Training assessment: Difference between revisions

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This page is a knowledge crystal of subtype assessment. The page identifier is Op_en5677
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This is a training assessment about an imaginary, simple case. The purpose is to illustrate assessment functionalities.

Scope

Question

What decisions are worth implementing in the training assessment?

Boundaries

Time: Year 2012 - 2020

Scenarios

Factory can reduce emissions, or continue business as usual.
School can increase health education, decrease it to save money, or continue business as usual.

Intended users

Anyone who wants to learn to make open assessments.

Participants

Main participants:
- YMAL,
- Summer workers of YMAL in 2012,
- Participants of [[Decision analysis and risk management 2013}}

Answer

Conclusions

Results

Not yet available.

Rationale

Assessment-specific data

Decisions

Decisions(-)
Obs	Decisionmaker	Decision	Option	Variable	Cell	Change	Unit	Amount
1	Factory	Cleaning.policy	Reduce emissions	exposure	Year:2020	Multiply	-	0.5
2	School	Health.promotion	Increase health education	health.impact	Year:2020	Multiply	-	0.9
3	School	Health.promotion	Promotion budget reduced	health.impact	Year:2020	Multiply	-	1.1

Probabilities

Probabilities(P)
Obs	Stakeholder	Variable	Cell	Probability
1	City of Kuopio	exposure	Cleaning.policy: Reduce emissions	0.8
2	City of Kuopio	exposure	Cleaning.policy: BAU	0.2
3	Factoy	health.impact	Health.promotion: Increase health education	0.1
4	Factoy	health.impact	Health.promotion: Promotion budget reduced	0.4
5	Factoy	health.impact	Health.promotion: BAU	0.5

Endpoints

Endpoints(-)
Obs	Stakeholder	Variable	Cell	Model	Result	Description
1	City of Kuopio	health.impact	Year:2012	Weighted sum	1000
2	City of Kuopio	health.impact	Year:2020	Weighted sum	1000
3	Citizens	health.impact	Year:2012	Weighted sum	1000
4	Citizens	health.impact	Year:2020	Weighted sum	2000	Future years are twice as important.

Variables

exposure: Training exposure
health.impact: Training health impact
training.costs: Training costs

Analyses

Decision analysis on each policy: Which option minimises the health risks?
Value of information (VOI) analysis for each policy about the major variables in the model and the total VOI.

Calculations

+ Show code - Hide code

library(OpasnetUtils)
library(xtable)
library(ggplot2)


net.cost <- new("ovariable",
	name = "net.cost",
	dependencies = data.frame(
		Name = c("health.impact", "costs", "endpoints"),
		Key = c("", "", "")
	),
	formula = function(dependencies, ...) {
		ComputeDependencies(dependencies, ...)

# Using weighted sum model


		return(0)
	}
)

endpoints <- new("ovariable",
	name = "endpoints",
	data = tidy(opbase.data("Op_en5677.endpoints"), objname = "endpoints")
)

decisions <- tidy(opbase.data("Op_en5677.decisions"))

# The next row should happen inside tidy but it doesn't. Why?
decisions <- decisions[ , colnames(decisions) != "Obs"]

# BAU options are added to each combination Decisionmaker - Decision - Variable. This should be included in the DecisionTableParser.
BAUs <- decisions[!duplicated(decisions[c("Decisionmaker", "Decision", "Variable")]), ]
BAUs$Change <- "Add"
BAUs$Result <- 0
BAUs$Option <- "BAU"
decisions <- rbind(decisions, BAUs)

DecisionTableParser(decisions)

Fetch2(data.frame(Name = c("health.impact", "exposure"), Key = c("h796UrJF1UAVZp9H", "m22i7AfzxByOBaaG")))

exposure <- EvalOutput(exposure)
exposure <- CheckDecisions(exposure)
health.impact <- EvalOutput(health.impact)
health.impact <- CheckDecisions(health.impact)

summary(exposure)
summary(health.impact)

probabilities <- new("ovariable",
	name = "probabilities",
	data = tidy(opbase.data("Op_en5677.probabilities"), objname = "probabilities")
)

ggplot(health.impact@output, aes(x = Year, y = health.impactResult, colour = Health.promotion)) + 
	geom_boxplot() + 
	theme_grey(base_size = 24)

ggplot(health.impact@output, aes(x = Year, y = health.impactResult, colour = Cleaning.policy)) + 
	geom_boxplot() + 
	theme_grey(base_size = 24)

ggplot(health.impact@output, aes(x = Year, y = health.impactResult, colour = health.impactSource)) + 
	geom_boxplot() + 
	theme_grey(base_size = 24)

#########################

#endp <- function(dectable)
endpoints <- tidy(opbase.data("Op_en5677.endpoints"))
endpoints
out <- health.impact@output

# Conditions: Constructing a list of logical vectors which correspond to those rows of our new data.frame that we want to apply 
# effects on.
				
# Build multiple condition vectors that correspond to a unique decision - option combination

endpoint <- list()
removals <- c("exposureUnit", "exposureDescription", "exposureSource", "health.impactDescription", "health.impactSource",
	"health.impactUnit", "exposureResult", "health.impactResult")

for(i in unique(endpoints$Stakeholder)) {

print(i)

dectable <- endpoints[endpoints$Stakeholder == i, ]

#print(dectable)

endpoint[[i]] <- new("ovariable", name = "endpoint", output = data.frame(Result = 0))

for (j in 1:nrow(dectable)) { 
	
	# In the decision table format conditions are given in the "Cell"-column separated by ";".
				
	sel1 <- strsplit(as.character(dectable[j, "Cell"]), split = ";")[[1]] 
				
	# ":" defines index - location matches as a condition.
				
	sel2 <- strsplit(sel1, split = ":") # No need for lapply, since strsplit is a vectorized function and current list depth is 1.
				
	# Create a list of conditions which the decision and option specific condition vector consists of. 
				
	tempovar <- get(as.character(dectable[j, "Variable"]))
	temp <- tempovar@output

	for (k in 1:length(sel1)) { # For each condition separated by ";"

		if (length(sel2[[k]]) > 1) { # If ":" has been used for condition k
			locs <- strsplit(sel2[[k]][2], split = ",")[[1]] # Split by "," for multiple locs per given index
			temp <- temp[temp[, sel2[[k]][1]] %in% locs , ] # Match our data.frame to the condition
			temp <- temp[, colnames(temp) != sel2[[k]][1] ] # Remove all indices that were  used in selecting rows, because otherwise they cannot be merged.

cat(j, k, "\n")
#print(head(temp))

		} 
	}
#print(head(temp))
	# Make an ovariable out of the rows matching the condition.
	tempovar@output <- temp
#print(head(tempovar@output))	
	# Multiply by the weight and add to previous stakeholder endpoint.
#print(as.numeric(as.character(dectable[j, "Result"])))
	tempovar <- tempovar * as.numeric(as.character(dectable[j, "Result"]))
cat("Tempovar\n")
tempovar@output <- tempovar@output[ , !colnames(tempovar@output) %in% removals]
print(head(tempovar@output))	
cat("endpoint[[i]]\n")
print(head(endpoint[[i]]@output))
	endpoint[[i]] <- endpoint[[i]] + tempovar
cat("Endpoint updated\n")
print(head(endpoint[[i]]@output))

}}
endpoint

ggplot(endpoint[[1]]@output, aes(x = Health.promotion, y = Result, colour = Cleaning.policy)) + 
	geom_boxplot() + 
	theme_grey(base_size = 24)

ggplot(endpoint[[2]]@output, aes(x = Health.promotion, y = Result, colour = Cleaning.policy)) + 
	geom_boxplot() + 
	theme_grey(base_size = 24)


#Pseudocode for the rest:
# Take each Stakeholder, one at a time.
# Take each row of Endpoints table that belongs to that stakeholder.
# Make an ovariable of the data.frame that is on the jth row, renaming the Result column as jvariableResult so that it can be used in computing.
# Multiply each ovariable with Result on row j.
# Sum each ovariable that belongs to that Stakeholder.
# Give outputs as a list that has one ovariable for each Stakeholder.

References

Related files

Help pages	Wiki editing • How to edit wikipages • Quick reference for wiki editing • Drawing graphs • Opasnet policies • Watching pages • Writing formulae • Word to Wiki • Wiki editing Advanced skills
Training assessment (examples of different objects)	Training assessment • Training exposure • Training health impact • Training costs • Climate change policies and health in Kuopio • Climate change policies in Kuopio
Methods and concepts	Assessment • Variable • Method • Question • Answer • Rationale • Attribute • Decision • Result • Object-oriented programming in Opasnet • Universal object • Study • Formula • OpasnetBaseUtils • Open assessment • PSSP
Terms with changed use	Scope • Definition • Result • Tool