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#'@title Prediction of occurrences of compound events
#'@description Fit the logistic regression model (LGR) based on occurrences of compound events (Y01) and climate indices (CI).The output is the predicted probability of compound event occurrence for the given climate index value CI0
#'@param CI Climate index (CI) as the driving factor of compound events (e.g., ENSO)
#'@param Y01 Occurrences of compound dry-hot events (0-1 binvary variable) (L lead time)
#'@param CI0 The antecedent climate indices (L lead time)
#'@usage PredictionLGR(CI,Y01,CI0)
#'
#'@return Probability of occurrences
#'@references Hao, Z. et al. (2019). Statistical prediction of the severity of compound dry-hot events based on ENSO . J. Hydrol., 572: 243-250.
#'@export
#'@examples
#' CI=c(-0.7,-1.2,1.3,0.7,-0.6,1.1,-0.5,0.8,0.5,-0.5,1.6,-1.8,-0.5,-1.4,-0.1,2.2,-0.7,-1.1, 0.6, -1.7)
#' Y01=c(0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0 )
#' PredictionLGR(CI,Y01,2)
PredictionLGR<-function(CI,Y01,CI0)
{
X=CI
Y=Y01
# Built the logistic regression model
logis.fit=glm(formula =Y ~ X, family = "binomial")
# Get the parameter alpha and beta
par1<-as.numeric(logis.fit$coefficients[1])
par2<-as.numeric(logis.fit$coefficients[2])
# Specify the climate index (with certain lag L)
py=1/(1+exp(-(par1+par2*CI0)))
return(py)
}
```

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