Modelling in Opasnet
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Opasnet modelling environment is an open, webbased platform for collaboratively developing numerical models.
Opasnet wikiR modeling platform
Contents
Introduction
Opasnet has integrated tools for building and running easily accessible statistical models in the wiki. The platform is completely modular and individual variables are perfectly reusable.
List of main concepts:
 Universal variable structure and related principles
 Variable wiki page format
 WikiR platform
 OpasnetUtils
 Modelling practices
 Data format and uploading
Technical main features
 Wiki  pages provide a natural analogy to variables in a statistical model. They contain descriptive information as well as necessary meta data (i.e. scope).
 R  is an open sourced statistical programming language akin to for example Matlab. The Opasnet wiki has an extension (R tools) to include executable R scripts on any page. The output is displayed in html format as an applet or seperate tab.
 Database  MongoDB is used to store variable related data.
 Interfaces  All these different components obviously need to work together and we have built interface solutions for each of the combinations: Rtools for running wiki integrated R scripts, Table2Base (php) for uploading wiki tables to the database, OpasnetBase wiki extension (php) for showing database entries in the wiki and the opbase script family for communication between R and the database.
 OpasnetUtils  is an R package (library), which contains tools for building mathematical models within our modeling framework which is described below in detail. The OpasnetUtils platform is completely modular, recursive for ease of use (very simple commands can trigger the execution a complex model) and fully customizable. A knowledgeable user can take over any automated model part and resume automation for the rest of the model. Use of the package revolves around Ovariables.
 All aspects of the Opasnet Modelling environment are open source and provided free of charge.
Usage
Mathematical models consist of variables, which may be known or unknown and/or can be derived from other variables using more models. Modeling in Opasnet is variablecentric. Since variables are defined universally, they should be reusable in all other models (partly or wholly). Though naturally more complex models with extremely large data sets will need more customized and static definitions to run efficiently. In practice known variables can be defined by writing parseable tables (t2b) in the wiki pages or uploading data sets directly to the OpasnetBase and downloading them in the variable defining R code or using some existing datatools within packages installed on the Opasnet server for example ScraperWiki.
...
As discussed above, variables should be considered isolated and be defined as reusable as possible. However, multiple versions of the same variable might be desirable when the scopes of models using said variable differ. For example the population of the world by age, country, sex etc. vs population of a single country defined for a spatial grid. While data resolution is good, for the purposes of some models less specific data is sufficient. If multiple versions of a variable exist they should be listed under a specific category.
The bulk of any model is data. The main tool for storing data is the Opasnet Base. For information about the different ways of uploading data to the database please check Uploading to Opasnet Base. Any files uploaded to the Opasnet server can be accessed as well, though the preferred method is to upload straight to the database.
Formulas for latent variables are dependent on other variables which most of the time eventually lead to data. Defining data based variables is very straightforward: get all relevant information into one table and make sure there are no unnecessary columns to clutter any latent variables. The process of defining new data based variables is streamlined for well defined and formatted tables (that do not need R code to clean them), a single function call creates an ovariable, downloads relevant data from the database and saves the R object on the server.
Formulas should be described and defined on the wiki page as well as in any R code that uses them. Sometimes a standard method is used for in the formula, in which case the method page should be linked to and a short description about how the variables and the methods and their parameters match up.
Formulas are essentially functions that may return an ovariable or a data.frame. For robustness the formulas should take '...' as a parameter which causes any excess parameters to not trigger errors. Formula dependencies are defined in an ovariable slot and will be handled automatically. The dependencies can then be operated mathematically in the formula function like any other simple variable.
Scenarios, fictuous values and such should be defined separately from the variables themselves. Since scenarios and custom inputs are often model specific, they are defined at the model level and supplied upstream to be incorporated into evaluated results of variables.
Project management in Opasnet
Opasnet aims to offer a systematic workspace with tools needed to manage data all the way from original observations to public discussions and decision support. Some related pages are also listed here.
 Project Template: An R package for creating R projects. [1]
 Explainr: translates S3 objects into text using standard templates in a simple and convenient way.
 knitr: Elegant, flexible and fast dynamic report generation with R
 THL is developing EARPI data management system (entry, analysis, reporting, publishing, impact) data phases
 THL's long march (THL:n pitkä marssi)
 THL:n Haravatyökalu
 THL: hankesalkkuväline
This is a plan for an improved page structure for pages related to modelling, databases, and codes in Opasnet.
Portal:Modelling with Opasnet Main page. Contains a brief introduction and links to the content.
 Practices
 Modelling in Opasnet Primary page of modelling instructions.
 Using Opasnet in an assessment project Motivation page, not really a method.
 Producing result from rationale An attempt for a systematic approach to info and data management.
 Objectoriented programming in Opasnet A page with an outdated training session from May 2012, and a table that should be moved to Ovariable. # : Merge with Modelling in Opasnet? Jouni 17:30, 8 July 2013 (EEST)
 Open assessment An encyclopedia page about open assessment.
 Open assessment method A method page about open assessment. Should be updated with fi:Tekaisumenetelmä.
 Open Assessors' Network An encyclopedia page about Avary.
 Contributing to a discussion A method of pragmadialectic discussion.
 Uploading to Opasnet Base A page about various upload methods.
 A Tutorial on R A generic R guidance for beginners. Does not contain Opasnetspecific stuff.
 Tools
 Rtools Technical description of the Rtools structure and parameters.
 Opasnet base 2 Technical description of the Opasnet Base 2 structure.
 Opasnet base · Opasnet base structure Description and technical description of the old Opasnet Base, respectively. ⇤# : These should be redirected to new pages and permanent links put to the archive link page. Jouni 17:30, 8 July 2013 (EEST)
 OpasnetUtils # : Should contain a brief description of the package and its functionalities. Jouni 17:30, 8 July 2013 (EEST)
 OpasnetBaseUtils ⇤# : Should be archived after relevant parts have been merged with OpasnetUtils. Jouni 17:30, 8 July 2013 (EEST) ←# : Nothing relevant on there. Teemu R 19:34, 8 August 2013 (EEST)
 Saved R objects A technical list of objects saved. Needed for the function objects.latest().
 Opasnet Base Connection for R ⇤# : Should be archived. Jouni 17:30, 8 July 2013 (EEST)
 Opasnet Basic idea of Opasnet. Some parts should be moved to Contributing to Opasnet.
 Welcome to Opasnet A welcome page that should be urgently updated.
 Contributing to Opasnet Practical guidance for participation ⇤# : Actually, not very practical. Jouni 17:30, 8 July 2013 (EEST)
 Frequently asked questions about Opasnet A part of the general description.
 What is improved by Opasnet and open assessment? # : Should be merged with some page? Jouni 17:30, 8 July 2013 (EEST)
 Opasnet structure Described different parts of Opasnet ⇤# : Should be updated. Jouni 17:30, 8 July 2013 (EEST)
 Help:Opasnet policies Part of Help pages. Update in line with them, not in this process.
 Task list for Opasnet An automatic list of tasks related to Opasnet. ⇤# : Check and clean, mostly outdated tasks. Jouni 17:30, 8 July 2013 (EEST)
 Attributes Technical variable. Add performance into the table, related to rating bar, discussion page, comments etc.
 Universal objects Update to more user friendly format. · PSSP Update. Not essential reading.
 Assessment Guidance for assessment structure. Update. · Category:Assessments
 Variable Update lists like in assessment. · Category:Variables Basically just describes the structure. Not essential reading.
 Study Update lists like in assessment. · Category:Study
 Ovariable Describes the structure of an ovariable and the main use of each attribute. Essential reading for those who want to understand the concept.
 Method Update lists like in assessment. · Category:Method
Question
How should modelling be done in Opasnet in practice? This page should be a general guidance on principles, not a technical manual for using different tools.
What should be the main functionalities of Opasnet modelling environment such that
 it supports decision analysis,
 it supports BBNs and Bayesian inference,
 it mainly contains modelling functionalities for numerically describe reality but
 it is also possible to numerically describe scenarios (i.e., deliberate deviations from the truth in order to be able to compare two alternative worlds that are the same in other respect than the deliberate deviation).
Answer
For a general instruction about contributing, see Contributing to Opasnet.
Obs  Property  Guidance 

1  Structure  Answer should be a data table either on the page or uploaded to Opasnet Base using R code. 
2  Structure  The indices should logically match those of parent objects. 
3  Applicability  The question of an object should primarily be tailored according to the particular needs of the assessment under work, and only secondarily to general use. 
4  Coherence  The Answer of an object should be coherent with all information used in the object. In addition, it should be coherent with all other objects. If some information in another object affects the answer of this object, a link to the other object should be placed under Rationale, and specifically under Dependencies if there is a causal connection. 
5  Coherence  Ensuring coherence is a huge task. Therefore, simple things should be done first and more laborious only if there is a need. The order in which things should be done is usually this: a) Search for similar objects and topics in Opasnet. b) If found, make links to them in both directions. c) Discuss the related info in Rationale. d) Include the info in calculations of the Answer. e) Merge the two related objects into one larger object that contains all information from the two objects and that is internally coherent. 
6  Coherence  When you find two (or more) pieces of information about one topic, but the pieces are inconsistent, describe the Answer in this way (from simple to complex): a) Describe qualitatively what was found. b) Describe the Answer quantitatively as a list of possible hypotheses, one hypothesis for each piece of information. c) Describe the hypotheses probabilistically by giving the same probability to each hypothesis. d) Using expert judgement and/or open critical discussion, adjust probabilities to give less weight to less convincing hypotheses. e) Develop a probabilistic model that explicitly describes the hypotheses based on our understanding about topic itself and the quality of the info, and use the info as input data. 
7  Multisite assessment  When several similar assessments are to be performed for several sites, the structure of the assessments should contain a) a single page for the multisite assessment, including code that has the site name as input, b) a single summary page containing a list of all sites and their individual pages, structured as a data table, c) an individual page for each site containing a data table with all sitespecific parameter values needed in the assessment. 
8  Formula  Whenever possible, all computing code should be written in R. 
9  Formula  The same code should not be copied to several different pages. R code should provide results relevant to the question on the page. Relevant descriptions of other variables should mostly be retrieved using the dependencies hook (automatically handled ovariable slot) or objects.latest(). 
10  Formula  Some procedures repeat themselves over and over again in impact assessments. These can be written as functions. Common or important functions can be included in libraries that are available in Rtools. Search for Rtools libraries so that you learn to use the same functions as others do. 
11  Formula  When you develop your own functions with a general purpose, you should suggest your own functions to be added to an Rtools library. 
12  Preferred R code  Variables should be described as ovariables where possible. 
13  Preferred R code  Probabilistic information is incorporated in a data.frame using the Iterationcolumn, which contains the number of Monte Carlo iteration from 1 to n (samplesize). 
14  Preferred R code  The ggplot2 graphics package is recommended for drawing graphs and diagrams. 
15  Preferred R code  Uploading to and downloading from Opasnet Base is done using opbase family functions (see ?opbase) 
16  Preferred R code  The summaryfunction can be used to acquire standard summaries of variable distributions. 
17  Preferred R code  Ovariables can be mathematically operated with. 
18  Preferred R code  Ovariables can be merged (joined based on common columns) using merge() and rbound (appended) using orbind() or combine() 
Links related to the answer: Data table Opasnet Base R Parent object Child object Rtools OpasnetBaseUtils ggplot2 tapply merge data.frame rbind cbind
Note! The text talks about objects, which means any information objects. The most common objects are variables.
Relationship of Answer and Rationale
All variable pages should have a clear question and a clear answer. The answer should typically be in a form of a data table that has all indices (explanatory columns) needed to make the answer unambiguous and detailed enough. If the answer table is very large, it might be a bad idea to show it on the page; instead, a description is shown about how to calculate the answer based on Dependencies and Rationale, and only a summary of the result is shown on the page; the full answer is saved into Opasnet Base.
The answer should be a clear and concise answer to the specific question, not a general description or discussion of the topic. The answer should be understandable to anyone who has general knowledge and has read the question.
In addition, the answer should be convincing to a critical reader who reads the following data and believes it is correct:
 The Rationale section of the page.
 The Answer sections of all upstream variables listed in the Dependencies section.
 In some cases, also downstream variables may be used in inference (e.g. in hierarchical Bayes models).
It should be noted that the data mentioned above should itself be backed up by original research from several independent sources, good rationale etc. It should also be noted that ALL information that is needed to convince the reader should be put into the places mentioned and not somewhere else. In other words, when the reader has read the rationale and the relevant results, (s)he should be able to trust that s(he) is now aware of all such major points related to the specific topic that have been described in Opasnet.
This results in guidance for info producers: if there is a relevant piece of information that you are aware of but it is not mentioned, you should add it.
R help
This section contains some basic functionalities of R software as it is applied in Opasnet.
library(OpasnetUtils) # Opens the OpasnetUtils package. This is needed to use ovariables. # Define a data frame with five rows and three columns. dat < data.frame( A = 1:5, B = c("a", "b", "c", "d", "e"), Result = c(6, 45, 2, 4.5, 2) ) dat$A # The vector that forms column A dat[["A"]] # The vector that forms column A dat[[1]] # The vector that forms the first column (with name A). dat["A"] # The column with name A (this is a data frame with one column). dat[1] # The first column (with name A; this is a data frame) dat[c(1,3)] # Data frame with the first and third columns. dat[c("A", "Result")] # Data frame with the first and third columns. dat[2:4, 1:2] # Data frame with rows 2 to 4 and columns 1 to 2. dat[2:4, "Result"] # The vector that is formed from rows 2 to 4 of column Result. dat$Result[2:4] # The same as previous. dat[2:4, ]["Result"] # Data frame that first takes rows 2 to 4 and then column Result. dat[2:4, "Result", drop = FALSE] # The same as previous. Drop = TRUE (default) turns onecolumn data.frames into vectors. dat[ , colnames(dat) != "B"] # Data frame without column B. dat[ , !colnames(dat) %in% c("A", "B")] # Data frame without columns A and B. dat$B < NULL # Permanently removes column B from data frame. odat < Ovariable("odat", data = dat) # Ovariable that has dat as data. odat@data # Data slot of odat (equals the data.frame dat). odat # All slots of odat. odat < EvalOutput(odat) # Evaluate odat (i.e., calculate the output) odat@output # Output slot of odat. result(odat) # The result column (as vector) of the output slot of dat. summary(odat) # Summary of odat output. If odat is probabilistic, summary includes mean and other statistics. odat@marginal # Which columns in output are marginals? colnames(odat@output) # Names of columns of the output of odat colnames(odat@output)[odat@marginal] # Names of marginal columns.
Indices of the data table
The indices, i.e. explanatory columns, should match in variables that are causally connected by a causal diagram (i.e., mentioned in Dependencies). This does not mean that they must be the same (as not all explanations are relevant for all variables) but it should be possible to see which parts of the results of two variables belong together. An example is a geographical grid for two connected variables such as a concentration field of a pollutant and an exposure variable for a population. If the concentration and population use the same grid, the exposure is easy to compute. However, they can be used together with different grids, but then there is a need to explain how one data can be converted into the other grid for calculating exposures.
Increasing preciseness of the answer
This is a rough order of emphasis that could guide the work when starting from scratch and proceeding to highly sophisticated and precise answers. The first step always requires careful thinking, but if there are lots of easily available data, you may proceed through steps 2  4 quickly; with very little data it might be impossible to get beyond step 3.
 Describe the variables, their dependencies and their indices (explantaions) to develop a coherent and understandable structure and causal diagram.
 Describe the variables as point estimates and simple (typically linear) relations to make the first runnable model. Check that all parts make sense. Check that all units are consistent. Whether all values and results are plausible is desirable but not yet critical.
 Describe the variables as ranges in such a way that the true value is most likely within the range. This is more important than having a very precise range (and thus higher probability not covering the truth). This may result in vague conclusions (like: It might be a good idea to do this, but on the other hand, it might be a bad idea). But that's exactly how it should be: in the beginning, we should be uncertain about conclusions. Only later when we collect more data and things become more precise, also the conclusions are clarified. At this step, you can use sensitivity analyses to see where the most critical parts of your model are.
 The purpose of an assessment model is to find recommendations for actions. Except for the most clear cases, this is not possible by using variable ranges. Instead, probability distributions are needed. Then, the model can be used in optimising, i.e., finding optimal decision combinations.
 When you have your model developed this far, you can use the Value of information analysis (VOI analysis) to find the critical parts of your model. The difference to a sensitivity analysis is that a VOI analysis tests which parts would change your recommendation, not which parts would change your estimate of outcome. Often the two analyses point to the same direction, but a VOI analysis is more about what you care, while a sensitivity analysis can be performed even if no explicit decision has yet been clarified.
Memory usage in R
 [2] Hadley Wckham's introduction to memory use in R] and guidance how to use it effectively.
Spatial data in R
Bayes in Opasnet
In Rtools, we have implemented the Bayes package rjags with the JAGS engine. You can create and run hierarchical Bayesian models with it.
Bayesian modelling
 en:Bayesian linear regression
 MCMC chain analysis and convergence diagnostics with coda
 Publishable Stuff (Rasmus Bååth's Research Blog). The Bayesian Counterpart of Pearson's Correlation Test [3]
 Bayesian Estimation of Correlation  Now Robust! [4]
 Correlated Poisson variables [5]
 Introduction to Bayesian regression modelling [6]
rjags package
 runjags: Interface utilities, parallel computing methods and additional distributions for MCMC models in JAGS
 JAGS in Sourceforge
 Practical examples of rjags use
 How can I generate a plot similar to that produced by plot.bugs and plot.jags from an mcmc.list?
 Save Jags and Winbugs output in R
 Parametrization of Wishart prior in JAGS [7]
R and Hugin (a BBN software)
 RHugin [8]
 User guide
Other
 Logistic regression (nonBayes)
Examples
Life tables in R
Regression
 Loess: local polynomial regression fitting
 logistic regression
 [:en:Binomial regression]]
Lognormal and normal distributions
Rationale
A draft based on own thinking. Not even the topics are clear yet.
Montako lukiolaista tarvitaan korvaamaan 1 asiantuntija? Laske tehokas asiantuntijan opiskeluaika ja se osuus joka siitä tarvitaan ratkaisemaan kyseinen ongelma
Arvaus: 10. Asiantuntijat halveksivat pinnallista tietoa ja heillä on syvällistä. Mikä ero? Kytkennät. Jos 2 asiaa on mahdollisia mutta ei yhtaikaa, asiantuntija tunnistaa tämän mutta maallikko ei. Lukiolaisista saadaan asiantuntijoita opettamalla heille menetelmä kuvata kytkentöjä. Sen jälkeen kaiken tiedon ei tarvitse enää olla 1 ihmisen päässä.
Ihmisten on vaikea hahmottaa, että lukuisia ongelmia voidaan ratkoa kerralla samalla menetelmällä. Sen sijaan yhden ongelman ratkaisuun voidaan motivoida suuria joukkoja, jos aihe on heille tärkeä. Pitäisikö siis löytää se yksi tärkeä asia? Muut sitten alkavat ratketa vahingossa.
Vaikeaa on myös nähdä metatason kysymyksiä eli järjestelmää tai itseä osana isompaa rakennetta, jonka puitteissa ovat myös mahdolliset maailmat ja jonka sisältä ratkaisut löytyvät.
Mielikuvituksen jaloin laji on kuvitella hyviä asioita, jotka voisivat olla mutta eivät ole, sekä niiden eiolemisen ja olemisen välistä polkua.
Tieteellinen tiede on kuin amerikkalainen unelma: tieteen menetelmin tehdään riittävästi läpimurtoja jotta joka sukupolvelle riittää omat menestystarinansa ja idolinsa, mutta käytännössä tieteen metodi on liian kaukana tutkijan arjesta jotta se todella siihen vaikuttaisi. Niinpä tutkijat elävät illuusion varassa kuten amerikkalaisetkin, ja puurtavat vailla mahdollisuuksia todellisiin tavoitteisiinsa jotka ovat suurempia ja vaikuttavampia kuin mihin tieteen järjestelmä antaa mahdollisuuksia. Tutkijoiden aika ja resurssit menevät 2 asian miettimiseen: mistä saan rahaa ja miten saan ajatuksiani julkaistuksi. Sen sijaan ajatustensa itsensä kehittämiseen on aina liian vähän aikaa. Niinpä ei vain tavoitteet vaan myös kyvyt ovat suuremmat kuin mihin järjestelmä taipuu. Parhaiten pärjäävät toimitusjohtajatyypit, jotka osaavat organisoida rahankeruun, julkaisemisen ja instituutiot oman mielenkiintonsa kohteisiin.
A metaanalysis produces an esimate (with confidence intervals) for the average exposureresponse. However, we are interested in the distribution of the individual exposureresponse functions. If we know the distribution of the individual distributions, we can always produce the distribution for the average. In addition, if we assume that our sample of individuals is not a random sample from the whole population of interest, we can try to assess the subgroup's bias from the random sample, and in this way produce subpopulationspecific exposureresponse distributions. In other words, we can mimic a real study using bootstrapping (biased bootstrapping, if we have an estimate how the study is biased) from our individuallevel wholepopulation distribution; in this way, we can test whether it is plausible that the study data actually came from such a wholepopulation distribution in such a way we think. This is difficult task, but if it works, it offers a method to systematically include any studies in the same examination of individuallevel (not average) exposureresponse functions.
See also
Pages related to Opasnet Base 
Opasnet Base · Uploading to Opasnet Base · Data structures in Opasnet · Opasnet Base UI · Modelling in Opasnet · Special:Opasnet Base Import · Opasnet Base Connection for R (needs updating) · Converting KOPRA data into Opasnet Base · Poll 
Pages related to the 20082011 version of Opasnet Base 
Opasnet base connection for Analytica · Opasnet base structure · Related Analytica file (old version File:Transferring to result database.ANA) · Analytica Web Player · Removed pages and other links · Standard run · OpasnetBaseUtils 
 Contributing to Opasnet
 Welcome to Opasnet
 Open assessment
 Frequently asked questions about Opasnet
 Modelling in Opasnet
 What is improved by Opasnet and open assessment?
 Open Assessors' Network
 Opasnet structure
 Assessment
 Variable
 Ovariable
 Rtools
 Opasnet base 2
 Contributing to a discussion
 Using Opasnet in an assessment project
 Help:Opasnet policies
 Task list for Opasnet
 Producing result from rationale
 Opasnet
 Opasnet Base Connection for R
 Operating intelligently with multidimensional arrays in R
 Objectoriented programming in Opasnet
Keywords
Modelling, Opasnet Base, scenario
References
Related files
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