Computing the Doses for a given independent variable, Model and Samples
Source:R/Model-methods.R
dose.Rd
A function that computes the dose reaching a specific target value of a given variable that dose depends on. The meaning of this variable depends on the type of the model. For instance, for single agent dose escalation model or pseudo DLE (dose-limiting events)/toxicity model, this variable represents the a probability of the occurrence of a DLE. For efficacy models, it represents expected efficacy. The doses are computed based on the samples of the model parameters (samples).
Usage
dose(x, model, samples, ...)
# S4 method for numeric,LogisticNormal,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticLogNormal,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticLogNormalOrdinal,Samples
dose(x, model, samples, grade)
# S4 method for numeric,LogisticLogNormalSub,Samples
dose(x, model, samples)
# S4 method for numeric,ProbitLogNormal,Samples
dose(x, model, samples)
# S4 method for numeric,ProbitLogNormalRel,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticLogNormalGrouped,Samples
dose(x, model, samples, group)
# S4 method for numeric,LogisticKadane,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticKadaneBetaGamma,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticNormalMixture,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticNormalFixedMixture,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticLogNormalMixture,Samples
dose(x, model, samples)
# S4 method for numeric,DualEndpoint,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticIndepBeta,Samples
dose(x, model, samples)
# S4 method for numeric,LogisticIndepBeta,missing
dose(x, model)
# S4 method for numeric,Effloglog,missing
dose(x, model)
# S4 method for numeric,EffFlexi,Samples
dose(x, model, samples)
# S4 method for numeric,OneParLogNormalPrior,Samples
dose(x, model, samples)
# S4 method for numeric,OneParExpPrior,Samples
dose(x, model, samples)
Arguments
- x
(
proportion
ornumeric
)
a value of an independent variable on which dose depends. The following recycling rule applies whensamples
is not missing: vectors of size 1 will be recycled to the size of the sample (i.e.size(samples)
). Otherwise,x
must have the same size as the sample.- model
(
GeneralModel
orModelPseudo
)
the model.- samples
(
Samples
)
the samples of model's parameters that will be used to compute the resulting doses. Can also be missing for some models.- ...
model specific parameters when
samples
are not used.- grade
(
integer
)
The toxicity grade for which probabilities are required- group
(
character
orfactor
)
forLogisticLogNormalGrouped
, indicating whether to calculate the dose for themono
or for thecombo
arm.
Value
A number
or numeric
vector with the doses.
If non-scalar samples
were used, then every element in the returned vector
corresponds to one element of a sample. Hence, in this case, the output
vector is of the same length as the sample vector. If scalar samples
were
used or no samples
were used, e.g. for pseudo DLE/toxicity model
,
then the output is of the same length as the length of the prob
.
Details
The dose()
function computes the doses corresponding to a value of
a given independent variable, using samples of the model parameter(s).
If you work with multivariate model parameters, then assume that your model
specific dose()
method receives a samples matrix where the rows
correspond to the sampling index, i.e. the layout is then
nSamples x dimParameter
.
Functions
dose(x = numeric, model = LogisticNormal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormalOrdinal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).In the case of a
LogisticLogNormalOrdinal
model,dose
returns only the probability of toxicity at the given grade or higherdose(x = numeric, model = LogisticLogNormalSub, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = ProbitLogNormal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = ProbitLogNormalRel, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormalGrouped, samples = Samples)
: method forLogisticLogNormalGrouped
which needsgroup
argument in addition.dose(x = numeric, model = LogisticKadane, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticKadaneBetaGamma, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticNormalMixture, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticNormalFixedMixture, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormalMixture, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = DualEndpoint, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticIndepBeta, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticIndepBeta, samples = missing)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
). All model parameters (exceptx
) should be present in themodel
object.dose(x = numeric, model = Effloglog, samples = missing)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
). All model parameters (exceptx
) should be present in themodel
object.dose(x = numeric, model = EffFlexi, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
). For this methodx
must be a scalar.dose(x = numeric, model = OneParLogNormalPrior, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLT (x
).dose(x = numeric, model = OneParExpPrior, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLT (x
).
Note
The dose()
and prob()
methods are the inverse of each other, for
all dose()
methods for which its first argument, i.e. a given independent
variable that dose depends on, represents toxicity probability.
Examples
# Create some data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
#> Used default patient IDs!
# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)
# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = my_model, samples = my_samples)
#> [1] 31.24939 31.24939 31.24939 31.24939 31.24939 65.81736 65.81736
#> [8] 65.81736 67.86139 67.86139 62.23308 62.23308 62.23308 62.23308
#> [15] 372.54366 19.09033 19.09033 19.09033 13.11659 52.26437
# Create data from the 'Data' (or 'DataDual') class.
dlt_data <- Data(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
doseGrid = seq(from = 25, to = 300, by = 25)
)
#> Used default patient IDs!
#> Used best guess cohort indices!
# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = dlt_data
)
# Get samples from posterior.
dlt_sample <- mcmc(data = dlt_data, model = dlt_model, options = my_options)
# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = dlt_model, samples = dlt_sample)
#> [1] 10.07613 10.07613 22.15191 850756.88704 48.50700
#> [6] 48.50700 48.50700 72.06078 72.06078 72.06078
#> [11] 2590.71936 155.17055 155.17055 155.17055 212.47066
#> [16] 156.91934 234.83714 234.83714 196.82793 90.86247
dose(x = c(0.45, 0.6), model = dlt_model)
#> [1] 144.6624 247.7348
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data_ordinal, model, options)
#> Warning: Unused variable "y" in data
dose(0.25, model, samples, grade = 2L)
#> [1] 5.471249e+01 6.274467e+01 5.755990e+01 8.732931e+02 7.339021e+01
#> [6] 1.536749e+02 8.579786e+01 5.917850e+01 9.987407e+01 7.916970e+01
#> [11] 1.235223e+02 1.215594e+02 5.682301e+01 5.633238e+01 6.241323e+01
#> [16] 5.954440e+01 9.093700e+01 7.340439e+01 6.734371e+01 6.393591e+01
#> [21] 8.677500e+01 9.362346e+01 7.351127e+01 5.843568e+01 5.666215e+01
#> [26] 5.885558e+01 1.991989e+02 7.402684e+01 5.919688e+01 5.888987e+01
#> [31] 5.746445e+01 6.128473e+01 6.126816e+01 1.520125e+02 6.618725e+01
#> [36] 6.599577e+01 3.705765e+02 7.068865e+01 8.386893e+01 5.824234e+01
#> [41] 1.210908e+02 5.678618e+01 7.313807e+01 7.103216e+01 6.193609e+01
#> [46] 6.512503e+01 6.099387e+01 1.005744e+02 9.498238e+01 5.691405e+01
#> [51] 6.142971e+01 5.750216e+01 7.450503e+01 6.252057e+01 5.410985e+01
#> [56] 5.728992e+01 6.979539e+01 6.751410e+01 7.056267e+01 5.109952e+01
#> [61] 3.615639e+02 4.138154e+02 1.227778e+02 5.112541e+01 5.294917e+01
#> [66] 7.357773e+01 5.475822e+01 6.649474e+01 1.694503e+02 6.043149e+01
#> [71] 5.363777e+01 8.048344e+01 5.558980e+01 5.702760e+01 5.975595e+01
#> [76] 5.869459e+01 7.005773e+01 6.345415e+01 6.492896e+01 6.765556e+01
#> [81] 6.680221e+01 1.175711e+02 5.732469e+01 6.360891e+01 8.953854e+01
#> [86] 6.870178e+01 6.369579e+01 6.310952e+01 7.415122e+01 1.340466e+02
#> [91] 7.649338e+01 5.915547e+01 6.792705e+01 6.765435e+01 5.897493e+01
#> [96] 5.975530e+01 6.694258e+01 8.736757e+01 6.719981e+01 6.371545e+01
#> [101] 5.776476e+01 5.768789e+01 6.798574e+01 5.380604e+01 6.467000e+01
#> [106] 5.676446e+01 6.337946e+01 6.018299e+01 6.463861e+01 5.913661e+01
#> [111] 6.138521e+01 6.638778e+01 5.859406e+01 9.957059e+01 6.166677e+01
#> [116] 9.530791e+01 6.423986e+01 6.241350e+01 6.331197e+01 7.820592e+01
#> [121] 6.250887e+01 5.898168e+01 8.063174e+01 1.120821e+02 1.031834e+03
#> [126] 1.182209e+02 7.173269e+01 6.765227e+01 8.043598e+01 7.487608e+01
#> [131] 1.517081e+02 7.430629e+01 8.686568e+01 8.320804e+01 1.403260e+02
#> [136] 6.190760e+01 5.873992e+01 6.940219e+01 7.715120e+01 1.463985e+02
#> [141] 5.769330e+01 7.144360e+01 7.461989e+01 8.433361e+01 8.142527e+01
#> [146] 8.948659e+01 6.661932e+01 6.824198e+01 5.905042e+01 6.003344e+01
#> [151] 1.331064e+02 5.032283e+01 7.448516e+01 6.483458e+01 6.637781e+01
#> [156] 9.827560e+01 5.417512e+01 1.056081e+02 6.145999e+01 5.413610e+01
#> [161] 6.561440e+01 6.929665e+01 5.543248e+01 6.369405e+01 6.435946e+01
#> [166] 5.563008e+01 5.234368e+01 6.111448e+01 6.513351e+01 8.012538e+01
#> [171] 5.165869e+01 5.920587e+01 5.822389e+01 6.096715e+01 5.870079e+01
#> [176] 5.965962e+01 6.158338e+01 6.525789e+01 2.254627e+02 6.466483e+01
#> [181] 7.667287e+01 1.152632e+02 7.374361e+01 4.590962e+01 5.634947e+01
#> [186] 5.835231e+01 5.903592e+01 7.535457e+01 6.637551e+01 8.637604e+01
#> [191] 7.174719e+01 6.318224e+01 5.835824e+01 6.835164e+01 6.995315e+01
#> [196] 6.628842e+01 5.645999e+01 8.595576e+01 8.924402e+01 1.278869e+02
#> [201] 5.706556e+01 1.069283e+02 5.668917e+01 6.852204e+01 8.040985e+01
#> [206] 6.010515e+01 5.833451e+01 7.952568e+01 8.645211e+01 7.828730e+01
#> [211] 1.241818e+02 6.830986e+01 5.193404e+02 2.891220e+02 5.108962e+01
#> [216] 5.544742e+01 6.063914e+01 6.505150e+01 8.959852e+01 6.804982e+01
#> [221] 7.158748e+01 5.789942e+01 6.418813e+01 5.718611e+01 5.259174e+01
#> [226] 7.721239e+01 9.446085e+01 5.858278e+01 6.147139e+01 1.689409e+02
#> [231] 6.360273e+01 5.632315e+01 6.216656e+01 7.166251e+01 5.693507e+01
#> [236] 2.144365e+03 8.972679e+01 6.177479e+01 6.445736e+01 7.905451e+01
#> [241] 5.395868e+01 7.126669e+01 2.983260e+02 6.986310e+01 7.316397e+01
#> [246] 7.549053e+01 9.732908e+01 5.233623e+01 1.183099e+02 7.696606e+01
#> [251] 7.273636e+01 5.631926e+01 7.004045e+01 4.336177e+01 6.699417e+01
#> [256] 9.354563e+01 5.954906e+01 9.216468e+01 6.882851e+01 8.431622e+01
#> [261] 6.366300e+01 5.920025e+01 5.560773e+01 6.367949e+01 6.593437e+01
#> [266] 5.612531e+01 6.106236e+01 5.843137e+01 5.946811e+01 8.714845e+01
#> [271] 7.426264e+01 5.898489e+01 6.147311e+01 2.915501e+02 4.802337e+01
#> [276] 8.424680e+01 5.726527e+01 6.074442e+01 1.009708e+02 9.310102e+01
#> [281] 1.633221e+02 1.529609e+02 8.309490e+01 5.404719e+01 8.322910e+01
#> [286] 6.441433e+01 1.236871e+02 6.231800e+01 5.640606e+01 6.187082e+01
#> [291] 6.279735e+01 7.149663e+01 6.280249e+01 6.886334e+01 9.088025e+01
#> [296] 6.801679e+01 6.063006e+01 7.204992e+01 8.585851e+01 5.728456e+01
#> [301] 7.622056e+01 1.842858e+02 6.070597e+01 8.452461e+01 5.590029e+01
#> [306] 9.574843e+01 7.781976e+01 5.526545e+01 1.127764e+02 6.048658e+01
#> [311] 6.232407e+01 1.055788e+02 6.044891e+01 1.442632e+02 5.394332e+01
#> [316] 7.077989e+01 5.789097e+01 7.228285e+01 1.142165e+02 5.398575e+01
#> [321] 1.455196e+02 8.211535e+01 5.607072e+01 9.625508e+01 7.945387e+01
#> [326] 5.639144e+01 5.459649e+01 5.864155e+01 8.637528e+01 6.555941e+01
#> [331] 6.777977e+01 6.310082e+01 6.104640e+01 5.866863e+01 1.214435e+02
#> [336] 6.998146e+01 7.407053e+01 1.517547e+05 1.028959e+08 2.000152e+04
#> [341] 1.008743e+02 1.158182e+02 9.341541e+01 8.116016e+01 6.809060e+01
#> [346] 6.536153e+01 5.499150e+01 6.687286e+01 6.580146e+01 6.707495e+01
#> [351] 6.045563e+01 7.410571e+01 6.025728e+01 5.969374e+01 1.146371e+02
#> [356] 6.629214e+01 5.874323e+01 6.271302e+01 6.933498e+01 5.954108e+01
#> [361] 6.378592e+01 7.450983e+01 5.249017e+01 5.346393e+01 5.581829e+01
#> [366] 6.334058e+01 8.502258e+01 6.476369e+01 5.841084e+01 6.805300e+01
#> [371] 6.715558e+01 6.178778e+01 6.518268e+01 6.932535e+01 5.958123e+01
#> [376] 6.481892e+01 6.166919e+01 6.577290e+01 6.902845e+01 6.161162e+01
#> [381] 6.253790e+01 8.170868e+01 2.035619e+02 5.741490e+01 5.820703e+01
#> [386] 5.972265e+01 5.932647e+01 6.255284e+01 7.913649e+01 6.063922e+01
#> [391] 5.745414e+01 6.633690e+01 7.274094e+01 1.226675e+02 2.284720e+02
#> [396] 6.118330e+01 5.547904e+01 6.296892e+01 7.588804e+01 6.479632e+01
#> [401] 5.649575e+01 6.075299e+01 1.040153e+02 8.715155e+01 6.583231e+01
#> [406] 7.384944e+01 6.006970e+01 6.308632e+01 9.222666e+01 7.808893e+01
#> [411] 6.202133e+01 6.310127e+01 6.011008e+01 5.868183e+01 5.886108e+01
#> [416] 5.800869e+01 6.772332e+01 7.859002e+01 5.736206e+01 8.045221e+01
#> [421] 6.301471e+01 1.697901e+02 7.188006e+01 6.433492e+01 5.314188e+01
#> [426] 1.048444e+02 7.181308e+01 5.292724e+01 7.325215e+01 6.104054e+01
#> [431] 7.287355e+01 3.306542e+02 7.268547e+01 5.768826e+01 5.704943e+01
#> [436] 5.466377e+01 5.910184e+01 8.016108e+01 1.479193e+02 1.317079e+02
#> [441] 1.199526e+02 7.249199e+01 8.331432e+01 6.182041e+01 6.629412e+01
#> [446] 7.207231e+01 6.190123e+01 6.617078e+01 5.694420e+01 5.658498e+01
#> [451] 5.950586e+01 3.496769e+02 9.365668e+01 5.366286e+01 1.334804e+02
#> [456] 7.509573e+01 1.162932e+02 6.423056e+01 1.048103e+02 7.387315e+01
#> [461] 9.525920e+01 9.984231e+01 5.705358e+01 5.979162e+01 5.982943e+01
#> [466] 6.041000e+01 6.265739e+01 5.668557e+01 5.856701e+01 5.982461e+01
#> [471] 6.070977e+01 6.195204e+01 1.270824e+02 6.679486e+01 7.214937e+01
#> [476] 6.085826e+01 5.913253e+01 6.743971e+01 5.705526e+01 5.944592e+01
#> [481] 6.320393e+01 6.145683e+01 1.422827e+02 6.406806e+01 6.203258e+01
#> [486] 5.953817e+01 5.827164e+01 7.218559e+01 1.200525e+02 1.082935e+02
#> [491] 5.473913e+01 6.469475e+01 7.013430e+01 6.014664e+01 6.270119e+01
#> [496] 1.604627e+02 1.176855e+02 6.070566e+01 7.269995e+01 7.264003e+01
#> [501] 5.773699e+01 6.299786e+01 5.650772e+01 8.171022e+01 8.554504e+01
#> [506] 7.757771e+01 6.509076e+01 6.719667e+01 1.000091e+02 6.380868e+01
#> [511] 6.375054e+01 6.588382e+01 1.068787e+02 6.023777e+01 7.372581e+01
#> [516] 2.340861e+02 1.054105e+03 3.200189e+02 6.529526e+01 5.454504e+01
#> [521] 5.167239e+01 5.499637e+01 5.599420e+01 1.049066e+02 6.182447e+01
#> [526] 6.270931e+01 2.089492e+02 1.021789e+02 8.144764e+01 6.099907e+01
#> [531] 8.969366e+01 6.161437e+01 6.076584e+01 6.445816e+01 5.803425e+01
#> [536] 5.700843e+01 7.428558e+01 7.233873e+01 1.395333e+02 1.082257e+02
#> [541] 6.247939e+01 7.001948e+01 1.082129e+02 9.677364e+01 8.362623e+01
#> [546] 2.854930e+02 6.827674e+01 5.434877e+01 8.360116e+01 9.065910e+01
#> [551] 6.776790e+01 8.668038e+01 6.408719e+01 3.272671e+03 6.261611e+01
#> [556] 8.052303e+01 4.852749e+01 7.141419e+01 7.486394e+01 6.687409e+01
#> [561] 6.620458e+01 6.482226e+01 6.227152e+01 7.466290e+01 5.988755e+01
#> [566] 6.393265e+01 6.741553e+01 5.741569e+01 7.568867e+01 5.355870e+01
#> [571] 6.470601e+01 6.359459e+01 6.534785e+01 5.556299e+01 6.132184e+01
#> [576] 7.483479e+01 6.482312e+01 5.618202e+01 6.862444e+01 8.388918e+01
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#> [766] 7.393146e+01 6.579338e+01 6.821428e+01 5.687109e+01 5.972497e+01
#> [771] 1.281560e+02 6.399374e+01 6.005693e+01 6.395053e+01 1.457290e+02
#> [776] 6.278060e+01 2.204875e+02 6.243989e+01 8.509808e+01 6.532319e+01
#> [781] 6.316485e+01 6.262339e+01 5.412622e+01 7.411160e+01 8.834117e+01
#> [786] 6.086303e+01 7.845221e+01 6.493700e+01 6.399244e+01 6.290445e+01
#> [791] 8.535547e+01 5.812351e+01 5.909238e+01 5.795279e+01 7.429360e+01
#> [796] 8.425704e+01 5.878773e+01 5.575679e+01 5.857010e+01 9.694323e+01
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#> [811] 5.430738e+01 6.199376e+01 7.887491e+01 1.422728e+02 6.185725e+01
#> [816] 9.582264e+01 7.447178e+01 5.867365e+01 5.810387e+01 7.209021e+01
#> [821] 6.537291e+01 6.839401e+01 7.995627e+01 7.135050e+01 6.681679e+01
#> [826] 6.910311e+01 5.548350e+01 5.777583e+01 6.233473e+01 6.290716e+01
#> [831] 6.818520e+01 5.556108e+01 7.733257e+01 1.285394e+02 6.693703e+01
#> [836] 6.354916e+01 1.264220e+02 9.316275e+01 7.002234e+01 2.492292e+02
#> [841] 6.200074e+01 6.371930e+01 5.797842e+01 2.003381e+02 6.865142e+01
#> [846] 9.484536e+01 1.668316e+02 5.475268e+01 5.989174e+01 6.391194e+01
#> [851] 5.617835e+01 6.207885e+01 1.041684e+02 1.116806e+02 5.474131e+01
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#> [861] 6.509415e+01 6.059272e+01 1.355539e+02 6.410088e+01 7.588028e+01
#> [866] 5.534778e+01 6.398028e+01 6.132116e+01 5.969032e+01 6.234388e+01
#> [871] 5.983538e+01 8.681634e+01 8.742810e+01 6.114619e+01 6.154857e+01
#> [876] 8.720435e+01 1.318643e+02 1.500534e+02 8.516663e+01 8.515345e+01
#> [881] 5.948399e+01 6.988092e+01 6.041254e+01 6.424596e+01 7.625860e+01
#> [886] 6.715676e+01 6.418979e+01 6.122964e+01 6.011600e+01 5.935320e+01
#> [891] 5.956912e+01 6.437868e+01 6.992023e+01 6.289050e+01 9.073888e+01
#> [896] 6.256050e+01 6.597746e+01 5.754843e+01 1.405953e+02 9.419752e+01
#> [901] 5.810301e+01 5.846539e+01 5.628509e+01 1.390301e+02 5.375096e+01
#> [906] 6.717771e+01 7.934274e+01 5.757352e+01 7.627952e+01 5.921519e+01
#> [911] 7.296596e+01 5.679082e+01 6.502518e+01 6.444095e+01 5.941139e+01
#> [916] 6.656027e+01 5.426468e+02 6.843280e+01 5.453797e+01 6.059650e+01
#> [921] 6.458134e+01 6.630874e+01 7.142177e+01 5.656714e+01 7.754836e+01
#> [926] 6.553813e+01 9.040772e+01 5.895001e+01 9.145316e+01 5.549355e+01
#> [931] 9.083003e+01 5.826395e+01 6.061072e+01 5.747966e+01 6.070686e+01
#> [936] 7.146049e+01 5.762085e+01 5.262395e+01 5.758483e+01 6.686124e+01
#> [941] 6.420121e+01 6.003894e+01 7.890846e+01 6.556275e+01 6.025899e+01
#> [946] 7.011315e+01 7.019785e+01 5.543121e+01 8.703192e+01 6.000652e+01
#> [951] 1.478959e+02 7.373553e+01 9.453344e+01 9.266823e+01 5.445219e+01
#> [956] 7.864112e+01 1.059749e+02 8.587259e+01 2.181471e+02 4.828023e+02
#> [961] 6.781532e+01 5.778312e+01 1.108324e+02 1.113774e+02 6.240208e+01
#> [966] 7.563386e+01 6.020161e+01 7.873504e+01 7.255913e+01 7.238231e+01
#> [971] 7.830757e+01 6.066391e+01 5.784522e+01 5.856875e+01 6.126723e+01
#> [976] 8.648628e+01 2.915166e+02 5.747629e+01 7.866078e+01 6.856181e+01
#> [981] 1.072047e+02 9.536218e+01 6.326173e+01 7.543370e+01 6.102503e+01
#> [986] 6.003202e+01 5.038718e+01 1.096310e+02 6.228921e+01 8.359987e+01
#> [991] 9.076504e+01 7.133703e+01 5.240986e+01 1.034295e+02 6.245734e+01
#> [996] 6.493634e+01 5.908734e+01 5.330751e+01 6.867541e+01 6.075211e+01