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# SECFISH (Strengthening regional cooperation in the area of fisheries data collection #
# -Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level) #
# Functions to identify correlations between costs and transversal variables by metier using #
# individual vessel data and for disaggregating variable costs from fleet segment to metier level #
# #
# Authors: Isabella Bitetto (COISPA), Loretta Malvarosa (NISEA), Maria Teresa Spedicato (COISPA), #
# Ralf Doering (THUENEN), Joerg Berkenhagen (THUENEN) #
# #
# #
# In case of use, the Authors should be cited. If you have any comments or suggestions please #
# contact the following e-mail address: bitetto@coispa.it #
# SECFISH is believed to be reliable. #
# However, we disclaim any implied warranty. #
# #
# July 2019 #
#########################################################################################################
Detect_outliers <- function(COSTS,Fleet_segment,formula) {
#CO <- NULL
COSTS_temp=COSTS[COSTS$FS==Fleet_segment,]
mod1=glm(formula, data=COSTS_temp,family=gaussian())
# Detecting outliers
rstand<-rstandard(mod1)
plot(rstand, main="Standardized residuals")
abline(h=1.96)
abline(h=-1.96)
vessels<-COSTS_temp$vessel_ID # names(rjack)
names(rstand)=vessels
outliers=identify(1:length(rstand),rstand, vessels)
as.character(vessels[outliers])
rjack<-rstudent(mod1)
rjack
names(rjack)=vessels
plot(rjack, main="Jacknife Residuals")
abline(h=-1.96)
abline(h=1.96)
vessels<-names(rjack)
outliers=identify(1:length(rjack),rjack, vessels)
as.character(vessels[outliers])
}
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