Difference between revisions of "Training health impact"

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[[Category:Costs and valuations]]
 
[[Category:Costs and valuations]]
 
[[Category:Opasnet training]]
 
[[Category:Opasnet training]]
 +
[[Category:Contains R code]]
 
{{variable|moderator=Jouni|stub=Yes}}
 
{{variable|moderator=Jouni|stub=Yes}}
  
Line 9: Line 10:
 
== Answer ==
 
== Answer ==
  
−
There is no data; the answer is based on modelling only.
+
<rcode graphics=1>
  
−
<rcode
+
library(OpasnetUtils)
−
name="answer"
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library(ggplot2)
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label="Run code"
 
−
include="
 
−
page:Object-oriented_programming_in_Opasnet|name:answer|
 
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page:OpasnetBaseUtils|name:generic|
 
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page:Training_health_impact|name:formula
 
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"
 
−
>
 
  
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cat("Initiation successful. Now starting the model.\n")
+
objects.latest("Op_en5675", code_name = "initiate") # [[Training health impact]]
  
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library(xtable)
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health_impact <- EvalOutput(health_impact)
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out <- make.ovariable(
+
 
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data = 0,
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oprint(summary(health_impact))
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formula = formula.Op_en5675,
+
 
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dependencies = dependencies.Op_en5675)
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ggplot(health_impact@output, aes(x = health_impactResult, colour = Year)) +
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cat("Computing training exposures.\n")
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geom_density() +
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print(out)
+
facet_grid(Trait ~ Year) +
 +
theme_grey(base_size = 24)
  
 
</rcode>
 
</rcode>
  
 +
==Rationale==
  
−
== Rationale ==
+
===Data===
  
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=== Formula ===
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<t2b index="Trait" obs="Result" desc="Description" unit="cases /(µg/m3) /a">
 +
Respiratory disease|20 - 30|
 +
</t2b>
  
−
<rcode
+
===Dependencies===
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name="formula"
 
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label="Initiate functions"
 
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include="
 
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page:Object-oriented_programming_in_Opasnet|name:answer|
 
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page:OpasnetBaseUtils|name:generic|
 
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page:Training_exposure|name:formula
 
−
"
 
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variables="name:population|description:What is the size of the population|default:100000"
 
−
>
 
  
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dependencies.Op_en5675 <- list(
+
* [[Training exposure]]
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exposure = "Op_en5674", # formula.Op_en5674(dependencies.Op_en5674), # Training exposure
 
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erf = data.frame(Unit = "RR per ug/m3", Result = 1.5),
 
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population = population,
 
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background = 100 / 100000 # cases per 100000 person-years
 
−
)
 
  
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formula.Op_en5675 <- function(x) {
+
===Calculations===
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population <- make.ovariable(x$population)
 
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background <- make.ovariable(x$background)
 
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exposure <- make.ovariable(x$exposure)
 
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erf <- make.ovariable(x$erf)
 
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cases <- population * background * exp(exposure * log(erf))
 
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return(cases)
 
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}
 
  
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cat("Initiation successful. Now starting the model.\n")
+
<rcode label="Initiate ovariable" name="initiate">
  
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library(xtable)
+
library(OpasnetUtils)
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out <- make.ovariable(
 
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data = "0 - 100000",
 
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formula = formula.Op_en5675,
 
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dependencies = dependencies.Op_en5675)
 
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cat("Computing training health impact.\n")
 
  
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########### update updates the sample of an ovariable based on data and function.
+
health_impact <- Ovariable("health_impact",  
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setMethod(
+
dependencies = data.frame(Name = "exposure", Ident = "Op_en5674/initiate"), # [[Training exposure]] exposure
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f = "update",  
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formula = function(...) {
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signature = "ovariable",
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ERF <- Ovariable("ERF", ddata = "Op_en5675") # [[Training health impact]]
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definition = function(object) {
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colnames(ERF@data) <- gsub("[ \\.]", "_", colnames(ERF@data))
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dat <- data.frame(Source = "Data", interpret(object@data))
 
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dep <- object@dependencies
 
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for(i in 1:length(dep)) {
 
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if(class(dep[[i]]) == "ovariable") {
 
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dep[[i]] <- dep[[i]]@sample
 
−
} else {
 
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if(length(grep("Op_(en|fi)", dep[[i]])) > 0) {
 
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dep[[i]] <- op_baseGetData("opasnet_base", dep[[i]])}
 
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else {
 
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if(class(dep[[i]]) != "data.frame" & !is.numeric(dep[[i]])) {
 
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dep[[i]] <- get(dep[[i]])
 
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}
 
−
}
 
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}
 
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}
 
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form <- data.frame(Source = "Formula", make.ovariable(object@formula(dep))@sample)
 
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object@sample <- orbind(dat, form)@sample
 
−
return(object)
 
−
}
 
−
)
 
  
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########### make.ovariable takes a vector or data.frame and makes an ovariable out of it.
+
out <- exposure * ERF
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make.ovariable <- function(
 
−
data,
 
−
formula = function(dependencies){return(dependencies)},
 
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dependencies = list(x = 0))
 
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{if(class(data) == "ovariable") {
 
−
out <- data}
 
−
else {
 
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if(is.vector(data)) {data <- data.frame(Result = data)}
 
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cat("Showing ovariable\n")
 
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sample <- interpret(data)
 
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print(sample)
 
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out <- new("ovariable",
 
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sample = sample,
 
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data = data,
 
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marginal = ifelse(colnames(sample) %in% c("Result", "Unit"), FALSE, TRUE),
 
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formula = formula,
 
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dependencies = dependencies)
 
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# out <- update(out)
 
−
}
 
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return(out)
 
−
}
 
  
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#################### Math defines basic mathematical operations (log, exp, abs, ...) for ovariables
+
return(out)
−
setMethod(
 
−
f = "Math",
 
−
signature = signature(x = "ovariable"),
 
−
definition = function(x) {
 
−
x@sample$Result <- callGeneric(x@sample$Result)
 
−
return(x)
 
 
}
 
}
 
)
 
)
  
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############ tapply of ovariables applies a function to each cell of a ragged array, that is to each (non-empty) group of
+
objects.store(health_impact)
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############ values given by a unique combination of the levels of certain factors.
 
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### parameters (other parameters are as in generic tapply):
 
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### X an ovariable
 
  
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setMethod(f = "tapply",
+
cat("Ovariable health_impact saved.\n")
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signature = signature(X = "ovariable"),
 
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definition = function(X, INDEX, FUN = NULL, ..., simplify = TRUE) {
 
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out <- as.data.frame(as.table(tapply(X@sample$Result, INDEX, FUN, ..., simplify = TRUE)))
 
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colnames(out)[colnames(out) == "Freq"] <- "Result"
 
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X@sample <- out
 
−
print(out)
 
−
return(X)
 
−
}
 
−
)
 
−
 
 
−
print(out)
 
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out <- update(out)
 
−
print(out)
 
  
 
</rcode>
 
</rcode>
−
 
−
{{attack|# |There are logical errors in the way that data and formula of a variable are used in an assessment. Think this through!|--[[User:Jouni|Jouni]] 06:48, 15 May 2012 (EEST)}}
 
  
 
==See also==
 
==See also==
Line 168: Line 75:
  
 
==Related files==
 
==Related files==
−
 
−
{{mfiles}}
 

Latest revision as of 15:48, 24 March 2015



Question

What is the health impact in the Training assessment?

Answer

+ Show code

Rationale

Data

Difference between revisions of "Training health impact"(cases /(µg/m3) /a)
ObsTraitResultDescription
1Respiratory disease20 - 30

Dependencies

Calculations

+ Show code

See also

Materials and examples for training in Opasnet and open assessment
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


Keywords

References


Related files

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