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model parameters from the old fit are used as starting values for the
updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
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<h1>Update an mkinfit model with different arguments</h1>
<div class="hidden name"><code>update.mkinfit.Rd</code></div>
</div>
<div class="ref-description">
<p>This function will return an updated mkinfit object. The fitted degradation
model parameters from the old fit are used as starting values for the
updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
override these starting values.</p>
</div>
<pre class="usage"><span class='co'># S3 method for mkinfit</span>
<span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span>(<span class='no'>object</span>, <span class='no'>...</span>, <span class='kw'>evaluate</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>object</th>
<td><p>An mkinfit object to be updated</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Arguments to <code><a href='mkinfit.html'>mkinfit</a></code> that should replace
the arguments from the original call. Arguments set to NULL will
remove arguments given in the original call</p></td>
</tr>
<tr>
<th>evaluate</th>
<td><p>Should the call be evaluated or returned as a call</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'># \dontrun{</span>
<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"DFOP"</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span>(<span class='no'>fit</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>)</div><div class='output co'>#> $par
#> parent_0 log_k1 log_k2 g_ilr sigma_low rsd_high
#> 100.85489822 8.84468217 -2.29693632 -14.95263998 0.00375222 0.06763435
#>
#> $objective
#> [1] 19.40656
#>
#> $convergence
#> [1] 0
#>
#> $iterations
#> [1] 120
#>
#> $evaluations
#> function gradient
#> 144 847
#>
#> $message
#> [1] "relative convergence (4)"
#>
#> $logLik
#> [1] -19.40656
#>
#> $d_3_message
#> threestep
#> "Three-step fitting yielded a higher likelihood than direct fitting"
#>
#> $hessian
#> parent_0 log_k1 log_k2 g_ilr sigma_low
#> parent_0 3.662473e-01 -2.914408e-16 -7.241561e+01 -3.021629e-08 1.923504e+01
#> log_k1 -2.914408e-16 0.000000e+00 -2.215935e-13 -7.291307e-25 3.416474e-15
#> log_k2 -7.241561e+01 -2.215935e-13 3.127457e+04 6.766544e-06 -1.495826e+04
#> g_ilr -3.021629e-08 -7.291307e-25 6.766544e-06 3.122099e-09 -1.797429e-06
#> sigma_low 1.923504e+01 3.416474e-15 -1.495826e+04 -1.797429e-06 7.759299e+04
#> rsd_high 3.902119e+00 -1.801019e-16 -1.685343e+02 -3.750713e-07 3.984179e+03
#> rsd_high
#> parent_0 3.902119e+00
#> log_k1 -1.801019e-16
#> log_k2 -1.685343e+02
#> g_ilr -3.750713e-07
#> sigma_low 3.984179e+03
#> rsd_high 7.188991e+03
#>
#> $hessian_notrans
#> parent_0 k1 k2 g sigma_low
#> parent_0 3.662473e-01 -3.714445e-19 -7.201669e+02 -3.261485e+01 1.923504e+01
#> k1 -3.714445e-19 0.000000e+00 -4.258512e-15 7.218123e-21 4.355854e-18
#> k2 -7.201669e+02 -4.258512e-15 3.092510e+06 7.263235e+04 -1.456870e+05
#> g -3.261485e+01 7.218123e-21 7.263235e+04 3.291750e+03 -1.939948e+03
#> sigma_low 1.923504e+01 4.355854e-18 -1.456870e+05 -1.939948e+03 7.759299e+04
#> rsd_high 3.902119e+00 -2.259812e-19 -1.779680e+03 -4.048658e+02 3.984179e+03
#> rsd_high
#> parent_0 3.902119e+00
#> k1 -2.259812e-19
#> k2 -1.779680e+03
#> g -4.048658e+02
#> sigma_low 3.984179e+03
#> rsd_high 7.188991e+03
#>
#> $call
#> mkinfit(mkinmod = "DFOP", observed = subset(FOCUS_2006_D, value !=
#> 0), parms.ini = c(k1 = 0.699298911979803, k2 = 0.0899931270871125,
#> g = 0.0923391681138686), state.ini = c(parent = 101.948852047129),
#> quiet = TRUE, error_model = "tc")
#>
#> $error_model_algorithm
#> [1] "d_3"
#>
#> $solution_type
#> [1] "analytical"
#>
#> $transform_rates
#> [1] TRUE
#>
#> $transform_fractions
#> [1] TRUE
#>
#> $reweight.tol
#> [1] 1e-08
#>
#> $reweight.max.iter
#> [1] 10
#>
#> $control
#> $control$eval.max
#> [1] 300
#>
#> $control$iter.max
#> [1] 200
#>
#>
#> $calls
#> [1] 3105
#>
#> $time
#> User System verstrichen
#> 10.251 0.000 10.257
#>
#> $mkinmod
#> <mkinmod> model generated with
#> Use of formation fractions $use_of_ff: min
#> Specification $spec:
#> $parent
#> $type: DFOP; $sink: TRUE
#> Differential equations:
#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#> * parent
#>
#> $observed
#> name time value
#> 1 parent 0 99.46
#> 2 parent 0 102.04
#> 3 parent 1 93.50
#> 4 parent 1 92.50
#> 5 parent 3 63.23
#> 6 parent 3 68.99
#> 7 parent 7 52.32
#> 8 parent 7 55.13
#> 9 parent 14 27.27
#> 10 parent 14 26.64
#> 11 parent 21 11.50
#> 12 parent 21 11.64
#> 13 parent 35 2.85
#> 14 parent 35 2.91
#> 15 parent 50 0.69
#> 16 parent 50 0.63
#> 17 parent 75 0.05
#> 18 parent 75 0.06
#>
#> $obs_vars
#> [1] "parent"
#>
#> $predicted
#> name time value
#> 1 parent 0.0000000 100.85489822
#> 2 parent 0.7575758 93.45650191
#> 3 parent 1.0000000 91.20560523
#> 4 parent 1.5151515 86.60082860
#> 5 parent 2.2727273 80.24806580
#> 6 parent 3.0000000 74.58828105
#> 7 parent 3.0303030 74.36132158
#> 8 parent 3.7878788 68.90641029
#> 9 parent 4.5454545 63.85165403
#> 10 parent 5.3030303 59.16769870
#> 11 parent 6.0606061 54.82734352
#> 12 parent 6.8181818 50.80538306
#> 13 parent 7.0000000 49.88485755
#> 14 parent 7.5757576 47.07846089
#> 15 parent 8.3333333 43.62493394
#> 16 parent 9.0909091 40.42474681
#> 17 parent 9.8484848 37.45931528
#> 18 parent 10.6060606 34.71141842
#> 19 parent 11.3636364 32.16509858
#> 20 parent 12.1212121 29.80556871
#> 21 parent 12.8787879 27.61912649
#> 22 parent 13.6363636 25.59307475
#> 23 parent 14.0000000 24.67405211
#> 24 parent 14.3939394 23.71564776
#> 25 parent 15.1515152 21.97594287
#> 26 parent 15.9090909 20.36385722
#> 27 parent 16.6666667 18.87002909
#> 28 parent 17.4242424 17.48578345
#> 29 parent 18.1818182 16.20308170
#> 30 parent 18.9393939 15.01447489
#> 31 parent 19.6969697 13.91306051
#> 32 parent 20.4545455 12.89244241
#> 33 parent 21.0000000 12.20428157
#> 34 parent 21.2121212 11.94669362
#> 35 parent 21.9696970 11.07032198
#> 36 parent 22.7272727 10.25823818
#> 37 parent 23.4848485 9.50572628
#> 38 parent 24.2424242 8.80841627
#> 39 parent 25.0000000 8.16225872
#> 40 parent 25.7575758 7.56350125
#> 41 parent 26.5151515 7.00866672
#> 42 parent 27.2727273 6.49453311
#> 43 parent 28.0303030 6.01811471
#> 44 parent 28.7878788 5.57664485
#> 45 parent 29.5454545 5.16755983
#> 46 parent 30.3030303 4.78848399
#> 47 parent 31.0606061 4.43721595
#> 48 parent 31.8181818 4.11171583
#> 49 parent 32.5757576 3.81009336
#> 50 parent 33.3333333 3.53059697
#> 51 parent 34.0909091 3.27160354
#> 52 parent 34.8484848 3.03160906
#> 53 parent 35.0000000 2.98576554
#> 54 parent 35.6060606 2.80921981
#> 55 parent 36.3636364 2.60314433
#> 56 parent 37.1212121 2.41218590
#> 57 parent 37.8787879 2.23523557
#> 58 parent 38.6363636 2.07126576
#> 59 parent 39.3939394 1.91932426
#> 60 parent 40.1515152 1.77852870
#> 61 parent 40.9090909 1.64806147
#> 62 parent 41.6666667 1.52716489
#> 63 parent 42.4242424 1.41513691
#> 64 parent 43.1818182 1.31132694
#> 65 parent 43.9393939 1.21513214
#> 66 parent 44.6969697 1.12599389
#> 67 parent 45.4545455 1.04339454
#> 68 parent 46.2121212 0.96685442
#> 69 parent 46.9696970 0.89592904
#> 70 parent 47.7272727 0.83020652
#> 71 parent 48.4848485 0.76930520
#> 72 parent 49.2424242 0.71287140
#> 73 parent 50.0000000 0.66057741
#> 74 parent 50.7575758 0.61211954
#> 75 parent 51.5151515 0.56721639
#> 76 parent 52.2727273 0.52560719
#> 77 parent 53.0303030 0.48705031
#> 78 parent 53.7878788 0.45132184
#> 79 parent 54.5454545 0.41821430
#> 80 parent 55.3030303 0.38753542
#> 81 parent 56.0606061 0.35910705
#> 82 parent 56.8181818 0.33276409
#> 83 parent 57.5757576 0.30835357
#> 84 parent 58.3333333 0.28573373
#> 85 parent 59.0909091 0.26477320
#> 86 parent 59.8484848 0.24535028
#> 87 parent 60.6060606 0.22735216
#> 88 parent 61.3636364 0.21067432
#> 89 parent 62.1212121 0.19521992
#> 90 parent 62.8787879 0.18089921
#> 91 parent 63.6363636 0.16762901
#> 92 parent 64.3939394 0.15533228
#> 93 parent 65.1515152 0.14393759
#> 94 parent 65.9090909 0.13337879
#> 95 parent 66.6666667 0.12359454
#> 96 parent 67.4242424 0.11452804
#> 97 parent 68.1818182 0.10612662
#> 98 parent 68.9393939 0.09834151
#> 99 parent 69.6969697 0.09112749
#> 100 parent 70.4545455 0.08444266
#> 101 parent 71.2121212 0.07824822
#> 102 parent 71.9696970 0.07250818
#> 103 parent 72.7272727 0.06718920
#> 104 parent 73.4848485 0.06226042
#> 105 parent 74.2424242 0.05769319
#> 106 parent 75.0000000 0.05346100
#>
#> $rss
#> function (P)
#> cost_function(P, OLS = TRUE, update_data = FALSE)
#> <bytecode: 0x555558e48258>
#> <environment: 0x55555caa08e0>
#>
#> $ll
#> function (P, fixed_degparms = FALSE, fixed_errparms = FALSE)
#> {
#> -cost_function(P, trans = FALSE, fixed_degparms = fixed_degparms,
#> fixed_errparms = fixed_errparms, OLS = FALSE, update_data = FALSE)
#> }
#> <bytecode: 0x555558e47e30>
#> <environment: 0x55555caa08e0>
#>
#> $start
#> value type
#> parent_0 101.94885205 state
#> k1 0.69929891 deparm
#> k2 0.08999313 deparm
#> g 0.09233917 deparm
#> sigma_low 0.10000000 error
#> rsd_high 0.10000000 error
#>
#> $start_transformed
#> value lower upper
#> parent_0 101.948852 -Inf Inf
#> log_k1 -0.357677 -Inf Inf
#> log_k2 -2.408022 -Inf Inf
#> g_ilr -1.616024 -Inf Inf
#> sigma_low 0.100000 0 Inf
#> rsd_high 0.100000 0 Inf
#>
#> $fixed
#> [1] value type
#> <0 Zeilen> (oder row.names mit Länge 0)
#>
#> $data
#> time variable observed predicted residual
#> 1 0 parent 99.46 100.8548982 -1.39489822
#> 2 0 parent 102.04 100.8548982 1.18510178
#> 3 1 parent 93.50 91.2056052 2.29439477
#> 4 1 parent 92.50 91.2056052 1.29439477
#> 5 3 parent 63.23 74.5882810 -11.35828105
#> 6 3 parent 68.99 74.5882810 -5.59828105
#> 7 7 parent 52.32 49.8848576 2.43514245
#> 8 7 parent 55.13 49.8848576 5.24514245
#> 9 14 parent 27.27 24.6740521 2.59594789
#> 10 14 parent 26.64 24.6740521 1.96594789
#> 11 21 parent 11.50 12.2042816 -0.70428157
#> 12 21 parent 11.64 12.2042816 -0.56428157
#> 13 35 parent 2.85 2.9857655 -0.13576554
#> 14 35 parent 2.91 2.9857655 -0.07576554
#> 15 50 parent 0.69 0.6605774 0.02942259
#> 16 50 parent 0.63 0.6605774 -0.03057741
#> 17 75 parent 0.05 0.0534610 -0.00346100
#> 18 75 parent 0.06 0.0534610 0.00653900
#>
#> $atol
#> [1] 1e-08
#>
#> $rtol
#> [1] 1e-10
#>
#> $err_mod
#> [1] "tc"
#>
#> $bparms.optim
#> parent_0 k1 k2 g
#> 1.008549e+02 6.937399e+03 1.005665e-01 6.551046e-10
#>
#> $bparms.fixed
#> numeric(0)
#>
#> $bparms.ode
#> k1 k2 g
#> 6.937399e+03 1.005665e-01 6.551046e-10
#>
#> $bparms.state
#> parent
#> 100.8549
#>
#> $errparms
#> sigma_low rsd_high
#> 0.00375222 0.06763435
#>
#> $df.residual
#> [1] 12
#>
#> $date
#> [1] "Fri Nov 1 10:10:42 2019"
#>
#> $version
#> [1] "0.9.49.6"
#>
#> $Rversion
#> [1] "3.6.1"
#>
#> attr(,"class")
#> [1] "mkinfit" "modFit" </div><div class='input'># }
</div></pre>
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