Difference between revisions of "Input.interp"
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| 1 - 50 ||# - # || Loguniform distribution between 1 and 50 (Lognormality is assumed if the ratio of upper to lower is => 30) || | | 1 - 50 ||# - # || Loguniform distribution between 1 and 50 (Lognormality is assumed if the ratio of upper to lower is => 30) || | ||
|---- | |---- | ||
| − | | 3.1 ± 1.2 | + | | 3.1 ± 1.2 or 3.1 +- 1.2||# ± # or # +- # ||Normal distribution with mean 3.1 and SD 1.2 || data.frame(obs=1:n, result=rnorm(n,3.1,1.2)) |
|---- | |---- | ||
| − | | 2.4 (1.8 - 3.0) || # (# - #) ||Normal distribution with mean 2.4 and 95 % confidence interval from 1.8 to 3.0 || data.frame(obs=1:n, result=rnorm(n,2.4,(3.0-1.8)/2/1.96)) | + | | 2.4 (1.8 - 3.0) || # (# - #) ||Normal distribution with mean 2.4 and 95 % confidence interval from 1.8 to 3.0 || data.frame(obs=1:n, result=rnorm(n,2.4,(3.0-1.8)/2/1.96)) |
|---- | |---- | ||
| 2.4 (2.0 - 3.2) || # (# - #) ||Lognormal distribution with mean 2.4 and 95 % confidence interval from 2.0 to 3.0. Lognormality is assumed if the difference from mean to upper limit is => 50 % greater than from mean to lower limit.|| | | 2.4 (2.0 - 3.2) || # (# - #) ||Lognormal distribution with mean 2.4 and 95 % confidence interval from 2.0 to 3.0. Lognormality is assumed if the difference from mean to upper limit is => 50 % greater than from mean to lower limit.|| | ||
|---- | |---- | ||
| − | | 24 - 35 (odds 5:1) || # - # (odds #:#) || Interpretation: odds is five to one that the truth is between 24 and 35. How to calculate this, I don't know yet, but there must be a prior. | + | | 24 - 35 (odds 5:1) || # - # (odds #:#) || Interpretation: odds is five to one that the truth is between 24 and 35. How to calculate this, I don't know yet, but there must be a prior.|| |
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Revision as of 22:50, 27 December 2011
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input.interp is an R function that interprets model inputs from a user-friendly format into explicit and exact mathematical format. The purpose is to make it easy for a user to give input without a need to worry about technical modelling details.
Question
What should be a list of important user input formats, and how should they be interpreted?
Answer
The basic feature is that if a text string can be converted to a meaningful numeric object, it will be. This function can be used when data is downloaded from Opasnet Base: if Result.Text contains this kind of numeric information, it is converted to numbers and fused with Result.
n is the number of iterations in the model. # is any numeric character in the text string.
| Example | Regular expression | Interpretation | Output in R |
|---|---|---|---|
| 12 000 | # # | 12000. Text is interpreted as number if space removal makes it a number. | as.numeric(gsub(" ", "", Result.text)) |
| 12,345 | #,# | 12.345. Commas are interpreted as decimal points. | as.numeric(gsub(",", ".", Result.text)) # Note! Do not use comma as a thousand separator! |
| -14,23 | -# | -14.23. Minus in the beginning of entry is interpreted as minus, not a sign for a range. | |
| 50 - 125 | # - # | Uniform distribution between 50 and 125 | data.frame(obs=1:n, result=runif(n,50,125)) |
| -12 345 - -23,56 | Uniform distribution between -12345 and -23.56. | ||
| 1 - 50 | # - # | Loguniform distribution between 1 and 50 (Lognormality is assumed if the ratio of upper to lower is => 30) | |
| 3.1 ± 1.2 or 3.1 +- 1.2 | # ± # or # +- # | Normal distribution with mean 3.1 and SD 1.2 | data.frame(obs=1:n, result=rnorm(n,3.1,1.2)) |
| 2.4 (1.8 - 3.0) | # (# - #) | Normal distribution with mean 2.4 and 95 % confidence interval from 1.8 to 3.0 | data.frame(obs=1:n, result=rnorm(n,2.4,(3.0-1.8)/2/1.96)) |
| 2.4 (2.0 - 3.2) | # (# - #) | Lognormal distribution with mean 2.4 and 95 % confidence interval from 2.0 to 3.0. Lognormality is assumed if the difference from mean to upper limit is => 50 % greater than from mean to lower limit. | |
| 24 - 35 (odds 5:1) | # - # (odds #:#) | Interpretation: odds is five to one that the truth is between 24 and 35. How to calculate this, I don't know yet, but there must be a prior. |
