Training assessment: Difference between revisions

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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	Factory	Cleaning.policy	Reduce emissions	training.costs	Year:2020;Expenditure:Cleaning equipment use	Add	€	50000
3	City of Kuopio	Health.promotion	Increase health education	health.impact	Year:2020	Multiply	-	0.9
4	City of Kuopio	Health.promotion	Increase health education	training.costs	Year:2020;Expenditure:Health promotion campaign	Add	€	10000
5	City of Kuopio	Health.promotion	Promotion budget reduced	health.impact	Year:2020	Multiply	-	1.1
6	City of Kuopio	Health.promotion	Promotion budget reduced	training.costs	Year:2020;Expenditure:Health promotion campaign	Add	€	-5000

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	City of Kuopio	training.costs	Year:2020	Weighted sum	1
4	Citizens	health.impact	Year:2012	Weighted sum	1000
5	Citizens	health.impact	Year:2020	Weighted sum	2000	Future years are twice as important.
6	City of Kuopio	training.costs	Year:2020	Weighted sum	1

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

dectable <- data.frame(
	Stakeholder = rep(c("School", "City of Kuopio"), 4),
	Variable = rep(c("health.impact", "exposure"), each = 2),
	Cell = c("Year:2012,2014,2020", "Year:2012"),
	Result = 2
)

health.impact <- data.frame(Year = 2011:2010, Result = 1:10)

exposure <- data.frame(Year = 2012, Result = 100:110)

#	temp <- tempovar@output[tempovar@output$health.impactSource == "Formula", ]

dectable <- dectable[dectable$Stakeholder == "School" & dectable$Variable == "health.impact", ]

#####################
##### SelectCond takes conditions and slices a data.frame leaving rows that fulfil the conditions.
##### SelectCond can be used for ovariables as well, then ovariable@output is taken as the data.frame.
##### Parameters: conditions: a data.frame with conditions for slicing. It must contain columns Variable and Cell.
###### Result and Stakeholder are optional but they are typically needed for further use.
###### Stakeholder and Variable columns must have the same values at each row, 
###### i.e. the Stakeholder-Variable pair must be unique. 
###### There must exist a data.frame or ovariable with the name found in the Variable column.

SelectCond <- function(conditions, ...) {

	temp <- get(as.character(dectable[1, "Variable"]))
	if(class(temp) == "ovariable") {temp <- temp@output}

	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. 
					
		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")


			} 
		}
	}
	return(temp)
}

SelectCond(dectable)