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Write the input files (data and specifications) for the AgeingError package

Source: R/write_files.R
write_files.Rd

Write the input files (data and specifications) for the AgeingError package

Usage

write_files(
  dat,
  dir = getwd(),
  file_dat = "data.dat",
  file_specs = "data.spc",
  minage = 0,
  maxage = NULL,
  refage = NULL,
  minusage = NULL,
  plusage = NULL,
  biasopt = NULL,
  sigopt = NULL,
  knotages = NULL
)

Arguments

dat

Dataframe or tibble with columns for each reader and rows for each age reading combination. This could either have a count column or not. If not, tally_repeats() will be called to add a count column. Order your reader/lab columns such that similar readers/labs are located next to one another because columns to the right can mirror columns to their immediate left in terms of parameter estimates.

dir

Directory where the data file will be saved.

minage

An integer, specifying the minimum possible "true" age.

maxage

An integer, specifying the maximum possible "true" age.

refage

An arbitrarily chosen age from which "true" age-composition fixed-effects are calculated as an offset. This has no effect on the answer but could potentially effect estimation speed. By default this will be set to the maxage / 4.

minusage

The minimum age for which an age-specific age-composition is estimated. Ages below minusage have "true" proportion-at-age (\(P_{a}\)) estimated as $$P_a = P_{minusage}*exp^{(\beta*(minusage - a))}$$, where beta is an estimated log-linear trend in the "true" proportion-at-age. If minusage = minage, beta is not estimated.

plusage

Identical to minusage except defining the age above with age-specific age composition is not estimated.

biasopt

A vector with one entry for each reader specifying the type of bias specific to each reader. Positive values lead to estimated parameters and negative values are used for shared parameters between readers. Parameter sharing (mirroring) is common when there is more than one reader in a lab working together to refine their methods such that they have matching techniques. If NULL is passed, the default is rep(0, nreaders) which specifies that all readers are unbiased.

Possible entries include the following:

-[0-9]+

Mirror the bias of another reader, where the negative integer corresponds to the column of the reader that is being mirrored minus one, e.g., -1 causes it to mirror reader 1. Only lower-numbered readers can be mirrored.

0

Unbiased, where at least one reader has to be unbiased.

1

Constant coefficient of variation, i.e., a 1-parameter linear relationship of bias with true age.

2

Curvilinear, i.e., a 2-parameter Hollings-form relationship of bias with true age.

An example entry for the situation where you have seven readers and you assume that the first reader is unbiased, readers 2-7 have a curvilinear bias, reader 3 shares parameters with reader 2, reader 5 shares parameters with reader 4, and reader 7 shares parameters with reader 6 would look like c(0, 2, -2, 2, -4, 2, -6).

sigopt

A vector with one entry for each reader. Each entry specifies the functional form of reading error as a function of true age. Positive values lead to estimated parameters and negative values are used for shared parameters between readers. If NULL is passed, the default is c(1, rep(-1, nreaders - 1)) which specifies a constant CV in ageing error which is shared among all readers.

Possible entries include the following:

-0-9+

Mirror the standard deviation of another reader, where the negative integer corresponds to the column of the reader that is being mirrored minus one, e.g., -1 causes it to mirror reader 1, for which data is stored in the second column of Data. This number must be lower than -1 times the current position in the vector.

0

No error. But, there could be potential bias.

1

Constant coefficient of variation, i.e., a 1-parameter linear relationship of the standard deviation with the true age.

2

Curvilinear standard deviation, i.e., a 3-parameter Hollings-form relationship of standard deviation with true age.

3

Curvilinear coefficient of variation, i.e., a 3-parameter Hollings-form relationship of coefficient of variation with true age.

5

Spline with estimated slope at beginning and end where the number of parameters is 2 + number of knots.

6

Linear interpolation with a first knot of 1 and a last knot of the maximum age, i.e., MaxAge.

knotages

Ages associated with each knot. This is a necessary input for sigopt = 5 or sigopt = 6. Not implemented in this function yet.

Value

Invisibly returns the path to the data file (file.path(dir, file_name)).

See also

load_data(), tally_repeats(), write_specs_file()

Author

Ian G. Taylor, James T. Thorson, Ian J. Stewart, Andre E. Punt

Examples

data_test <- data.frame(
  reader1 = c(7, 10, 7, 6, 6, 10, 7, 9, 8, 10, 10, 5, 6, 7, 9, 7, 7, 5, 8, 5),
  reader2 = c(8, 10, 7, 6, 6, 10, 7, 9, 8, 10, 10, 5, 6, 7, 9, 7, 7, NA, NA, NA),
  reader3 = c(7, 10, 7, 6, 6, 8, 7, 9, 8, 10, 10, 5, 6, 7, NA, NA, NA, 5, 8, 5)
)
write_files(dat = data_test, dir = tempdir())
#> ℹ Input 'dat' doesn't contain a column called 'count'; adding one via tally_repeats()
#> ℹ Total observations: 20
#> ℹ Aggregated unique combinations: 12
#> ℹ Range of observed ages in the data: 5 - 10
#> ℹ Number of readers: 3
#> ℹ Max age not specified; using 15 which is the multiple of 5 which is >120% of the observed maximum
#> ℹ Minus group set to the minimum observed age 5
#> ℹ Plus group set to the maximum observed age 10
#> ℹ Reference age not specified; using 7 = floor(median(c(minusage, plusage)))
#> ℹ Writing data file to /tmp/Rtmpk1RUGZ/data.dat
#> ℹ 'biasopt' not specified; settings all readers to unbiased
#> ℹ 'sigopt' not specified; settings all readers to share a constant CV parameter
#> ℹ Writing specifications file to /tmp/Rtmpk1RUGZ/data.spc
run(dir = tempdir())
#> 
#> ! There are some missing data; the effective sample size calculation may be dubious
#> Structure of the data set
#> Data set # Entries Reader boolean
#> ℹ Number of rows in NrowStruc 3 = 3
#> [1] "ReaderStruc"
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    3    1    0    3
#> [2,]    1   14    1    2    3
#> [3,]    1    3    1    2    0
#> 1 3 1 0 3
#> 1 14 1 2 3
#> 1 3 1 2 0
#> [1] "ReaderSumm"
#>      [,1] [,2] [,3]
#> [1,]    0    0    3
#> ReaderSumm
#> 1 2 3
#> [1] "ReaderSumm"
#>      [,1] [,2] [,3]
#> [1,]    1    2    3
#> Number of reads by data set:        3
#> Minimum and Maximum Ages:           5   10
#> 
#> total cells  256
#> [1] 89.09485
#> [1] 481.2413
#> [1] 150.0598
#> [1] 84.7052
#> [1] 74.92202
#> [1] 493.3396
#> [1] 492.6485
#> [1] 72.32722
#> [1] 70.51958
#> [1] 69.90354
#> [1] 69.35269
#> [1] 68.30155
#> [1] 64.49775
#> [1] 57.9512
#> [1] 57.28665
#> [1] 55.26544
#> [1] 53.49773
#> [1] 52.46171
#> [1] 51.69157
#> [1] 51.26842
#> [1] 50.96094
#> [1] 50.78005
#> [1] 50.71023
#> [1] 50.68466
#> [1] 50.68283
#> [1] 50.68261
#> [1] 50.68259
#> [1] 50.68252
#> [1] 50.68238
#> [1] 50.6821
#> [1] 50.68178
#> [1] 50.68161
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#> [1] 50.68156733
#> [1] "looping"
#> [1] 1e+20
#> [1] 50.68157
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#> [1] 50.68157
#> [1] 50.68157
#> [1] "Objective fn:"
#> [1] 50.68156733
#> Difference 6.252776e-13 
#> [1] 50.68157
#> [1] 50.68157
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#>              [,1]          [,2]        [,3]         [,4]         [,5]
#> [1,] -2.16439e-06 -2.253708e-08 9.59208e-09 2.581235e-08 6.473982e-09
#>               [,6]          [,7]         [,8]
#> [1,] -1.720454e-07 -2.885515e-08 1.313795e-07
#>       SDPar       Slope       Slope       Probs       Probs       Probs 
#>  0.03668531 -3.72104118 -3.73840006 -0.71364490 -0.68892753 -1.08530590 
#>       Probs       Probs 
#> -0.71204793 -0.68866006