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#' Scenario uncertainty(DEPRECATED)
#'
#' This function performs uncertainty decomposition by scenario based on the second order interaction ANOVA model.
#' The uncertainty from interaction effect from two scenarios is divided equally and assigned to each scenario.
#' @param data a data frame containing scenarios(factor or character) for each stages and the variable of interest(numeric).
#' data should contain all combinations of scenarios.
#' @param var_name the name of the variable of interest
#' @param stages names of the stages of interest.
#' @return List of 4 elements
#' \item{summary}{summary of uncertainties}
#' \item{main_uncer}{list of which element is a vector of uncertainties from the main effects of scenarios in the corresponding stage}
#' \item{int_uncer}{list of which element is a vector of uncertainties from the interaction effects of scenarios in the corresponding stage}
#' \item{scenario_uncer}{list of which element is a vector of uncertainties of scenarios in the corresponding stage}
#' @import stats
#' @export
#' @examples
#' stage1 <- LETTERS[1:3]
#' stage2 <- LETTERS[1:2]
#' stage3 <- LETTERS[1:4]
#' y <- rnorm(3*2*4)
#' data <- expand.grid(stage1=stage1,
#' stage2=stage2,
#' stage3=stage3)
#' data <- cbind(data, y)
#' # scenario_uncertainty() is deprecated. Use UD_ANOVA_model()
#' # scenario_uncertainty(data,"y", names(data)[-4])
scenario_uncertainty<-function(data, var_name,
stages=setdiff(names(data),var_name)){
.Deprecated(old = "scenario_uncertainty", new = "UD_ANOVA_model")
nstage <- length(stages)
data[stages] <- lapply(data[stages], as.factor)
formula0 <- as.formula(paste(var_name, "~1" , sep=""))
mean_proj <- aggregate(formula0, data=data, mean)[[var_name]]
main_proj <- list()
for(j in 1:nstage){
formula0 <- as.formula(paste(var_name, "~" , stages[j], sep=""))
main_proj[[j]] <- aggregate(formula0, data=data, mean)[[var_name]]
names(main_proj[[j]]) <- levels(data[[stages[j]]])
}
names(main_proj) <- stages
main_uncer <- mapply(function(x,y) (x-y)^2/length(x), main_proj, mean_proj)
int_uncer <- list()
for(j in 1:nstage){
int_uncer[[j]] <- 0
for(k in (1:nstage)[-j]){
formula0 <- as.formula(paste(var_name, "~" , stages[j], "+", stages[k], sep=""))
inter_proj <- aggregate(formula0, data=data, mean)[[var_name]]
int_uncer0 <- ((inter_proj - main_proj[[j]] - rep(main_proj[[k]],each=nlevels(data[,j])) + mean_proj)^2)
int_uncer0 <- tapply(int_uncer0, rep(levels(data[,j]),nlevels(data[,k])) , mean)
int_uncer[[j]] <- int_uncer[[j]] + int_uncer0
}
int_uncer[[j]] <- int_uncer[[j]]/(2*nlevels(data[,j]))
}
names(int_uncer) <- stages
scenario_uncer <- Map("+",main_uncer, int_uncer)
result_table<-NULL
for(j in 1:nstage){
stage_name <- stages[j]
result_table0 <- data.frame(stage=stage_name,
scenario=levels(data[,j]),
unc_from_main=main_uncer[[j]],
unc_from_int=int_uncer[[j]],
scenario_uncer=scenario_uncer[[j]])
result_table <- rbind(result_table, result_table0)
}
rownames(result_table) <- NULL
tot_uncer <- mean((data[[var_name]]-mean(data[[var_name]]))^2)
natural_uncer <- tot_uncer - sum(result_table$scenario_uncer)
result_table0 <- data.frame(stage=c("Internal", "Total"),
scenario=c("", ""),
unc_from_main=c(0, 0),
unc_from_int=c(0, 0),
scenario_uncer=c(natural_uncer, tot_uncer))
result_table <- rbind(result_table, result_table0)
result_table$Prop <- result_table$scenario_uncer/tot_uncer
result <- list(summary=result_table,
main_uncer=main_uncer,
int_uncer=int_uncer,
scenario_uncer=scenario_uncer)
return(result)
}
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