Description Usage Arguments Value See Also Examples
It is a wrapper of predict.glm
method for the a PrecipitationOccurrenceModel
model object S3 class.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## S3 method for class 'PrecipitationOccurrenceModel'
predict(
object,
newdata = NULL,
type = "response",
previous = NULL,
endogenous = NULL,
...
)
## S3 method for class 'PrecipitationOccurrenceMultiSiteModel'
predict(object, ...)
## S3 method for class 'PrecipitationAmountModel'
predict(
object,
newdata = NULL,
origin_newdata = NA,
precipitation.value.random.generation = FALSE,
...
)
|
object |
model returned by |
newdata |
predictor or exogenous variables |
type |
see |
previous |
logical vector containing previously occurred states. |
endogenous |
String vector containing the name of the endogenous variables.
It is used if the endogenous variables are more than one, otherwise is set |
... |
further arguments |
origin_newdata |
character string containing the date corresponding the first row of |
precipitation.value.random.generation |
logical value.
If it is |
A vector or a data frame reporting predicted time series for each station.
predict.glm
,PrecipitationOccurrenceModel
predict.glm
,predict.glm
,PrecipitationOccurrenceModel
,PrecipitationAmountModel
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 71 72 73 74 75 76 77 78 79 80 81 82 83 | library(RGENERATEPREC)
data(trentino)
year_min <- 1961
year_max <- 1990
period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
period_temp <- TEMPERATURE_MAX$year>=year_min & TEMPERATURE_MAX$year<=year_max
prec_mes <- PRECIPITATION[period,]
Tx_mes <- TEMPERATURE_MAX[period_temp,]
Tn_mes <- TEMPERATURE_MIN[period_temp,]
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
acc <- TRUE
acc <- (length(which(!is.na(Tx_mes[,it])))==length(Tx_mes[,it]))
acc <- (length(which(!is.na(Tn_mes[,it])))==length(Tn_mes[,it])) & acc
accepted[it] <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it])) & acc
}
valmin <- 1.0
prec_mes <- prec_mes[,accepted]
Tx_mes <- Tx_mes[,accepted]
Tn_mes <- Tn_mes[,accepted]
origin <- paste(year_min,1,1,sep="-")
prec_occurrence_mes <- prec_mes>=valmin
station <- names(prec_mes)[!(names(prec_mes) %in% c("day","month","year"))]
it <- station[2]
vect <- Tx_mes[,it]-Tn_mes[,it]
months <- factor(prec_mes$month)
model <- PrecipitationOccurrenceModel(x=prec_mes[,it],exogen=vect,monthly.factor=months)
probs <- predict(model)
nday <- 3.0
vect_new <- array(1.0,nday)
months_new <- array(1,nday)
row_test <- 2000:2007
newdata <- model$predictor[row_test,]
probs2 <- predict(model,newdata=newdata)
probs[row_test]==probs2
###
prec_occurrence_mes <- prec_mes>=valmin
station <- names(prec_mes)[!(names(prec_mes) %in% c("day","month","year"))]
station <- station[1:4] ## reduced the dataset!!!
Tx_mes <- Tx_mes[,station]
Tn_mes <- Tn_mes[,station]
prec_mes <- prec_mes[,station]
exogen <- Tx_mes-Tn_mes
months <- factor(prec_mes$month)
### Not Run
### Please uncomment the following lines to run them
model_multisite <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,
exogen=exogen,origin=origin,multisite_type="wilks")
model_multisite_logit <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,
exogen=exogen,origin=origin,multisite_type="logit")
probs_multimodel <- predict(model_multisite_logit)
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