Imputation

Description

Gap filling on time steps with no data during the observation period.

Usage

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)

Arguments

fit

Fitted model derived from fit.glm

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

Value

An additional column in the cov data frame of the variable of interest with no more missing values

See Also

fit.glm, glm

Examples

# NOT RUN {
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")
# }