Expected Value of Perfect Partial Information (EVPPI) for Selected Parameters
Source:R/evppi.R, R/evppi.default.R
evppi.RdCalculates the Expected Value of Perfect Partial Information (EVPPI) for subsets of parameters. Uses GAM non-parametric regression for single parameter EVPPI and the SPDE-INLA method for larger parameter subsets.
Usage
evppi(he, param_idx, input, N = NULL, plot = FALSE, residuals = TRUE, ...)
# S3 method for default
evppi(he, ...)
# S3 method for bcea
evppi(
he,
param_idx = NULL,
input,
N = NULL,
plot = FALSE,
residuals = TRUE,
method = NULL,
...
)Arguments
- he
A
bceaobject containing the results of the Bayesian modelling and the economic evaluation.- param_idx
A vector of parameters for which the EVPPI should be calculated. This can be given as a string (or vector of strings) of names or a numeric vector, corresponding to the column numbers of important parameters.
- input
A matrix containing the simulations for all the parameters monitored by the call to JAGS or BUGS. The matrix should have column names matching the names of the parameters and the values in the vector parameter should match at least one of those values.
- N
The number of PSA simulations used to calculate the EVPPI. The default uses all the available samples.
- plot
A logical value indicating whether the triangular mesh for SPDE-INLA should be plotted. Default set to
FALSE.- residuals
A logical value indicating whether the fitted values for the SPDE-INLA method should be outputted. Default set to
TRUE.- ...
Additional arguments. Details of the methods to compute the EVPPI and their additional arguments are:
For single-parameter:
Generalized additive model (GAM) (default).
The method of Strong & Oakley use
methodas stringso. The user needs to also specify the number of "blocks" (e.g.n.blocks=20). Note that the multi-parameter version for this method has been deprecated.The method of Sadatsafavi et al. where
methodtakes as value a string of eithersadorsal. It is then possible to also specify the number of "separators" (e.g.n.seps=3). If none is specified, the default valuen.seps=1is used. Note that the multi-parameter version for this method has been deprecated.
For multi-parameter:
INLA/SPDE (default).
Gaussian process regression with
methodofgp.
- method
Character string to select which method to use. The default methods are recommended. However, it is possible (mainly for backward compatibility) to use different methods.
Value
Object of class evppi:
- evppi
The computed values of evppi for all values of the parameter of willingness to pay.
- index
A numerical vector with the index associated with the parameters for which the EVPPI was calculated.
- k
The vector of values for the willingness to pay.
- evi
The vector of values for the overall EVPPI.
- fitted.costs
The fitted values for the costs.
- fitted.effects
The fitted values for the effects.
- parameters
A single string containing the names of the parameters for which the EVPPI was calculated, used for plotting the EVPPI.
- time
Computational time (in seconds).
- fit.c
The object produced by the model fit for the costs.
- fit.e
The object produced by the model fit for the effects.
- formula
The formula used to fit the model.
- method
A string indicating the method used to estimate the EVPPI.
Details
The single parameter EVPPI has been calculated using the non-parametric GAM regression developed by Strong et al. (2014). The multi-parameter EVPPI is calculated using the SPDE-INLA regression method for Gaussian Process regression developed by Heath et al. (2015).
This function has been completely changed and restructured to make it possible
to change regression method.
The method argument can now be given as a list. The first element element in the
list is a vector giving the regression method for the effects. The second gives
the regression method for the costs. The method' argument can also be given as before which then uses the same regression method for all curves. All other extra_argscan be given as before.int.ordcan be updated using the list formulation above to give the interactions for each different curve. The formula argument for GAM can only be given once, eitherte()ors() + s()`
as this is for computational reasons rather than to aid fit.
You can still plot the INLA mesh elements but not output the meshes.
GAM regression
For multi-parameter, the user can select 3 possible methods. If
method = "GAM" (BCEA will accept also "gam", "G" or
"g"), then the computations are based on GAM regression. The user can
also specify the formula for the regression. The default option is to use a
tensor product (e.g. if there are two main parameters, p1 and
p2, this amounts to setting formula = "te(p1,p2)", which
indicates that the two parameters interact). Alternatively, it is possible
to specify a model in which the parameters are independent using the
notation formula = "s(p1) + s(p2)". This may lead to worse accuracy in
the estimates.
Strong et al. GP regression
This is used if method="GP" (BCEA will also accept the specification
method="gp"). In this case, the user can also specify the number of
PSA runs that should be used to estimate the hyperparameters of the model
(e.g. n.sim=100). This value is set by default to 500.
INLA-related options
These are all rather technical and are described in detail in Baio et al. (2017).
The optional parameter vector int.ord can take integer values (c(1,1) is
default) and will force the predictor to include interactions: if
int.ord = c(k, h), then all k-way interactions will be used for the
effects and all h-way interactions will be used for the costs. Also, the
user can specify the feature of the mesh for the "spatial" part of the
model. The optional parameter cutoff (default 0.3) controls the
density of the points inside the mesh. Acceptable values are typically in
the interval (0.1, 0.5), with lower values implying more points (and thus
better approximation and greater computational time). The construction of the
boundaries for the mesh can be controlled by the optional inputs
convex.inner (default = -0.4) and convex.outer (default =
-0.7). These should be negative values and can be decreased (say to -0.7 and
-1, respectively) to increase the distance between the points and the outer
boundary, which also increases precision and computational time. The
optional argumentrobust can be set to TRUE, in which case INLA will
use a t prior distribution for the coefficients of the linear predictor.
Finally, the user can control the accuracy of the INLA grid-search for the
estimation of the hyperparameters. This is done by setting a value
h.value (default = 0.00005). Lower values imply a more refined search
(and hence better accuracy), at the expense of computational speed. The
method argument can also be given as a list allowing different regression
methods for the effects and costs, and the different incremental decisions.
The first list element should contain a vector of methods for the
incremental effects and the second for the costs, for example
method = list(c("GAM"), c("INLA")). The int.ord argument can also
be given as a list to give different interaction levels for each regression
curve.
By default, when no method is specified by the user, evppi will
use GAM if the number of parameters is <5 and INLA otherwise.
References
Strong M, Oakley JE, Brennan A (2014). “Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample : A Nonparametric Regression Approach.” Medical Decision Making, 311--326. doi:10.1177/0272989X13505910 .
Sadatsafavi M, Bansback N, Zafari Z, Najafzadeh M, Marra C (2013). “Need for speed: An efficient algorithm for calculation of single-parameter expected value of partial perfect information.” Value Heal., 16(2), 438--448. ISSN 10983015, doi:10.1016/j.jval.2012.10.018 , http://dx.doi.org/10.1016/j.jval.2012.10.018.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
Baio, Gianluca, Berardi, Andrea, Heath A (2017). Bayesian Cost-Effectiveness Analysis with the R package BCEA. Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-55718-2.
Heath A, Manolopoulou I, Baio G (2016). “Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.” Stat. Med., 35(23), 4264--4280. ISSN 0277-6715, doi:10.1002/sim.6983 , 1504.05436, https://pubmed.ncbi.nlm.nih.gov/27189534/.
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
if (FALSE) {
# Load the post-processed results of the MCMC simulation model
# original JAGS output is can be downloaded from here
# https://gianluca.statistica.it/book/bcea/code/vaccine.RData
data(Vaccine, package = "BCEA")
treats <- c("Status quo", "Vaccination")
# Run the health economic evaluation using BCEA
m <- bcea(e.pts, c.pts, ref = 2, interventions = treats)
# Compute the EVPPI for a bunch of parameters
inp <- createInputs(vaccine_mat)
EVPPI <- evppi(m, c("beta.1." , "beta.2."), inp$mat)
plot(EVPPI)
# deprecated (single parameter) methods
EVPPI.so <- evppi(m, c("beta.1.", "beta.2."), inp$mat, method = "so", n.blocks = 50)
EVPPI.sad <- evppi(m, c("beta.1.", "beta.2."), inp$mat, method = "sad", n.seps = 1)
plot(EVPPI.so)
plot(EVPPI.sad)
# Compute the EVPPI using INLA/SPDE
if (require("INLA"))
x_inla <- evppi(he = m, 39:40, input = inp$mat)
# using GAM regression
x_gam <- evppi(he = m, 39:40, input = inp$mat, method = "GAM")
# using Strong et al GP regression
x_gp <- evppi(he = m, 39:40, input = inp$mat, method = "GP")
# plot results
if (require("INLA")) plot(x_inla)
points(x_inla$k, x_inla$evppi, type = "l", lwd = 2, lty = 2)
points(x_gam$k, x_gam$evppi, type = "l", col = "red")
points(x_gp$k, x_gp$evppi, type = "l", col = "blue")
if (require("INLA")) {
plot(x_inla$k, x_inla$evppi, type = "l", lwd = 2, lty = 2)
points(x_gam$k, x_gam$evppi, type = "l", col = "red")
points(x_gp$k, x_gp$evppi, type = "l", col = "blue")
}
data(Smoking)
treats <- c("No intervention", "Self-help",
"Individual counselling", "Group counselling")
m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
inp <- createInputs(smoking_output)
EVPPI <- evppi(m, c(2,3), inp$mat, h.value = 0.0000005)
plot(EVPPI)
}
data(Vaccine, package = "BCEA")
treats <- c("Status quo", "Vaccination")
bcea_vacc <- bcea(e.pts, c.pts, ref = 2, interventions = treats)
inp <- createInputs(vaccine_mat)
#> [1] "14 \nLinear dependence: removing column pi.2.2."
#> [2] "15 \nLinear dependence: removing column pi.2.2."
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#> [1] "14 \nLinear dependence: removing column pi.2.1."
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#> [1] "14 \nLinear dependence: removing column Repeat.GP.2.2."
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#> [1] "14 \nLinear dependence: removing column Repeat.GP.2.1."
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#> [3] "20 \nLinear dependence: removing column Repeat.GP.1.1."
evppi(bcea_vacc, c("beta.1.", "beta.2."), inp$mat)
#> $evppi
#> [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.457218e-05
#> [36] 9.285676e-05 1.611413e-04 2.294259e-04 2.977105e-04 3.659951e-04
#> [41] 4.342796e-04 5.025642e-04 5.708488e-04 6.391333e-04 7.074179e-04
#> [46] 8.359579e-04 9.696656e-04 1.103373e-03 1.237081e-03 1.370789e-03
#> [51] 1.504497e-03 1.670549e-03 1.859133e-03 2.047718e-03 2.236302e-03
#> [56] 2.424887e-03 2.613472e-03 2.802056e-03 2.990641e-03 3.179226e-03
#> [61] 3.388697e-03 3.632914e-03 3.877131e-03 4.139327e-03 4.557299e-03
#> [66] 5.018772e-03 5.518913e-03 6.019054e-03 6.519195e-03 7.072839e-03
#> [71] 7.631472e-03 8.238981e-03 8.914107e-03 9.667252e-03 1.050133e-02
#> [76] 1.136910e-02 1.233047e-02 1.330277e-02 1.435958e-02 1.545712e-02
#> [81] 1.665844e-02 1.791987e-02 1.926646e-02 2.069755e-02 2.215719e-02
#> [86] 2.365858e-02 2.536231e-02 2.721777e-02 2.926248e-02 3.142167e-02
#> [91] 3.366613e-02 3.594727e-02 3.828947e-02 4.066173e-02 4.308486e-02
#> [96] 4.554495e-02 4.810142e-02 5.076759e-02 5.354944e-02 5.645627e-02
#> [101] 5.953500e-02 6.272953e-02 6.605710e-02 6.946561e-02 7.299518e-02
#> [106] 7.661519e-02 8.044882e-02 8.454452e-02 8.880028e-02 9.316356e-02
#> [111] 9.757211e-02 1.020631e-01 1.066899e-01 1.115583e-01 1.165761e-01
#> [116] 1.217236e-01 1.269848e-01 1.324089e-01 1.379159e-01 1.435288e-01
#> [121] 1.492557e-01 1.550713e-01 1.610079e-01 1.670532e-01 1.733199e-01
#> [126] 1.797311e-01 1.862892e-01 1.929569e-01 1.998673e-01 2.069650e-01
#> [131] 2.141277e-01 2.214818e-01 2.290093e-01 2.366830e-01 2.444225e-01
#> [136] 2.522186e-01 2.601369e-01 2.681379e-01 2.761958e-01 2.844056e-01
#> [141] 2.927010e-01 3.011088e-01 3.096150e-01 3.182103e-01 3.270021e-01
#> [146] 3.359932e-01 3.451267e-01 3.543843e-01 3.637561e-01 3.733013e-01
#> [151] 3.829607e-01 3.927341e-01 4.026574e-01 4.127028e-01 4.228980e-01
#> [156] 4.332371e-01 4.437379e-01 4.544137e-01 4.651813e-01 4.761334e-01
#> [161] 4.872191e-01 4.984766e-01 5.098566e-01 5.214073e-01 5.330834e-01
#> [166] 5.449266e-01 5.569304e-01 5.690002e-01 5.812412e-01 5.936338e-01
#> [171] 6.061169e-01 6.187243e-01 6.314361e-01 6.442246e-01 6.570961e-01
#> [176] 6.700288e-01 6.830269e-01 6.960982e-01 7.092648e-01 7.225512e-01
#> [181] 7.358751e-01 7.492499e-01 7.627448e-01 7.763942e-01 7.901216e-01
#> [186] 8.039977e-01 8.179865e-01 8.320791e-01 8.462536e-01 8.605366e-01
#> [191] 8.749299e-01 8.893719e-01 9.039347e-01 9.186171e-01 9.334328e-01
#> [196] 9.483216e-01 9.632936e-01 9.783535e-01 9.935304e-01 1.008831e+00
#> [201] 1.024234e+00 1.039745e+00 1.055422e+00 1.071224e+00 1.084066e+00
#> [206] 1.074809e+00 1.065660e+00 1.056641e+00 1.047729e+00 1.038873e+00
#> [211] 1.030071e+00 1.021314e+00 1.012653e+00 1.004120e+00 9.956920e-01
#> [216] 9.872935e-01 9.789248e-01 9.706356e-01 9.623851e-01 9.542037e-01
#> [221] 9.461404e-01 9.381714e-01 9.302605e-01 9.224375e-01 9.147457e-01
#> [226] 9.071125e-01 8.995306e-01 8.919891e-01 8.844713e-01 8.770460e-01
#> [231] 8.697233e-01 8.625451e-01 8.554401e-01 8.483837e-01 8.413724e-01
#> [236] 8.344054e-01 8.275071e-01 8.206365e-01 8.138200e-01 8.071060e-01
#> [241] 8.004723e-01 7.938925e-01 7.873783e-01 7.809368e-01 7.745497e-01
#> [246] 7.682354e-01 7.619523e-01 7.557112e-01 7.495586e-01 7.434515e-01
#> [251] 7.374292e-01 7.314887e-01 7.256113e-01 7.197572e-01 7.139554e-01
#> [256] 7.081910e-01 7.024605e-01 6.967964e-01 6.912702e-01 6.858125e-01
#> [261] 6.804006e-01 6.750311e-01 6.697037e-01 6.644020e-01 6.591274e-01
#> [266] 6.539432e-01 6.488189e-01 6.437494e-01 6.387192e-01 6.337315e-01
#> [271] 6.287676e-01 6.238712e-01 6.190358e-01 6.143003e-01 6.096329e-01
#> [276] 6.050127e-01 6.004741e-01 5.960047e-01 5.915586e-01 5.871403e-01
#> [281] 5.827532e-01 5.783884e-01 5.740675e-01 5.697920e-01 5.655458e-01
#> [286] 5.613352e-01 5.571441e-01 5.529855e-01 5.488534e-01 5.447722e-01
#> [291] 5.406968e-01 5.366213e-01 5.325726e-01 5.285423e-01 5.245240e-01
#> [296] 5.205150e-01 5.165845e-01 5.126795e-01 5.088076e-01 5.049948e-01
#> [301] 5.012110e-01 4.975225e-01 4.938624e-01 4.902186e-01 4.865747e-01
#> [306] 4.829573e-01 4.794062e-01 4.758743e-01 4.723592e-01 4.688739e-01
#> [311] 4.654132e-01 4.619793e-01 4.585647e-01 4.551584e-01 4.517521e-01
#> [316] 4.483486e-01 4.449656e-01 4.416163e-01 4.383141e-01 4.350518e-01
#> [321] 4.318246e-01 4.286196e-01 4.254631e-01 4.223070e-01 4.191951e-01
#> [326] 4.161302e-01 4.131096e-01 4.101110e-01 4.071347e-01 4.041827e-01
#> [331] 4.012589e-01 3.983743e-01 3.955212e-01 3.927254e-01 3.899432e-01
#> [336] 3.871855e-01 3.844689e-01 3.817796e-01 3.791394e-01 3.765335e-01
#> [341] 3.739600e-01 3.714164e-01 3.688934e-01 3.663704e-01 3.638535e-01
#> [346] 3.613474e-01 3.588413e-01 3.563576e-01 3.539089e-01 3.514843e-01
#> [351] 3.490616e-01 3.466548e-01 3.442652e-01 3.418779e-01 3.395314e-01
#> [356] 3.372204e-01 3.349244e-01 3.326430e-01 3.303822e-01 3.281223e-01
#> [361] 3.258784e-01 3.236347e-01 3.214071e-01 3.191795e-01 3.169807e-01
#> [366] 3.148009e-01 3.126353e-01 3.105210e-01 3.084279e-01 3.063603e-01
#> [371] 3.043336e-01 3.023203e-01 3.003220e-01 2.983323e-01 2.963581e-01
#> [376] 2.944046e-01 2.924985e-01 2.906183e-01 2.887512e-01 2.869099e-01
#> [381] 2.850765e-01 2.832430e-01 2.814097e-01 2.796030e-01 2.778181e-01
#> [386] 2.760596e-01 2.743042e-01 2.725626e-01 2.708210e-01 2.690877e-01
#> [391] 2.673614e-01 2.656351e-01 2.639213e-01 2.622214e-01 2.605248e-01
#> [396] 2.588283e-01 2.571434e-01 2.554615e-01 2.537810e-01 2.521252e-01
#> [401] 2.504935e-01 2.488892e-01 2.472959e-01 2.457275e-01 2.441923e-01
#> [406] 2.426613e-01 2.411526e-01 2.396684e-01 2.382291e-01 2.367951e-01
#> [411] 2.353736e-01 2.339666e-01 2.325611e-01 2.311557e-01 2.297563e-01
#> [416] 2.283653e-01 2.269863e-01 2.256259e-01 2.242938e-01 2.229744e-01
#> [421] 2.216551e-01 2.203358e-01 2.190165e-01 2.176971e-01 2.163804e-01
#> [426] 2.150968e-01 2.138344e-01 2.125824e-01 2.113339e-01 2.100859e-01
#> [431] 2.088515e-01 2.076339e-01 2.064492e-01 2.052776e-01 2.041179e-01
#> [436] 2.029666e-01 2.018338e-01 2.007256e-01 1.996347e-01 1.985587e-01
#> [441] 1.974892e-01 1.964197e-01 1.953502e-01 1.942885e-01 1.932456e-01
#> [446] 1.922157e-01 1.911862e-01 1.901613e-01 1.891454e-01 1.881421e-01
#> [451] 1.871387e-01 1.861354e-01 1.851410e-01 1.841523e-01 1.831884e-01
#> [456] 1.822613e-01 1.813469e-01 1.804353e-01 1.795236e-01 1.786235e-01
#> [461] 1.777250e-01 1.768265e-01 1.759280e-01 1.750408e-01 1.741682e-01
#> [466] 1.733006e-01 1.724417e-01 1.715828e-01 1.707240e-01 1.698655e-01
#> [471] 1.690192e-01 1.681822e-01 1.673487e-01 1.665152e-01 1.656817e-01
#> [476] 1.648482e-01 1.640322e-01 1.632500e-01 1.624679e-01 1.616857e-01
#> [481] 1.609060e-01 1.601366e-01 1.593672e-01 1.585978e-01 1.578284e-01
#> [486] 1.570590e-01 1.562896e-01 1.555302e-01 1.547736e-01 1.540170e-01
#> [491] 1.532604e-01 1.525038e-01 1.517472e-01 1.509906e-01 1.502340e-01
#> [496] 1.494882e-01 1.487427e-01 1.479973e-01 1.472519e-01 1.465064e-01
#> [501] 1.457650e-01
#>
#> $index
#> [1] "beta.1." "beta.2."
#>
#> $k
#> [1] 0 100 200 300 400 500 600 700 800 900 1000 1100
#> [13] 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300
#> [25] 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500
#> [37] 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700
#> [49] 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900
#> [61] 6000 6100 6200 6300 6400 6500 6600 6700 6800 6900 7000 7100
#> [73] 7200 7300 7400 7500 7600 7700 7800 7900 8000 8100 8200 8300
#> [85] 8400 8500 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500
#> [97] 9600 9700 9800 9900 10000 10100 10200 10300 10400 10500 10600 10700
#> [109] 10800 10900 11000 11100 11200 11300 11400 11500 11600 11700 11800 11900
#> [121] 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900 13000 13100
#> [133] 13200 13300 13400 13500 13600 13700 13800 13900 14000 14100 14200 14300
#> [145] 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300 15400 15500
#> [157] 15600 15700 15800 15900 16000 16100 16200 16300 16400 16500 16600 16700
#> [169] 16800 16900 17000 17100 17200 17300 17400 17500 17600 17700 17800 17900
#> [181] 18000 18100 18200 18300 18400 18500 18600 18700 18800 18900 19000 19100
#> [193] 19200 19300 19400 19500 19600 19700 19800 19900 20000 20100 20200 20300
#> [205] 20400 20500 20600 20700 20800 20900 21000 21100 21200 21300 21400 21500
#> [217] 21600 21700 21800 21900 22000 22100 22200 22300 22400 22500 22600 22700
#> [229] 22800 22900 23000 23100 23200 23300 23400 23500 23600 23700 23800 23900
#> [241] 24000 24100 24200 24300 24400 24500 24600 24700 24800 24900 25000 25100
#> [253] 25200 25300 25400 25500 25600 25700 25800 25900 26000 26100 26200 26300
#> [265] 26400 26500 26600 26700 26800 26900 27000 27100 27200 27300 27400 27500
#> [277] 27600 27700 27800 27900 28000 28100 28200 28300 28400 28500 28600 28700
#> [289] 28800 28900 29000 29100 29200 29300 29400 29500 29600 29700 29800 29900
#> [301] 30000 30100 30200 30300 30400 30500 30600 30700 30800 30900 31000 31100
#> [313] 31200 31300 31400 31500 31600 31700 31800 31900 32000 32100 32200 32300
#> [325] 32400 32500 32600 32700 32800 32900 33000 33100 33200 33300 33400 33500
#> [337] 33600 33700 33800 33900 34000 34100 34200 34300 34400 34500 34600 34700
#> [349] 34800 34900 35000 35100 35200 35300 35400 35500 35600 35700 35800 35900
#> [361] 36000 36100 36200 36300 36400 36500 36600 36700 36800 36900 37000 37100
#> [373] 37200 37300 37400 37500 37600 37700 37800 37900 38000 38100 38200 38300
#> [385] 38400 38500 38600 38700 38800 38900 39000 39100 39200 39300 39400 39500
#> [397] 39600 39700 39800 39900 40000 40100 40200 40300 40400 40500 40600 40700
#> [409] 40800 40900 41000 41100 41200 41300 41400 41500 41600 41700 41800 41900
#> [421] 42000 42100 42200 42300 42400 42500 42600 42700 42800 42900 43000 43100
#> [433] 43200 43300 43400 43500 43600 43700 43800 43900 44000 44100 44200 44300
#> [445] 44400 44500 44600 44700 44800 44900 45000 45100 45200 45300 45400 45500
#> [457] 45600 45700 45800 45900 46000 46100 46200 46300 46400 46500 46600 46700
#> [469] 46800 46900 47000 47100 47200 47300 47400 47500 47600 47700 47800 47900
#> [481] 48000 48100 48200 48300 48400 48500 48600 48700 48800 48900 49000 49100
#> [493] 49200 49300 49400 49500 49600 49700 49800 49900 50000
#>
#> $evi
#> [1] 0.03705361 0.03785587 0.03869327 0.03957261 0.04053147 0.04149032
#> [7] 0.04249547 0.04357790 0.04468983 0.04580177 0.04696679 0.04820919
#> [13] 0.04945159 0.05071720 0.05204083 0.05341006 0.05479360 0.05628177
#> [19] 0.05790349 0.05968408 0.06163395 0.06371142 0.06579055 0.06796376
#> [25] 0.07022097 0.07273816 0.07532847 0.07795127 0.08057406 0.08320268
#> [31] 0.08589638 0.08868468 0.09152601 0.09437325 0.09732932 0.10029796
#> [37] 0.10336608 0.10650386 0.10969266 0.11291187 0.11623517 0.11961608
#> [43] 0.12301796 0.12655367 0.13016897 0.13389347 0.13771150 0.14164319
#> [49] 0.14562582 0.14972633 0.15408068 0.15855398 0.16308971 0.16773032
#> [55] 0.17246620 0.17733774 0.18240585 0.18763480 0.19302758 0.19870097
#> [61] 0.20455777 0.21058793 0.21671040 0.22296970 0.22947367 0.23630862
#> [67] 0.24330447 0.25039355 0.25766800 0.26518678 0.27299674 0.28116086
#> [73] 0.28956168 0.29817600 0.30710155 0.31640030 0.32590075 0.33562984
#> [79] 0.34549466 0.35556390 0.36586862 0.37636890 0.38722042 0.39819986
#> [85] 0.40931334 0.42067277 0.43210667 0.44377365 0.45566760 0.46785113
#> [91] 0.48014364 0.49246093 0.50499804 0.51762457 0.53037623 0.54325794
#> [97] 0.55623809 0.56931969 0.58247875 0.59587792 0.60962413 0.62343508
#> [103] 0.63739430 0.65146528 0.66560388 0.67977458 0.69403487 0.70842482
#> [109] 0.72293925 0.73755255 0.75234332 0.76723566 0.78221180 0.79721725
#> [115] 0.81232276 0.82758674 0.84296480 0.85844709 0.87398274 0.88966269
#> [121] 0.90550552 0.92141819 0.93740388 0.95345109 0.96960001 0.98586393
#> [127] 1.00217636 1.01852827 1.03494208 1.05142297 1.06797820 1.08468101
#> [133] 1.10149448 1.11850610 1.13565841 1.15294406 1.17038384 1.18787278
#> [139] 1.20544370 1.22316318 1.24098583 1.25890098 1.27685790 1.29494468
#> [145] 1.31305675 1.33121282 1.34951452 1.36794968 1.38653595 1.40518347
#> [151] 1.42398997 1.44291220 1.46187667 1.48090091 1.49998386 1.51913333
#> [157] 1.53842038 1.55785893 1.57742357 1.59708499 1.61685411 1.63671449
#> [163] 1.65661087 1.67656286 1.69652816 1.71649345 1.73649008 1.75651452
#> [169] 1.77658853 1.79668980 1.81684907 1.83704353 1.85732385 1.87768519
#> [175] 1.89813142 1.91868758 1.93931951 1.95998689 1.98070096 2.00151878
#> [181] 2.02248963 2.04353027 2.06465898 2.08582184 2.10699995 2.12828009
#> [187] 2.14966652 2.17114009 2.19267665 2.21423926 2.23585252 2.25753717
#> [193] 2.27924027 2.30098203 2.32278791 2.34466165 2.36659499 2.38854408
#> [199] 2.41055686 2.43261442 2.45475044 2.47693925 2.49917875 2.52143631
#> [205] 2.54070684 2.53787999 2.53506303 2.53229036 2.52958762 2.52697130
#> [211] 2.52440752 2.52185173 2.51932424 2.51682773 2.51436495 2.51199912
#> [217] 2.50972006 2.50748163 2.50529882 2.50313412 2.50097250 2.49883204
#> [223] 2.49676430 2.49474058 2.49271685 2.49069313 2.48867961 2.48671075
#> [229] 2.48488026 2.48307698 2.48130197 2.47958992 2.47791258 2.47625725
#> [235] 2.47464086 2.47306603 2.47152238 2.46999548 2.46856835 2.46717635
#> [241] 2.46580518 2.46446047 2.46314796 2.46185416 2.46056499 2.45930632
#> [247] 2.45807492 2.45694841 2.45588064 2.45485168 2.45384951 2.45290828
#> [253] 2.45201256 2.45115357 2.45034345 2.44954816 2.44877788 2.44801321
#> [259] 2.44724855 2.44648588 2.44575366 2.44502992 2.44434475 2.44368189
#> [265] 2.44304942 2.44246395 2.44190999 2.44138249 2.44092506 2.44048420
#> [271] 2.44005376 2.43966976 2.43930692 2.43896298 2.43867630 2.43841126
#> [277] 2.43815833 2.43792271 2.43770326 2.43750768 2.43734001 2.43717648
#> [283] 2.43701295 2.43685240 2.43672731 2.43660228 2.43649001 2.43639935
#> [289] 2.43632504 2.43627307 2.43624400 2.43625683 2.43628988 2.43635282
#> [295] 2.43644101 2.43653657 2.43664089 2.43676890 2.43691328 2.43709723
#> [301] 2.43728708 2.43747693 2.43766678 2.43785663 2.43804648 2.43823633
#> [307] 2.43844428 2.43869049 2.43895177 2.43921999 2.43948822 2.43975645
#> [313] 2.44002468 2.44029291 2.44056114 2.44082937 2.44109760 2.44137953
#> [319] 2.44170391 2.44205164 2.44241982 2.44282017 2.44323328 2.44364639
#> [325] 2.44409249 2.44457061 2.44507864 2.44562174 2.44616484 2.44670794
#> [331] 2.44725127 2.44781211 2.44838171 2.44895677 2.44954877 2.45015945
#> [337] 2.45077012 2.45139695 2.45203779 2.45268564 2.45333350 2.45398136
#> [343] 2.45462922 2.45528430 2.45595980 2.45663861 2.45733166 2.45803623
#> [349] 2.45876134 2.45951256 2.46027210 2.46104954 2.46184788 2.46270278
#> [355] 2.46356630 2.46442982 2.46529334 2.46616454 2.46705682 2.46794910
#> [361] 2.46884707 2.46975739 2.47066771 2.47157803 2.47250829 2.47345963
#> [367] 2.47441097 2.47536231 2.47631365 2.47726499 2.47821633 2.47916766
#> [373] 2.48011900 2.48108604 2.48205474 2.48302345 2.48399401 2.48498194
#> [379] 2.48596986 2.48696203 2.48797773 2.48900137 2.49002501 2.49106195
#> [385] 2.49210191 2.49314802 2.49419569 2.49526209 2.49633230 2.49740252
#> [391] 2.49847482 2.49956060 2.50065377 2.50174694 2.50284011 2.50393327
#> [397] 2.50502644 2.50613166 2.50724137 2.50835109 2.50946173 2.51058409
#> [403] 2.51171324 2.51285383 2.51399442 2.51515263 2.51631514 2.51748307
#> [409] 2.51865224 2.51982141 2.52100290 2.52218565 2.52336840 2.52455115
#> [415] 2.52573390 2.52692109 2.52811219 2.52933476 2.53058895 2.53185552
#> [421] 2.53312209 2.53441819 2.53575671 2.53709574 2.53846228 2.53987875
#> [427] 2.54131648 2.54276301 2.54421702 2.54567692 2.54713737 2.54861863
#> [433] 2.55010724 2.55161064 2.55311404 2.55461744 2.55612084 2.55762424
#> [439] 2.55912764 2.56063104 2.56213444 2.56364595 2.56516720 2.56668845
#> [445] 2.56821847 2.56975283 2.57129950 2.57284964 2.57442273 2.57600088
#> [451] 2.57757903 2.57916437 2.58076660 2.58238097 2.58400753 2.58565356
#> [457] 2.58730489 2.58896026 2.59062565 2.59229225 2.59396413 2.59565871
#> [463] 2.59736472 2.59909129 2.60083599 2.60258363 2.60433128 2.60607892
#> [469] 2.60782656 2.60957421 2.61132185 2.61306949 2.61482472 2.61658627
#> [475] 2.61835210 2.62011955 2.62188699 2.62365443 2.62542187 2.62718931
#> [481] 2.62896830 2.63074890 2.63253189 2.63431941 2.63611797 2.63793732
#> [487] 2.63975777 2.64157823 2.64339903 2.64522609 2.64705546 2.64888724
#> [493] 2.65071902 2.65255080 2.65440805 2.65627824 2.65815205 2.66002586
#> [499] 2.66189967 2.66377348 2.66564729
#>
#> $parameters
#> [1] "beta.1. and beta.2."
#>
#> $time
#> $time$`Fitting for Effects`
#> NULL
#>
#> $time$`Fitting for Costs`
#> NULL
#>
#> $time$`Calculating EVPPI`
#> NULL
#>
#>
#> $method
#> $method$`Methods for Effects`
#> [1] "gam"
#>
#> $method$`Methods for Costs`
#> [1] "gam"
#>
#>
#> $fitted.costs
#> ...1
#> [1,] 5.539060 0
#> [2,] 5.042096 0
#> [3,] 5.420907 0
#> [4,] 5.738414 0
#> [5,] 5.469780 0
#> [6,] 5.552250 0
#> [7,] 3.622860 0
#> [8,] 6.049415 0
#> [9,] 5.829584 0
#> [10,] 4.978065 0
#> [11,] 4.429175 0
#> [12,] 5.407992 0
#> [13,] 6.090357 0
#> [14,] 6.110306 0
#> [15,] 4.868019 0
#> [16,] 5.264807 0
#> [17,] 5.938002 0
#> [18,] 4.435298 0
#> [19,] 4.992994 0
#> [20,] 5.138636 0
#> [21,] 5.692185 0
#> [22,] 5.451911 0
#> [23,] 5.696216 0
#> [24,] 5.172949 0
#> [25,] 4.411596 0
#> [26,] 4.540321 0
#> [27,] 6.219929 0
#> [28,] 5.880996 0
#> [29,] 5.089494 0
#> [30,] 5.046960 0
#> [31,] 5.684144 0
#> [32,] 3.806804 0
#> [33,] 5.209105 0
#> [34,] 5.301564 0
#> [35,] 5.106181 0
#> [36,] 5.813065 0
#> [37,] 4.163367 0
#> [38,] 5.291885 0
#> [39,] 5.057783 0
#> [40,] 5.640054 0
#> [41,] 5.212866 0
#> [42,] 5.686964 0
#> [43,] 5.509276 0
#> [44,] 4.560746 0
#> [45,] 5.257646 0
#> [46,] 5.586814 0
#> [47,] 5.714918 0
#> [48,] 5.374325 0
#> [49,] 5.181640 0
#> [50,] 6.092522 0
#> [51,] 5.040795 0
#> [52,] 3.921548 0
#> [53,] 6.035031 0
#> [54,] 5.881642 0
#> [55,] 5.361438 0
#> [56,] 6.353782 0
#> [57,] 5.481254 0
#> [58,] 5.536473 0
#> [59,] 5.249361 0
#> [60,] 5.351821 0
#> [61,] 4.919147 0
#> [62,] 5.741087 0
#> [63,] 4.555483 0
#> [64,] 5.829663 0
#> [65,] 4.456022 0
#> [66,] 4.756325 0
#> [67,] 5.156087 0
#> [68,] 4.299859 0
#> [69,] 4.859611 0
#> [70,] 4.520524 0
#> [71,] 4.270351 0
#> [72,] 5.854351 0
#> [73,] 4.204380 0
#> [74,] 5.162071 0
#> [75,] 5.889816 0
#> [76,] 4.742549 0
#> [77,] 5.483039 0
#> [78,] 4.585330 0
#> [79,] 5.076819 0
#> [80,] 4.929809 0
#> [81,] 5.851112 0
#> [82,] 6.150576 0
#> [83,] 5.039752 0
#> [84,] 4.184469 0
#> [85,] 5.557155 0
#> [86,] 3.080087 0
#> [87,] 5.447201 0
#> [88,] 5.299291 0
#> [89,] 4.586296 0
#> [90,] 5.248812 0
#> [91,] 3.693466 0
#> [92,] 4.814863 0
#> [93,] 5.685345 0
#> [94,] 4.713743 0
#> [95,] 4.499628 0
#> [96,] 3.619984 0
#> [97,] 4.879671 0
#> [98,] 4.647593 0
#> [99,] 5.920827 0
#> [100,] 6.020268 0
#> [101,] 4.651883 0
#> [102,] 5.377532 0
#> [103,] 5.025746 0
#> [104,] 4.793158 0
#> [105,] 3.982976 0
#> [106,] 4.550494 0
#> [107,] 5.395733 0
#> [108,] 5.616294 0
#> [109,] 6.198823 0
#> [110,] 6.239986 0
#> [111,] 4.160499 0
#> [112,] 5.684216 0
#> [113,] 6.312563 0
#> [114,] 4.996849 0
#> [115,] 4.215937 0
#> [116,] 4.857364 0
#> [117,] 4.417169 0
#> [118,] 3.308411 0
#> [119,] 4.758624 0
#> [120,] 5.334703 0
#> [121,] 5.117974 0
#> [122,] 5.452513 0
#> [123,] 5.616120 0
#> [124,] 5.031824 0
#> [125,] 5.906992 0
#> [126,] 5.201315 0
#> [127,] 5.199154 0
#> [128,] 6.321913 0
#> [129,] 5.327667 0
#> [130,] 4.656508 0
#> [131,] 5.418207 0
#> [132,] 5.610628 0
#> [133,] 4.035627 0
#> [134,] 4.090451 0
#> [135,] 5.323000 0
#> [136,] 5.161108 0
#> [137,] 5.628860 0
#> [138,] 6.002209 0
#> [139,] 5.940414 0
#> [140,] 5.951488 0
#> [141,] 4.155158 0
#> [142,] 4.305728 0
#> [143,] 5.811871 0
#> [144,] 3.968335 0
#> [145,] 4.683313 0
#> [146,] 4.813944 0
#> [147,] 5.717913 0
#> [148,] 3.841804 0
#> [149,] 5.448253 0
#> [150,] 6.119106 0
#> [151,] 5.366319 0
#> [152,] 5.886763 0
#> [153,] 6.044123 0
#> [154,] 5.484369 0
#> [155,] 5.156000 0
#> [156,] 6.495019 0
#> [157,] 5.582958 0
#> [158,] 4.897245 0
#> [159,] 5.068719 0
#> [160,] 4.308136 0
#> [161,] 4.848514 0
#> [162,] 4.541059 0
#> [163,] 5.500579 0
#> [164,] 5.744604 0
#> [165,] 4.803044 0
#> [166,] 4.975764 0
#> [167,] 5.303851 0
#> [168,] 5.046976 0
#> [169,] 5.036179 0
#> [170,] 4.239431 0
#> [171,] 6.274060 0
#> [172,] 4.101257 0
#> [173,] 6.202505 0
#> [174,] 4.269867 0
#> [175,] 5.798713 0
#> [176,] 4.612728 0
#> [177,] 5.447640 0
#> [178,] 5.744765 0
#> [179,] 4.769053 0
#> [180,] 5.942330 0
#> [181,] 4.969693 0
#> [182,] 5.259084 0
#> [183,] 5.637955 0
#> [184,] 5.069238 0
#> [185,] 6.448760 0
#> [186,] 5.282090 0
#> [187,] 4.093503 0
#> [188,] 4.514099 0
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#> [787,] 4.083619 0
#> [788,] 4.707319 0
#> [789,] 4.742773 0
#> [790,] 5.516353 0
#> [791,] 5.782819 0
#> [792,] 5.560904 0
#> [793,] 5.708047 0
#> [794,] 4.915511 0
#> [795,] 6.642908 0
#> [796,] 5.432561 0
#> [797,] 4.221304 0
#> [798,] 3.496642 0
#> [799,] 5.692967 0
#> [800,] 5.722526 0
#> [801,] 4.969542 0
#> [802,] 5.770040 0
#> [803,] 5.795408 0
#> [804,] 6.427136 0
#> [805,] 5.977338 0
#> [806,] 4.762600 0
#> [807,] 5.147554 0
#> [808,] 5.035376 0
#> [809,] 5.456191 0
#> [810,] 5.240130 0
#> [811,] 4.990371 0
#> [812,] 5.899012 0
#> [813,] 5.102199 0
#> [814,] 5.835979 0
#> [815,] 4.685216 0
#> [816,] 6.633196 0
#> [817,] 5.052105 0
#> [818,] 4.367502 0
#> [819,] 4.721172 0
#> [820,] 3.904837 0
#> [821,] 6.070173 0
#> [822,] 5.824268 0
#> [823,] 3.616765 0
#> [824,] 4.774442 0
#> [825,] 4.758659 0
#> [826,] 5.040123 0
#> [827,] 5.971828 0
#> [828,] 4.621744 0
#> [829,] 6.224164 0
#> [830,] 5.598749 0
#> [831,] 4.860810 0
#> [832,] 5.042192 0
#> [833,] 4.271244 0
#> [834,] 5.600378 0
#> [835,] 5.413170 0
#> [836,] 4.810251 0
#> [837,] 6.928535 0
#> [838,] 3.738353 0
#> [839,] 6.434138 0
#> [840,] 5.639613 0
#> [841,] 5.830245 0
#> [842,] 5.853504 0
#> [843,] 4.574596 0
#> [844,] 4.602798 0
#> [845,] 3.317075 0
#> [846,] 5.527132 0
#> [847,] 5.508854 0
#> [848,] 3.817729 0
#> [849,] 5.266908 0
#> [850,] 4.425765 0
#> [851,] 5.778654 0
#> [852,] 4.959931 0
#> [853,] 5.411868 0
#> [854,] 5.158816 0
#> [855,] 4.757418 0
#> [856,] 6.047146 0
#> [857,] 4.515698 0
#> [858,] 5.018813 0
#> [859,] 5.358219 0
#> [860,] 5.323224 0
#> [861,] 5.826494 0
#> [862,] 4.930627 0
#> [863,] 4.964465 0
#> [864,] 4.775029 0
#> [865,] 5.369395 0
#> [866,] 4.892918 0
#> [867,] 5.768430 0
#> [868,] 5.894890 0
#> [869,] 4.942648 0
#> [870,] 3.799714 0
#> [871,] 5.616387 0
#> [872,] 5.541883 0
#> [873,] 4.804498 0
#> [874,] 5.008426 0
#> [875,] 5.208790 0
#> [876,] 5.837551 0
#> [877,] 4.217789 0
#> [878,] 5.487706 0
#> [879,] 5.475554 0
#> [880,] 5.505751 0
#> [881,] 5.561145 0
#> [882,] 5.418828 0
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#> [907,] 4.850997 0
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#> [931,] 5.796199 0
#> [932,] 5.025632 0
#> [933,] 5.985846 0
#> [934,] 3.475001 0
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#> [937,] 5.826111 0
#> [938,] 5.128853 0
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#> [944,] 4.324901 0
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#> [952,] 5.581159 0
#> [953,] 5.862596 0
#> [954,] 6.381265 0
#> [955,] 4.692011 0
#> [956,] 5.124032 0
#> [957,] 4.336472 0
#> [958,] 3.497129 0
#> [959,] 5.484078 0
#> [960,] 5.226008 0
#> [961,] 4.827766 0
#> [962,] 3.666006 0
#> [963,] 4.317422 0
#> [964,] 5.181731 0
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#> [967,] 3.507307 0
#> [968,] 4.491812 0
#> [969,] 5.176884 0
#> [970,] 4.728877 0
#> [971,] 5.891132 0
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#> [973,] 5.065135 0
#> [974,] 4.364556 0
#> [975,] 5.199892 0
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#> [977,] 3.834479 0
#> [978,] 5.996242 0
#> [979,] 5.138226 0
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#> [981,] 4.847854 0
#> [982,] 5.356390 0
#> [983,] 4.628017 0
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#> [985,] 4.239180 0
#> [986,] 5.406299 0
#> [987,] 6.265569 0
#> [988,] 5.355807 0
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#> [990,] 5.472295 0
#> [991,] 5.080981 0
#> [992,] 5.001610 0
#> [993,] 6.080408 0
#> [994,] 5.163193 0
#> [995,] 4.732320 0
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#> [997,] 4.838222 0
#> [998,] 4.837899 0
#> [999,] 4.764121 0
#> [1000,] 4.937521 0
#>
#> $fitted.effects
#> ...1
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#> [3,] 2.004124e-04 0
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#> [11,] 3.465267e-04 0
#> [12,] 2.154358e-04 0
#> [13,] 1.396417e-04 0
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#> [19,] 2.842686e-04 0
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#> [293,] 1.465561e-04 0
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#> [295,] 3.651161e-04 0
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#> [303,] 7.164345e-05 0
#> [304,] 2.786644e-04 0
#> [305,] 3.434537e-04 0
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#> [794,] 2.672030e-04 0
#> [795,] 7.667651e-05 0
#> [796,] 2.112453e-04 0
#> [797,] 3.736929e-04 0
#> [798,] 5.560811e-04 0
#> [799,] 1.790343e-04 0
#> [800,] 1.746465e-04 0
#> [801,] 2.447328e-04 0
#> [802,] 1.693475e-04 0
#> [803,] 1.674992e-04 0
#> [804,] 6.930171e-05 0
#> [805,] 8.983587e-05 0
#> [806,] 2.634435e-04 0
#> [807,] 2.582486e-04 0
#> [808,] 2.556380e-04 0
#> [809,] 1.995702e-04 0
#> [810,] 2.323220e-04 0
#> [811,] 2.857051e-04 0
#> [812,] 1.570781e-04 0
#> [813,] 2.510821e-04 0
#> [814,] 1.558716e-04 0
#> [815,] 2.963195e-04 0
#> [816,] 4.929957e-05 0
#> [817,] 2.629739e-04 0
#> [818,] 3.690959e-04 0
#> [819,] 3.209644e-04 0
#> [820,] 5.537454e-04 0
#> [821,] 1.403553e-04 0
#> [822,] 1.646922e-04 0
#> [823,] 4.237362e-04 0
#> [824,] 2.988110e-04 0
#> [825,] 3.215684e-04 0
#> [826,] 2.762804e-04 0
#> [827,] 1.348681e-04 0
#> [828,] 3.215862e-04 0
#> [829,] 1.280123e-04 0
#> [830,] 1.836707e-04 0
#> [831,] 3.014668e-04 0
#> [832,] 2.678450e-04 0
#> [833,] 3.514266e-04 0
#> [834,] 1.878796e-04 0
#> [835,] 2.105712e-04 0
#> [836,] 3.305988e-04 0
#> [837,] 6.751337e-05 0
#> [838,] 3.685690e-04 0
#> [839,] 1.003287e-04 0
#> [840,] 1.851440e-04 0
#> [841,] 1.603199e-04 0
#> [842,] 1.468121e-04 0
#> [843,] 3.060096e-04 0
#> [844,] 3.520104e-04 0
#> [845,] 5.563269e-04 0
#> [846,] 1.859809e-04 0
#> [847,] 1.993243e-04 0
#> [848,] 4.688514e-04 0
#> [849,] 2.369772e-04 0
#> [850,] 3.941052e-04 0
#> [851,] 1.504409e-04 0
#> [852,] 2.960572e-04 0
#> [853,] 2.108035e-04 0
#> [854,] 2.246083e-04 0
#> [855,] 2.852168e-04 0
#> [856,] 1.423209e-04 0
#> [857,] 3.518317e-04 0
#> [858,] 2.797742e-04 0
#> [859,] 2.222195e-04 0
#> [860,] 2.281903e-04 0
#> [861,] 1.316371e-04 0
#> [862,] 3.027514e-04 0
#> [863,] 2.942336e-04 0
#> [864,] 3.196133e-04 0
#> [865,] 2.178491e-04 0
#> [866,] 3.078019e-04 0
#> [867,] 1.659707e-04 0
#> [868,] 1.574956e-04 0
#> [869,] 2.561883e-04 0
#> [870,] 3.813491e-04 0
#> [871,] 1.841328e-04 0
#> [872,] 1.878828e-04 0
#> [873,] 2.876409e-04 0
#> [874,] 2.834307e-04 0
#> [875,] 2.445162e-04 0
#> [876,] 1.625989e-04 0
#> [877,] 3.593802e-04 0
#> [878,] 1.990678e-04 0
#> [879,] 1.946178e-04 0
#> [880,] 1.917611e-04 0
#> [881,] 1.932140e-04 0
#> [882,] 2.130751e-04 0
#> [883,] 2.530181e-04 0
#> [884,] 1.422756e-04 0
#> [885,] 2.391618e-04 0
#> [886,] 2.545553e-04 0
#> [887,] 2.307330e-04 0
#> [888,] 7.217122e-05 0
#> [889,] 4.158472e-04 0
#> [890,] 1.999728e-04 0
#> [891,] 2.276761e-04 0
#> [892,] 3.742574e-04 0
#> [893,] 2.460132e-04 0
#> [894,] 2.837380e-04 0
#> [895,] 2.418482e-04 0
#> [896,] 4.452901e-04 0
#> [897,] 2.472185e-04 0
#> [898,] 1.484630e-04 0
#> [899,] 2.565810e-04 0
#> [900,] 3.116468e-04 0
#> [901,] 2.057377e-04 0
#> [902,] 2.248702e-04 0
#> [903,] 2.476694e-04 0
#> [904,] 2.393110e-04 0
#> [905,] 1.758681e-04 0
#> [906,] 3.539754e-04 0
#> [907,] 2.837831e-04 0
#> [908,] 2.018909e-04 0
#> [909,] 2.859341e-04 0
#> [910,] 1.315055e-04 0
#> [911,] 3.526117e-04 0
#> [912,] 1.404419e-04 0
#> [913,] 1.636013e-04 0
#> [914,] 3.183048e-04 0
#> [915,] 1.109403e-04 0
#> [916,] 2.805868e-04 0
#> [917,] 4.248834e-05 0
#> [918,] 2.641218e-04 0
#> [919,] 1.591183e-04 0
#> [920,] 5.045417e-04 0
#> [921,] 1.781811e-04 0
#> [922,] 4.753054e-04 0
#> [923,] 2.873764e-04 0
#> [924,] 2.030911e-04 0
#> [925,] 1.672600e-04 0
#> [926,] 2.856257e-04 0
#> [927,] 4.057556e-04 0
#> [928,] 2.847504e-04 0
#> [929,] 1.221909e-04 0
#> [930,] 2.410461e-04 0
#> [931,] 1.670921e-04 0
#> [932,] 2.497373e-04 0
#> [933,] 9.214140e-05 0
#> [934,] 5.533825e-04 0
#> [935,] 2.583420e-04 0
#> [936,] 1.731495e-04 0
#> [937,] 1.590001e-04 0
#> [938,] 2.612118e-04 0
#> [939,] 1.852760e-04 0
#> [940,] 3.501000e-04 0
#> [941,] 2.984257e-04 0
#> [942,] 2.376755e-04 0
#> [943,] 3.153702e-04 0
#> [944,] 3.773165e-04 0
#> [945,] 3.644880e-04 0
#> [946,] 2.543904e-04 0
#> [947,] 2.732824e-04 0
#> [948,] 2.906559e-04 0
#> [949,] 1.327584e-04 0
#> [950,] 3.356992e-04 0
#> [951,] 3.028427e-04 0
#> [952,] 1.893701e-04 0
#> [953,] 1.358843e-04 0
#> [954,] 7.866640e-05 0
#> [955,] 3.341807e-04 0
#> [956,] 2.154368e-04 0
#> [957,] 3.409731e-04 0
#> [958,] 4.846245e-04 0
#> [959,] 2.034264e-04 0
#> [960,] 2.416117e-04 0
#> [961,] 3.012140e-04 0
#> [962,] 4.464723e-04 0
#> [963,] 3.008839e-04 0
#> [964,] 2.210974e-04 0
#> [965,] 2.549875e-04 0
#> [966,] 2.730135e-04 0
#> [967,] 4.464490e-04 0
#> [968,] 3.302712e-04 0
#> [969,] 2.318027e-04 0
#> [970,] 3.287092e-04 0
#> [971,] 1.561973e-04 0
#> [972,] 2.966892e-04 0
#> [973,] 2.479396e-04 0
#> [974,] 3.828799e-04 0
#> [975,] 2.450978e-04 0
#> [976,] 1.585412e-04 0
#> [977,] 3.925257e-04 0
#> [978,] 1.406725e-04 0
#> [979,] 2.428278e-04 0
#> [980,] 1.963504e-04 0
#> [981,] 2.996787e-04 0
#> [982,] 2.225087e-04 0
#> [983,] 3.079070e-04 0
#> [984,] 2.144225e-04 0
#> [985,] 3.831465e-04 0
#> [986,] 2.136507e-04 0
#> [987,] 9.502069e-05 0
#> [988,] 2.147885e-04 0
#> [989,] 2.199898e-04 0
#> [990,] 1.990254e-04 0
#> [991,] 2.568355e-04 0
#> [992,] 2.764570e-04 0
#> [993,] 1.339279e-04 0
#> [994,] 2.515579e-04 0
#> [995,] 3.253564e-04 0
#> [996,] 3.340186e-04 0
#> [997,] 3.056571e-04 0
#> [998,] 3.111801e-04 0
#> [999,] 2.623249e-04 0
#> [1000,] 2.901752e-04 0
#>
#> $select
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14
#> [15] 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [29] 29 30 31 32 33 34 35 36 37 38 39 40 41 42
#> [43] 43 44 45 46 47 48 49 50 51 52 53 54 55 56
#> [57] 57 58 59 60 61 62 63 64 65 66 67 68 69 70
#> [71] 71 72 73 74 75 76 77 78 79 80 81 82 83 84
#> [85] 85 86 87 88 89 90 91 92 93 94 95 96 97 98
#> [99] 99 100 101 102 103 104 105 106 107 108 109 110 111 112
#> [113] 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140
#> [141] 141 142 143 144 145 146 147 148 149 150 151 152 153 154
#> [155] 155 156 157 158 159 160 161 162 163 164 165 166 167 168
#> [169] 169 170 171 172 173 174 175 176 177 178 179 180 181 182
#> [183] 183 184 185 186 187 188 189 190 191 192 193 194 195 196
#> [197] 197 198 199 200 201 202 203 204 205 206 207 208 209 210
#> [211] 211 212 213 214 215 216 217 218 219 220 221 222 223 224
#> [225] 225 226 227 228 229 230 231 232 233 234 235 236 237 238
#> [239] 239 240 241 242 243 244 245 246 247 248 249 250 251 252
#> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266
#> [267] 267 268 269 270 271 272 273 274 275 276 277 278 279 280
#> [281] 281 282 283 284 285 286 287 288 289 290 291 292 293 294
#> [295] 295 296 297 298 299 300 301 302 303 304 305 306 307 308
#> [309] 309 310 311 312 313 314 315 316 317 318 319 320 321 322
#> [323] 323 324 325 326 327 328 329 330 331 332 333 334 335 336
#> [337] 337 338 339 340 341 342 343 344 345 346 347 348 349 350
#> [351] 351 352 353 354 355 356 357 358 359 360 361 362 363 364
#> [365] 365 366 367 368 369 370 371 372 373 374 375 376 377 378
#> [379] 379 380 381 382 383 384 385 386 387 388 389 390 391 392
#> [393] 393 394 395 396 397 398 399 400 401 402 403 404 405 406
#> [407] 407 408 409 410 411 412 413 414 415 416 417 418 419 420
#> [421] 421 422 423 424 425 426 427 428 429 430 431 432 433 434
#> [435] 435 436 437 438 439 440 441 442 443 444 445 446 447 448
#> [449] 449 450 451 452 453 454 455 456 457 458 459 460 461 462
#> [463] 463 464 465 466 467 468 469 470 471 472 473 474 475 476
#> [477] 477 478 479 480 481 482 483 484 485 486 487 488 489 490
#> [491] 491 492 493 494 495 496 497 498 499 500 501 502 503 504
#> [505] 505 506 507 508 509 510 511 512 513 514 515 516 517 518
#> [519] 519 520 521 522 523 524 525 526 527 528 529 530 531 532
#> [533] 533 534 535 536 537 538 539 540 541 542 543 544 545 546
#> [547] 547 548 549 550 551 552 553 554 555 556 557 558 559 560
#> [561] 561 562 563 564 565 566 567 568 569 570 571 572 573 574
#> [575] 575 576 577 578 579 580 581 582 583 584 585 586 587 588
#> [589] 589 590 591 592 593 594 595 596 597 598 599 600 601 602
#> [603] 603 604 605 606 607 608 609 610 611 612 613 614 615 616
#> [617] 617 618 619 620 621 622 623 624 625 626 627 628 629 630
#> [631] 631 632 633 634 635 636 637 638 639 640 641 642 643 644
#> [645] 645 646 647 648 649 650 651 652 653 654 655 656 657 658
#> [659] 659 660 661 662 663 664 665 666 667 668 669 670 671 672
#> [673] 673 674 675 676 677 678 679 680 681 682 683 684 685 686
#> [687] 687 688 689 690 691 692 693 694 695 696 697 698 699 700
#> [701] 701 702 703 704 705 706 707 708 709 710 711 712 713 714
#> [715] 715 716 717 718 719 720 721 722 723 724 725 726 727 728
#> [729] 729 730 731 732 733 734 735 736 737 738 739 740 741 742
#> [743] 743 744 745 746 747 748 749 750 751 752 753 754 755 756
#> [757] 757 758 759 760 761 762 763 764 765 766 767 768 769 770
#> [771] 771 772 773 774 775 776 777 778 779 780 781 782 783 784
#> [785] 785 786 787 788 789 790 791 792 793 794 795 796 797 798
#> [799] 799 800 801 802 803 804 805 806 807 808 809 810 811 812
#> [813] 813 814 815 816 817 818 819 820 821 822 823 824 825 826
#> [827] 827 828 829 830 831 832 833 834 835 836 837 838 839 840
#> [841] 841 842 843 844 845 846 847 848 849 850 851 852 853 854
#> [855] 855 856 857 858 859 860 861 862 863 864 865 866 867 868
#> [869] 869 870 871 872 873 874 875 876 877 878 879 880 881 882
#> [883] 883 884 885 886 887 888 889 890 891 892 893 894 895 896
#> [897] 897 898 899 900 901 902 903 904 905 906 907 908 909 910
#> [911] 911 912 913 914 915 916 917 918 919 920 921 922 923 924
#> [925] 925 926 927 928 929 930 931 932 933 934 935 936 937 938
#> [939] 939 940 941 942 943 944 945 946 947 948 949 950 951 952
#> [953] 953 954 955 956 957 958 959 960 961 962 963 964 965 966
#> [967] 967 968 969 970 971 972 973 974 975 976 977 978 979 980
#> [981] 981 982 983 984 985 986 987 988 989 990 991 992 993 994
#> [995] 995 996 997 998 999 1000
#>
#> $formula
#> [1] "te( beta.1.,beta.2., bs='cr')"
#>
#> $pars
#> [1] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [5] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [9] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [13] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [17] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [21] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [25] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [29] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [33] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [37] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [41] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [45] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [49] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [53] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [57] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [61] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [65] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [69] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [73] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [77] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [81] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [85] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [89] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [93] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [97] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [101] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [105] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [109] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [113] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [117] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [121] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [125] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [129] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [133] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [137] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [141] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [145] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [149] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [153] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [157] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [161] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [165] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [169] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [173] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [177] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [181] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [185] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [189] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [193] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [197] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [201] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [205] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [209] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [213] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [217] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [221] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [225] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [229] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [233] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [237] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [241] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [245] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [249] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [253] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [257] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [261] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [265] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [269] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [273] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [277] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [281] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [285] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [289] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [293] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [297] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [301] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [305] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [309] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [313] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [317] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [321] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [325] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [329] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [333] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [337] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [341] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [345] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [349] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [353] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [357] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [361] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [365] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [369] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [373] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [377] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [381] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [385] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [389] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [393] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [397] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [401] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [405] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [409] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [413] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [417] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [421] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [425] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [429] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [433] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [437] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [441] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [445] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [449] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [453] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [457] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [461] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [465] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [469] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [473] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [477] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [481] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [485] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [489] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [493] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [497] "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2." "beta.1.,beta.2."
#> [501] "beta.1.,beta.2."
#>
#> $res
#> pars k evppi
#> 1 beta.1.,beta.2. 0 0.000000e+00
#> 2 beta.1.,beta.2. 100 0.000000e+00
#> 3 beta.1.,beta.2. 200 0.000000e+00
#> 4 beta.1.,beta.2. 300 0.000000e+00
#> 5 beta.1.,beta.2. 400 0.000000e+00
#> 6 beta.1.,beta.2. 500 0.000000e+00
#> 7 beta.1.,beta.2. 600 0.000000e+00
#> 8 beta.1.,beta.2. 700 0.000000e+00
#> 9 beta.1.,beta.2. 800 0.000000e+00
#> 10 beta.1.,beta.2. 900 0.000000e+00
#> 11 beta.1.,beta.2. 1000 0.000000e+00
#> 12 beta.1.,beta.2. 1100 0.000000e+00
#> 13 beta.1.,beta.2. 1200 0.000000e+00
#> 14 beta.1.,beta.2. 1300 0.000000e+00
#> 15 beta.1.,beta.2. 1400 0.000000e+00
#> 16 beta.1.,beta.2. 1500 0.000000e+00
#> 17 beta.1.,beta.2. 1600 0.000000e+00
#> 18 beta.1.,beta.2. 1700 0.000000e+00
#> 19 beta.1.,beta.2. 1800 0.000000e+00
#> 20 beta.1.,beta.2. 1900 0.000000e+00
#> 21 beta.1.,beta.2. 2000 0.000000e+00
#> 22 beta.1.,beta.2. 2100 0.000000e+00
#> 23 beta.1.,beta.2. 2200 0.000000e+00
#> 24 beta.1.,beta.2. 2300 0.000000e+00
#> 25 beta.1.,beta.2. 2400 0.000000e+00
#> 26 beta.1.,beta.2. 2500 0.000000e+00
#> 27 beta.1.,beta.2. 2600 0.000000e+00
#> 28 beta.1.,beta.2. 2700 0.000000e+00
#> 29 beta.1.,beta.2. 2800 0.000000e+00
#> 30 beta.1.,beta.2. 2900 0.000000e+00
#> 31 beta.1.,beta.2. 3000 0.000000e+00
#> 32 beta.1.,beta.2. 3100 0.000000e+00
#> 33 beta.1.,beta.2. 3200 0.000000e+00
#> 34 beta.1.,beta.2. 3300 0.000000e+00
#> 35 beta.1.,beta.2. 3400 2.457218e-05
#> 36 beta.1.,beta.2. 3500 9.285676e-05
#> 37 beta.1.,beta.2. 3600 1.611413e-04
#> 38 beta.1.,beta.2. 3700 2.294259e-04
#> 39 beta.1.,beta.2. 3800 2.977105e-04
#> 40 beta.1.,beta.2. 3900 3.659951e-04
#> 41 beta.1.,beta.2. 4000 4.342796e-04
#> 42 beta.1.,beta.2. 4100 5.025642e-04
#> 43 beta.1.,beta.2. 4200 5.708488e-04
#> 44 beta.1.,beta.2. 4300 6.391333e-04
#> 45 beta.1.,beta.2. 4400 7.074179e-04
#> 46 beta.1.,beta.2. 4500 8.359579e-04
#> 47 beta.1.,beta.2. 4600 9.696656e-04
#> 48 beta.1.,beta.2. 4700 1.103373e-03
#> 49 beta.1.,beta.2. 4800 1.237081e-03
#> 50 beta.1.,beta.2. 4900 1.370789e-03
#> 51 beta.1.,beta.2. 5000 1.504497e-03
#> 52 beta.1.,beta.2. 5100 1.670549e-03
#> 53 beta.1.,beta.2. 5200 1.859133e-03
#> 54 beta.1.,beta.2. 5300 2.047718e-03
#> 55 beta.1.,beta.2. 5400 2.236302e-03
#> 56 beta.1.,beta.2. 5500 2.424887e-03
#> 57 beta.1.,beta.2. 5600 2.613472e-03
#> 58 beta.1.,beta.2. 5700 2.802056e-03
#> 59 beta.1.,beta.2. 5800 2.990641e-03
#> 60 beta.1.,beta.2. 5900 3.179226e-03
#> 61 beta.1.,beta.2. 6000 3.388697e-03
#> 62 beta.1.,beta.2. 6100 3.632914e-03
#> 63 beta.1.,beta.2. 6200 3.877131e-03
#> 64 beta.1.,beta.2. 6300 4.139327e-03
#> 65 beta.1.,beta.2. 6400 4.557299e-03
#> 66 beta.1.,beta.2. 6500 5.018772e-03
#> 67 beta.1.,beta.2. 6600 5.518913e-03
#> 68 beta.1.,beta.2. 6700 6.019054e-03
#> 69 beta.1.,beta.2. 6800 6.519195e-03
#> 70 beta.1.,beta.2. 6900 7.072839e-03
#> 71 beta.1.,beta.2. 7000 7.631472e-03
#> 72 beta.1.,beta.2. 7100 8.238981e-03
#> 73 beta.1.,beta.2. 7200 8.914107e-03
#> 74 beta.1.,beta.2. 7300 9.667252e-03
#> 75 beta.1.,beta.2. 7400 1.050133e-02
#> 76 beta.1.,beta.2. 7500 1.136910e-02
#> 77 beta.1.,beta.2. 7600 1.233047e-02
#> 78 beta.1.,beta.2. 7700 1.330277e-02
#> 79 beta.1.,beta.2. 7800 1.435958e-02
#> 80 beta.1.,beta.2. 7900 1.545712e-02
#> 81 beta.1.,beta.2. 8000 1.665844e-02
#> 82 beta.1.,beta.2. 8100 1.791987e-02
#> 83 beta.1.,beta.2. 8200 1.926646e-02
#> 84 beta.1.,beta.2. 8300 2.069755e-02
#> 85 beta.1.,beta.2. 8400 2.215719e-02
#> 86 beta.1.,beta.2. 8500 2.365858e-02
#> 87 beta.1.,beta.2. 8600 2.536231e-02
#> 88 beta.1.,beta.2. 8700 2.721777e-02
#> 89 beta.1.,beta.2. 8800 2.926248e-02
#> 90 beta.1.,beta.2. 8900 3.142167e-02
#> 91 beta.1.,beta.2. 9000 3.366613e-02
#> 92 beta.1.,beta.2. 9100 3.594727e-02
#> 93 beta.1.,beta.2. 9200 3.828947e-02
#> 94 beta.1.,beta.2. 9300 4.066173e-02
#> 95 beta.1.,beta.2. 9400 4.308486e-02
#> 96 beta.1.,beta.2. 9500 4.554495e-02
#> 97 beta.1.,beta.2. 9600 4.810142e-02
#> 98 beta.1.,beta.2. 9700 5.076759e-02
#> 99 beta.1.,beta.2. 9800 5.354944e-02
#> 100 beta.1.,beta.2. 9900 5.645627e-02
#> 101 beta.1.,beta.2. 10000 5.953500e-02
#> 102 beta.1.,beta.2. 10100 6.272953e-02
#> 103 beta.1.,beta.2. 10200 6.605710e-02
#> 104 beta.1.,beta.2. 10300 6.946561e-02
#> 105 beta.1.,beta.2. 10400 7.299518e-02
#> 106 beta.1.,beta.2. 10500 7.661519e-02
#> 107 beta.1.,beta.2. 10600 8.044882e-02
#> 108 beta.1.,beta.2. 10700 8.454452e-02
#> 109 beta.1.,beta.2. 10800 8.880028e-02
#> 110 beta.1.,beta.2. 10900 9.316356e-02
#> 111 beta.1.,beta.2. 11000 9.757211e-02
#> 112 beta.1.,beta.2. 11100 1.020631e-01
#> 113 beta.1.,beta.2. 11200 1.066899e-01
#> 114 beta.1.,beta.2. 11300 1.115583e-01
#> 115 beta.1.,beta.2. 11400 1.165761e-01
#> 116 beta.1.,beta.2. 11500 1.217236e-01
#> 117 beta.1.,beta.2. 11600 1.269848e-01
#> 118 beta.1.,beta.2. 11700 1.324089e-01
#> 119 beta.1.,beta.2. 11800 1.379159e-01
#> 120 beta.1.,beta.2. 11900 1.435288e-01
#> 121 beta.1.,beta.2. 12000 1.492557e-01
#> 122 beta.1.,beta.2. 12100 1.550713e-01
#> 123 beta.1.,beta.2. 12200 1.610079e-01
#> 124 beta.1.,beta.2. 12300 1.670532e-01
#> 125 beta.1.,beta.2. 12400 1.733199e-01
#> 126 beta.1.,beta.2. 12500 1.797311e-01
#> 127 beta.1.,beta.2. 12600 1.862892e-01
#> 128 beta.1.,beta.2. 12700 1.929569e-01
#> 129 beta.1.,beta.2. 12800 1.998673e-01
#> 130 beta.1.,beta.2. 12900 2.069650e-01
#> 131 beta.1.,beta.2. 13000 2.141277e-01
#> 132 beta.1.,beta.2. 13100 2.214818e-01
#> 133 beta.1.,beta.2. 13200 2.290093e-01
#> 134 beta.1.,beta.2. 13300 2.366830e-01
#> 135 beta.1.,beta.2. 13400 2.444225e-01
#> 136 beta.1.,beta.2. 13500 2.522186e-01
#> 137 beta.1.,beta.2. 13600 2.601369e-01
#> 138 beta.1.,beta.2. 13700 2.681379e-01
#> 139 beta.1.,beta.2. 13800 2.761958e-01
#> 140 beta.1.,beta.2. 13900 2.844056e-01
#> 141 beta.1.,beta.2. 14000 2.927010e-01
#> 142 beta.1.,beta.2. 14100 3.011088e-01
#> 143 beta.1.,beta.2. 14200 3.096150e-01
#> 144 beta.1.,beta.2. 14300 3.182103e-01
#> 145 beta.1.,beta.2. 14400 3.270021e-01
#> 146 beta.1.,beta.2. 14500 3.359932e-01
#> 147 beta.1.,beta.2. 14600 3.451267e-01
#> 148 beta.1.,beta.2. 14700 3.543843e-01
#> 149 beta.1.,beta.2. 14800 3.637561e-01
#> 150 beta.1.,beta.2. 14900 3.733013e-01
#> 151 beta.1.,beta.2. 15000 3.829607e-01
#> 152 beta.1.,beta.2. 15100 3.927341e-01
#> 153 beta.1.,beta.2. 15200 4.026574e-01
#> 154 beta.1.,beta.2. 15300 4.127028e-01
#> 155 beta.1.,beta.2. 15400 4.228980e-01
#> 156 beta.1.,beta.2. 15500 4.332371e-01
#> 157 beta.1.,beta.2. 15600 4.437379e-01
#> 158 beta.1.,beta.2. 15700 4.544137e-01
#> 159 beta.1.,beta.2. 15800 4.651813e-01
#> 160 beta.1.,beta.2. 15900 4.761334e-01
#> 161 beta.1.,beta.2. 16000 4.872191e-01
#> 162 beta.1.,beta.2. 16100 4.984766e-01
#> 163 beta.1.,beta.2. 16200 5.098566e-01
#> 164 beta.1.,beta.2. 16300 5.214073e-01
#> 165 beta.1.,beta.2. 16400 5.330834e-01
#> 166 beta.1.,beta.2. 16500 5.449266e-01
#> 167 beta.1.,beta.2. 16600 5.569304e-01
#> 168 beta.1.,beta.2. 16700 5.690002e-01
#> 169 beta.1.,beta.2. 16800 5.812412e-01
#> 170 beta.1.,beta.2. 16900 5.936338e-01
#> 171 beta.1.,beta.2. 17000 6.061169e-01
#> 172 beta.1.,beta.2. 17100 6.187243e-01
#> 173 beta.1.,beta.2. 17200 6.314361e-01
#> 174 beta.1.,beta.2. 17300 6.442246e-01
#> 175 beta.1.,beta.2. 17400 6.570961e-01
#> 176 beta.1.,beta.2. 17500 6.700288e-01
#> 177 beta.1.,beta.2. 17600 6.830269e-01
#> 178 beta.1.,beta.2. 17700 6.960982e-01
#> 179 beta.1.,beta.2. 17800 7.092648e-01
#> 180 beta.1.,beta.2. 17900 7.225512e-01
#> 181 beta.1.,beta.2. 18000 7.358751e-01
#> 182 beta.1.,beta.2. 18100 7.492499e-01
#> 183 beta.1.,beta.2. 18200 7.627448e-01
#> 184 beta.1.,beta.2. 18300 7.763942e-01
#> 185 beta.1.,beta.2. 18400 7.901216e-01
#> 186 beta.1.,beta.2. 18500 8.039977e-01
#> 187 beta.1.,beta.2. 18600 8.179865e-01
#> 188 beta.1.,beta.2. 18700 8.320791e-01
#> 189 beta.1.,beta.2. 18800 8.462536e-01
#> 190 beta.1.,beta.2. 18900 8.605366e-01
#> 191 beta.1.,beta.2. 19000 8.749299e-01
#> 192 beta.1.,beta.2. 19100 8.893719e-01
#> 193 beta.1.,beta.2. 19200 9.039347e-01
#> 194 beta.1.,beta.2. 19300 9.186171e-01
#> 195 beta.1.,beta.2. 19400 9.334328e-01
#> 196 beta.1.,beta.2. 19500 9.483216e-01
#> 197 beta.1.,beta.2. 19600 9.632936e-01
#> 198 beta.1.,beta.2. 19700 9.783535e-01
#> 199 beta.1.,beta.2. 19800 9.935304e-01
#> 200 beta.1.,beta.2. 19900 1.008831e+00
#> 201 beta.1.,beta.2. 20000 1.024234e+00
#> 202 beta.1.,beta.2. 20100 1.039745e+00
#> 203 beta.1.,beta.2. 20200 1.055422e+00
#> 204 beta.1.,beta.2. 20300 1.071224e+00
#> 205 beta.1.,beta.2. 20400 1.084066e+00
#> 206 beta.1.,beta.2. 20500 1.074809e+00
#> 207 beta.1.,beta.2. 20600 1.065660e+00
#> 208 beta.1.,beta.2. 20700 1.056641e+00
#> 209 beta.1.,beta.2. 20800 1.047729e+00
#> 210 beta.1.,beta.2. 20900 1.038873e+00
#> 211 beta.1.,beta.2. 21000 1.030071e+00
#> 212 beta.1.,beta.2. 21100 1.021314e+00
#> 213 beta.1.,beta.2. 21200 1.012653e+00
#> 214 beta.1.,beta.2. 21300 1.004120e+00
#> 215 beta.1.,beta.2. 21400 9.956920e-01
#> 216 beta.1.,beta.2. 21500 9.872935e-01
#> 217 beta.1.,beta.2. 21600 9.789248e-01
#> 218 beta.1.,beta.2. 21700 9.706356e-01
#> 219 beta.1.,beta.2. 21800 9.623851e-01
#> 220 beta.1.,beta.2. 21900 9.542037e-01
#> 221 beta.1.,beta.2. 22000 9.461404e-01
#> 222 beta.1.,beta.2. 22100 9.381714e-01
#> 223 beta.1.,beta.2. 22200 9.302605e-01
#> 224 beta.1.,beta.2. 22300 9.224375e-01
#> 225 beta.1.,beta.2. 22400 9.147457e-01
#> 226 beta.1.,beta.2. 22500 9.071125e-01
#> 227 beta.1.,beta.2. 22600 8.995306e-01
#> 228 beta.1.,beta.2. 22700 8.919891e-01
#> 229 beta.1.,beta.2. 22800 8.844713e-01
#> 230 beta.1.,beta.2. 22900 8.770460e-01
#> 231 beta.1.,beta.2. 23000 8.697233e-01
#> 232 beta.1.,beta.2. 23100 8.625451e-01
#> 233 beta.1.,beta.2. 23200 8.554401e-01
#> 234 beta.1.,beta.2. 23300 8.483837e-01
#> 235 beta.1.,beta.2. 23400 8.413724e-01
#> 236 beta.1.,beta.2. 23500 8.344054e-01
#> 237 beta.1.,beta.2. 23600 8.275071e-01
#> 238 beta.1.,beta.2. 23700 8.206365e-01
#> 239 beta.1.,beta.2. 23800 8.138200e-01
#> 240 beta.1.,beta.2. 23900 8.071060e-01
#> 241 beta.1.,beta.2. 24000 8.004723e-01
#> 242 beta.1.,beta.2. 24100 7.938925e-01
#> 243 beta.1.,beta.2. 24200 7.873783e-01
#> 244 beta.1.,beta.2. 24300 7.809368e-01
#> 245 beta.1.,beta.2. 24400 7.745497e-01
#> 246 beta.1.,beta.2. 24500 7.682354e-01
#> 247 beta.1.,beta.2. 24600 7.619523e-01
#> 248 beta.1.,beta.2. 24700 7.557112e-01
#> 249 beta.1.,beta.2. 24800 7.495586e-01
#> 250 beta.1.,beta.2. 24900 7.434515e-01
#> 251 beta.1.,beta.2. 25000 7.374292e-01
#> 252 beta.1.,beta.2. 25100 7.314887e-01
#> 253 beta.1.,beta.2. 25200 7.256113e-01
#> 254 beta.1.,beta.2. 25300 7.197572e-01
#> 255 beta.1.,beta.2. 25400 7.139554e-01
#> 256 beta.1.,beta.2. 25500 7.081910e-01
#> 257 beta.1.,beta.2. 25600 7.024605e-01
#> 258 beta.1.,beta.2. 25700 6.967964e-01
#> 259 beta.1.,beta.2. 25800 6.912702e-01
#> 260 beta.1.,beta.2. 25900 6.858125e-01
#> 261 beta.1.,beta.2. 26000 6.804006e-01
#> 262 beta.1.,beta.2. 26100 6.750311e-01
#> 263 beta.1.,beta.2. 26200 6.697037e-01
#> 264 beta.1.,beta.2. 26300 6.644020e-01
#> 265 beta.1.,beta.2. 26400 6.591274e-01
#> 266 beta.1.,beta.2. 26500 6.539432e-01
#> 267 beta.1.,beta.2. 26600 6.488189e-01
#> 268 beta.1.,beta.2. 26700 6.437494e-01
#> 269 beta.1.,beta.2. 26800 6.387192e-01
#> 270 beta.1.,beta.2. 26900 6.337315e-01
#> 271 beta.1.,beta.2. 27000 6.287676e-01
#> 272 beta.1.,beta.2. 27100 6.238712e-01
#> 273 beta.1.,beta.2. 27200 6.190358e-01
#> 274 beta.1.,beta.2. 27300 6.143003e-01
#> 275 beta.1.,beta.2. 27400 6.096329e-01
#> 276 beta.1.,beta.2. 27500 6.050127e-01
#> 277 beta.1.,beta.2. 27600 6.004741e-01
#> 278 beta.1.,beta.2. 27700 5.960047e-01
#> 279 beta.1.,beta.2. 27800 5.915586e-01
#> 280 beta.1.,beta.2. 27900 5.871403e-01
#> 281 beta.1.,beta.2. 28000 5.827532e-01
#> 282 beta.1.,beta.2. 28100 5.783884e-01
#> 283 beta.1.,beta.2. 28200 5.740675e-01
#> 284 beta.1.,beta.2. 28300 5.697920e-01
#> 285 beta.1.,beta.2. 28400 5.655458e-01
#> 286 beta.1.,beta.2. 28500 5.613352e-01
#> 287 beta.1.,beta.2. 28600 5.571441e-01
#> 288 beta.1.,beta.2. 28700 5.529855e-01
#> 289 beta.1.,beta.2. 28800 5.488534e-01
#> 290 beta.1.,beta.2. 28900 5.447722e-01
#> 291 beta.1.,beta.2. 29000 5.406968e-01
#> 292 beta.1.,beta.2. 29100 5.366213e-01
#> 293 beta.1.,beta.2. 29200 5.325726e-01
#> 294 beta.1.,beta.2. 29300 5.285423e-01
#> 295 beta.1.,beta.2. 29400 5.245240e-01
#> 296 beta.1.,beta.2. 29500 5.205150e-01
#> 297 beta.1.,beta.2. 29600 5.165845e-01
#> 298 beta.1.,beta.2. 29700 5.126795e-01
#> 299 beta.1.,beta.2. 29800 5.088076e-01
#> 300 beta.1.,beta.2. 29900 5.049948e-01
#> 301 beta.1.,beta.2. 30000 5.012110e-01
#> 302 beta.1.,beta.2. 30100 4.975225e-01
#> 303 beta.1.,beta.2. 30200 4.938624e-01
#> 304 beta.1.,beta.2. 30300 4.902186e-01
#> 305 beta.1.,beta.2. 30400 4.865747e-01
#> 306 beta.1.,beta.2. 30500 4.829573e-01
#> 307 beta.1.,beta.2. 30600 4.794062e-01
#> 308 beta.1.,beta.2. 30700 4.758743e-01
#> 309 beta.1.,beta.2. 30800 4.723592e-01
#> 310 beta.1.,beta.2. 30900 4.688739e-01
#> 311 beta.1.,beta.2. 31000 4.654132e-01
#> 312 beta.1.,beta.2. 31100 4.619793e-01
#> 313 beta.1.,beta.2. 31200 4.585647e-01
#> 314 beta.1.,beta.2. 31300 4.551584e-01
#> 315 beta.1.,beta.2. 31400 4.517521e-01
#> 316 beta.1.,beta.2. 31500 4.483486e-01
#> 317 beta.1.,beta.2. 31600 4.449656e-01
#> 318 beta.1.,beta.2. 31700 4.416163e-01
#> 319 beta.1.,beta.2. 31800 4.383141e-01
#> 320 beta.1.,beta.2. 31900 4.350518e-01
#> 321 beta.1.,beta.2. 32000 4.318246e-01
#> 322 beta.1.,beta.2. 32100 4.286196e-01
#> 323 beta.1.,beta.2. 32200 4.254631e-01
#> 324 beta.1.,beta.2. 32300 4.223070e-01
#> 325 beta.1.,beta.2. 32400 4.191951e-01
#> 326 beta.1.,beta.2. 32500 4.161302e-01
#> 327 beta.1.,beta.2. 32600 4.131096e-01
#> 328 beta.1.,beta.2. 32700 4.101110e-01
#> 329 beta.1.,beta.2. 32800 4.071347e-01
#> 330 beta.1.,beta.2. 32900 4.041827e-01
#> 331 beta.1.,beta.2. 33000 4.012589e-01
#> 332 beta.1.,beta.2. 33100 3.983743e-01
#> 333 beta.1.,beta.2. 33200 3.955212e-01
#> 334 beta.1.,beta.2. 33300 3.927254e-01
#> 335 beta.1.,beta.2. 33400 3.899432e-01
#> 336 beta.1.,beta.2. 33500 3.871855e-01
#> 337 beta.1.,beta.2. 33600 3.844689e-01
#> 338 beta.1.,beta.2. 33700 3.817796e-01
#> 339 beta.1.,beta.2. 33800 3.791394e-01
#> 340 beta.1.,beta.2. 33900 3.765335e-01
#> 341 beta.1.,beta.2. 34000 3.739600e-01
#> 342 beta.1.,beta.2. 34100 3.714164e-01
#> 343 beta.1.,beta.2. 34200 3.688934e-01
#> 344 beta.1.,beta.2. 34300 3.663704e-01
#> 345 beta.1.,beta.2. 34400 3.638535e-01
#> 346 beta.1.,beta.2. 34500 3.613474e-01
#> 347 beta.1.,beta.2. 34600 3.588413e-01
#> 348 beta.1.,beta.2. 34700 3.563576e-01
#> 349 beta.1.,beta.2. 34800 3.539089e-01
#> 350 beta.1.,beta.2. 34900 3.514843e-01
#> 351 beta.1.,beta.2. 35000 3.490616e-01
#> 352 beta.1.,beta.2. 35100 3.466548e-01
#> 353 beta.1.,beta.2. 35200 3.442652e-01
#> 354 beta.1.,beta.2. 35300 3.418779e-01
#> 355 beta.1.,beta.2. 35400 3.395314e-01
#> 356 beta.1.,beta.2. 35500 3.372204e-01
#> 357 beta.1.,beta.2. 35600 3.349244e-01
#> 358 beta.1.,beta.2. 35700 3.326430e-01
#> 359 beta.1.,beta.2. 35800 3.303822e-01
#> 360 beta.1.,beta.2. 35900 3.281223e-01
#> 361 beta.1.,beta.2. 36000 3.258784e-01
#> 362 beta.1.,beta.2. 36100 3.236347e-01
#> 363 beta.1.,beta.2. 36200 3.214071e-01
#> 364 beta.1.,beta.2. 36300 3.191795e-01
#> 365 beta.1.,beta.2. 36400 3.169807e-01
#> 366 beta.1.,beta.2. 36500 3.148009e-01
#> 367 beta.1.,beta.2. 36600 3.126353e-01
#> 368 beta.1.,beta.2. 36700 3.105210e-01
#> 369 beta.1.,beta.2. 36800 3.084279e-01
#> 370 beta.1.,beta.2. 36900 3.063603e-01
#> 371 beta.1.,beta.2. 37000 3.043336e-01
#> 372 beta.1.,beta.2. 37100 3.023203e-01
#> 373 beta.1.,beta.2. 37200 3.003220e-01
#> 374 beta.1.,beta.2. 37300 2.983323e-01
#> 375 beta.1.,beta.2. 37400 2.963581e-01
#> 376 beta.1.,beta.2. 37500 2.944046e-01
#> 377 beta.1.,beta.2. 37600 2.924985e-01
#> 378 beta.1.,beta.2. 37700 2.906183e-01
#> 379 beta.1.,beta.2. 37800 2.887512e-01
#> 380 beta.1.,beta.2. 37900 2.869099e-01
#> 381 beta.1.,beta.2. 38000 2.850765e-01
#> 382 beta.1.,beta.2. 38100 2.832430e-01
#> 383 beta.1.,beta.2. 38200 2.814097e-01
#> 384 beta.1.,beta.2. 38300 2.796030e-01
#> 385 beta.1.,beta.2. 38400 2.778181e-01
#> 386 beta.1.,beta.2. 38500 2.760596e-01
#> 387 beta.1.,beta.2. 38600 2.743042e-01
#> 388 beta.1.,beta.2. 38700 2.725626e-01
#> 389 beta.1.,beta.2. 38800 2.708210e-01
#> 390 beta.1.,beta.2. 38900 2.690877e-01
#> 391 beta.1.,beta.2. 39000 2.673614e-01
#> 392 beta.1.,beta.2. 39100 2.656351e-01
#> 393 beta.1.,beta.2. 39200 2.639213e-01
#> 394 beta.1.,beta.2. 39300 2.622214e-01
#> 395 beta.1.,beta.2. 39400 2.605248e-01
#> 396 beta.1.,beta.2. 39500 2.588283e-01
#> 397 beta.1.,beta.2. 39600 2.571434e-01
#> 398 beta.1.,beta.2. 39700 2.554615e-01
#> 399 beta.1.,beta.2. 39800 2.537810e-01
#> 400 beta.1.,beta.2. 39900 2.521252e-01
#> 401 beta.1.,beta.2. 40000 2.504935e-01
#> 402 beta.1.,beta.2. 40100 2.488892e-01
#> 403 beta.1.,beta.2. 40200 2.472959e-01
#> 404 beta.1.,beta.2. 40300 2.457275e-01
#> 405 beta.1.,beta.2. 40400 2.441923e-01
#> 406 beta.1.,beta.2. 40500 2.426613e-01
#> 407 beta.1.,beta.2. 40600 2.411526e-01
#> 408 beta.1.,beta.2. 40700 2.396684e-01
#> 409 beta.1.,beta.2. 40800 2.382291e-01
#> 410 beta.1.,beta.2. 40900 2.367951e-01
#> 411 beta.1.,beta.2. 41000 2.353736e-01
#> 412 beta.1.,beta.2. 41100 2.339666e-01
#> 413 beta.1.,beta.2. 41200 2.325611e-01
#> 414 beta.1.,beta.2. 41300 2.311557e-01
#> 415 beta.1.,beta.2. 41400 2.297563e-01
#> 416 beta.1.,beta.2. 41500 2.283653e-01
#> 417 beta.1.,beta.2. 41600 2.269863e-01
#> 418 beta.1.,beta.2. 41700 2.256259e-01
#> 419 beta.1.,beta.2. 41800 2.242938e-01
#> 420 beta.1.,beta.2. 41900 2.229744e-01
#> 421 beta.1.,beta.2. 42000 2.216551e-01
#> 422 beta.1.,beta.2. 42100 2.203358e-01
#> 423 beta.1.,beta.2. 42200 2.190165e-01
#> 424 beta.1.,beta.2. 42300 2.176971e-01
#> 425 beta.1.,beta.2. 42400 2.163804e-01
#> 426 beta.1.,beta.2. 42500 2.150968e-01
#> 427 beta.1.,beta.2. 42600 2.138344e-01
#> 428 beta.1.,beta.2. 42700 2.125824e-01
#> 429 beta.1.,beta.2. 42800 2.113339e-01
#> 430 beta.1.,beta.2. 42900 2.100859e-01
#> 431 beta.1.,beta.2. 43000 2.088515e-01
#> 432 beta.1.,beta.2. 43100 2.076339e-01
#> 433 beta.1.,beta.2. 43200 2.064492e-01
#> 434 beta.1.,beta.2. 43300 2.052776e-01
#> 435 beta.1.,beta.2. 43400 2.041179e-01
#> 436 beta.1.,beta.2. 43500 2.029666e-01
#> 437 beta.1.,beta.2. 43600 2.018338e-01
#> 438 beta.1.,beta.2. 43700 2.007256e-01
#> 439 beta.1.,beta.2. 43800 1.996347e-01
#> 440 beta.1.,beta.2. 43900 1.985587e-01
#> 441 beta.1.,beta.2. 44000 1.974892e-01
#> 442 beta.1.,beta.2. 44100 1.964197e-01
#> 443 beta.1.,beta.2. 44200 1.953502e-01
#> 444 beta.1.,beta.2. 44300 1.942885e-01
#> 445 beta.1.,beta.2. 44400 1.932456e-01
#> 446 beta.1.,beta.2. 44500 1.922157e-01
#> 447 beta.1.,beta.2. 44600 1.911862e-01
#> 448 beta.1.,beta.2. 44700 1.901613e-01
#> 449 beta.1.,beta.2. 44800 1.891454e-01
#> 450 beta.1.,beta.2. 44900 1.881421e-01
#> 451 beta.1.,beta.2. 45000 1.871387e-01
#> 452 beta.1.,beta.2. 45100 1.861354e-01
#> 453 beta.1.,beta.2. 45200 1.851410e-01
#> 454 beta.1.,beta.2. 45300 1.841523e-01
#> 455 beta.1.,beta.2. 45400 1.831884e-01
#> 456 beta.1.,beta.2. 45500 1.822613e-01
#> 457 beta.1.,beta.2. 45600 1.813469e-01
#> 458 beta.1.,beta.2. 45700 1.804353e-01
#> 459 beta.1.,beta.2. 45800 1.795236e-01
#> 460 beta.1.,beta.2. 45900 1.786235e-01
#> 461 beta.1.,beta.2. 46000 1.777250e-01
#> 462 beta.1.,beta.2. 46100 1.768265e-01
#> 463 beta.1.,beta.2. 46200 1.759280e-01
#> 464 beta.1.,beta.2. 46300 1.750408e-01
#> 465 beta.1.,beta.2. 46400 1.741682e-01
#> 466 beta.1.,beta.2. 46500 1.733006e-01
#> 467 beta.1.,beta.2. 46600 1.724417e-01
#> 468 beta.1.,beta.2. 46700 1.715828e-01
#> 469 beta.1.,beta.2. 46800 1.707240e-01
#> 470 beta.1.,beta.2. 46900 1.698655e-01
#> 471 beta.1.,beta.2. 47000 1.690192e-01
#> 472 beta.1.,beta.2. 47100 1.681822e-01
#> 473 beta.1.,beta.2. 47200 1.673487e-01
#> 474 beta.1.,beta.2. 47300 1.665152e-01
#> 475 beta.1.,beta.2. 47400 1.656817e-01
#> 476 beta.1.,beta.2. 47500 1.648482e-01
#> 477 beta.1.,beta.2. 47600 1.640322e-01
#> 478 beta.1.,beta.2. 47700 1.632500e-01
#> 479 beta.1.,beta.2. 47800 1.624679e-01
#> 480 beta.1.,beta.2. 47900 1.616857e-01
#> 481 beta.1.,beta.2. 48000 1.609060e-01
#> 482 beta.1.,beta.2. 48100 1.601366e-01
#> 483 beta.1.,beta.2. 48200 1.593672e-01
#> 484 beta.1.,beta.2. 48300 1.585978e-01
#> 485 beta.1.,beta.2. 48400 1.578284e-01
#> 486 beta.1.,beta.2. 48500 1.570590e-01
#> 487 beta.1.,beta.2. 48600 1.562896e-01
#> 488 beta.1.,beta.2. 48700 1.555302e-01
#> 489 beta.1.,beta.2. 48800 1.547736e-01
#> 490 beta.1.,beta.2. 48900 1.540170e-01
#> 491 beta.1.,beta.2. 49000 1.532604e-01
#> 492 beta.1.,beta.2. 49100 1.525038e-01
#> 493 beta.1.,beta.2. 49200 1.517472e-01
#> 494 beta.1.,beta.2. 49300 1.509906e-01
#> 495 beta.1.,beta.2. 49400 1.502340e-01
#> 496 beta.1.,beta.2. 49500 1.494882e-01
#> 497 beta.1.,beta.2. 49600 1.487427e-01
#> 498 beta.1.,beta.2. 49700 1.479973e-01
#> 499 beta.1.,beta.2. 49800 1.472519e-01
#> 500 beta.1.,beta.2. 49900 1.465064e-01
#> 501 beta.1.,beta.2. 50000 1.457650e-01
#>
#> attr(,"class")
#> [1] "evppi" "list"