Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples
Source:R/Model-methods.R
biomarker.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Usage
biomarker(xLevel, model, samples, ...)
# S4 method for class 'integer,DualEndpoint,Samples'
biomarker(xLevel, model, samples, ...)Arguments
- xLevel
(
integer)
the levels for the doses the patients have been given w.r.t dose grid. SeeDatafor more details.- model
(
DualEndpoint)
the model.- samples
(
Samples)
the samples of model's parameters that store the value of biomarker levels for all doses on the dose grid.- ...
not used.
Details
This function simply returns a specific columns (with the indices equal
to xLevel) of the biomarker samples matrix, which is included in the the
samples object.
Examples
# Create the data.
my_data <- DataDual(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
ID = 1:17,
cohort = c(
1L,
2L,
3L,
4L,
5L,
6L,
6L,
6L,
7L,
7L,
7L,
8L,
8L,
8L,
9L,
9L,
9L
),
w = c(
0.31,
0.42,
0.59,
0.45,
0.6,
0.7,
0.55,
0.6,
0.52,
0.54,
0.56,
0.43,
0.41,
0.39,
0.34,
0.38,
0.21
),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
mean = c(0, 1),
cov = matrix(c(1, 0, 0, 1), nrow = 2),
sigma2betaW = 0.01,
sigma2W = c(a = 0.1, b = 0.1),
rho = c(a = 1, b = 1),
rw1 = TRUE
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(
burnin = 100,
step = 2,
samples = 500
)
my_samples <- mcmc(my_data, my_model, my_options)
# Obtain the biomarker levels (samples) for the second dose from the dose grid,
# which is 0.5.
biomarker(
xLevel = 2L,
model = my_model,
samples = my_samples
)
#> [1] 0.43026211 0.45131850 0.39380979 0.52566904 0.44627087 0.40601049
#> [7] 0.42065494 0.43232603 0.55707081 0.39378351 0.37215468 0.48241023
#> [13] 0.41488911 0.39157462 0.38519177 0.37408688 0.34644024 0.37242171
#> [19] 0.55810838 0.50591901 0.54552695 0.50487546 0.43249218 0.49550168
#> [25] 0.47712592 0.49542807 0.51025900 0.51743746 0.46142855 0.49274214
#> [31] 0.46368892 0.50577985 0.45135781 0.48476940 0.38564123 0.46903608
#> [37] 0.44432307 0.40453772 0.44404011 0.33044144 0.30762967 0.38202722
#> [43] 0.42061159 0.44898588 0.43482690 0.48073491 0.48035114 0.49709696
#> [49] 0.52202583 0.48396963 0.40502205 0.52087296 0.39597659 0.26604342
#> [55] 0.39714875 0.37597313 0.40343790 0.45427623 0.38469723 0.36827904
#> [61] 0.43468787 0.44794407 0.37109012 0.51155412 0.53745690 0.44930280
#> [67] 0.33623861 0.38107450 0.39356363 0.45480340 0.43631623 0.38563574
#> [73] 0.38009493 0.48449430 0.44525360 0.53119916 0.51525312 0.43594639
#> [79] 0.49315951 0.43762219 0.46306671 0.65249871 0.60094340 0.66245219
#> [85] 0.62746516 0.63169440 0.59165687 0.47029510 0.42848547 0.47201859
#> [91] 0.42692822 0.39905786 0.43341521 0.38041716 0.30917151 0.35005693
#> [97] 0.37495792 0.40789895 0.26295576 0.20769909 0.33306749 0.29157882
#> [103] 0.28984833 0.29070472 0.20820384 0.28809408 0.29968313 0.17920711
#> [109] -0.02466640 0.11271554 0.35793027 0.33981862 0.38686518 0.34292116
#> [115] 0.32688148 0.31911403 0.31443727 0.18681964 0.26829858 0.41575809
#> [121] 0.48857595 0.46854573 0.41090012 0.38954611 0.20631790 0.14501242
#> [127] 0.06914466 0.22729848 0.31011661 0.54072940 0.65715032 0.66157336
#> [133] 0.42910298 0.45590459 0.40694884 0.44029181 0.52155298 0.21630453
#> [139] 0.19751340 0.29011982 0.37564498 0.41567967 0.43212050 0.41782149
#> [145] 0.47119115 0.41168223 0.42281810 0.47357704 0.44275536 0.44531542
#> [151] 0.45718276 0.39439875 0.50107347 0.58748935 0.56783330 0.70720323
#> [157] 0.66508042 0.51749751 0.43246835 0.38490571 0.39485253 0.40500383
#> [163] 0.41599551 0.41276655 0.48060437 0.48248167 0.50904945 0.46227557
#> [169] 0.37447389 0.36598625 0.42591361 0.44896649 0.48278696 0.46534016
#> [175] 0.50063995 0.46908939 0.45926367 0.54530222 0.35630344 0.27641421
#> [181] 0.21907666 0.27390022 0.36720687 0.30322413 0.38544659 0.34068356
#> [187] 0.36004974 0.31488098 0.32467703 0.43869342 0.44209702 0.56232409
#> [193] 0.55476155 0.64039459 0.72174034 0.78623222 0.70022312 0.56437573
#> [199] 0.48492826 0.44872881 0.35335999 0.41575069 0.49913488 0.35477569
#> [205] 0.35206698 0.38863956 0.33757738 0.32885254 0.45612472 0.24553768
#> [211] 0.31549807 0.46051346 0.40364873 0.38774097 0.45330294 0.44589433
#> [217] 0.50671905 0.52949775 0.52931769 0.58304448 0.56146996 0.55133534
#> [223] 0.39359948 0.40314135 0.36255157 0.36057361 0.31590690 0.25282163
#> [229] 0.29983377 0.32110260 0.29331261 0.32886307 0.33856503 0.31409646
#> [235] 0.34780030 0.40352436 0.46419103 0.47741234 0.50010524 0.44536449
#> [241] 0.45419632 0.50456553 0.52983494 0.52852609 0.50704201 0.50867951
#> [247] 0.48546658 0.46586884 0.42256010 0.42841704 0.33984140 0.41270775
#> [253] 0.40527497 0.48808369 0.56185145 0.63659906 0.56626357 0.60852272
#> [259] 0.52772054 0.62656843 0.47830831 0.49079606 0.42026867 0.29880301
#> [265] 0.28917272 0.19333139 0.35758887 0.39614910 0.33855142 0.31587349
#> [271] 0.29496170 0.38702940 0.51784601 0.50943283 0.46706551 0.43746131
#> [277] 0.43562390 0.48460158 0.59200439 0.44989731 0.47058063 0.45718062
#> [283] 0.49240008 0.41220809 0.46086929 0.44941695 0.42400063 0.35046454
#> [289] 0.44708000 0.45677424 0.38316863 0.41526538 0.45927466 0.48520191
#> [295] 0.50961757 0.42682758 0.37253095 0.34665029 0.37465316 0.38013675
#> [301] 0.33357080 0.35609150 0.37507119 0.38124110 0.34200598 0.34825777
#> [307] 0.46129900 0.37332596 0.34789886 0.47925652 0.52624042 0.53112756
#> [313] 0.54732551 0.54079418 0.41354558 0.33864067 0.41602387 0.35498754
#> [319] 0.42333472 0.46810806 0.39548106 0.36354764 0.48869541 0.52741879
#> [325] 0.42649374 0.42863734 0.41385816 0.58411683 0.59293527 0.55163281
#> [331] 0.58590745 0.55190623 0.54884218 0.57134409 0.42999287 0.45896290
#> [337] 0.44869111 0.36817601 0.43573223 0.44333539 0.41152657 0.42359905
#> [343] 0.40106156 0.53655442 0.44246981 0.39005279 0.44271767 0.48185496
#> [349] 0.42168389 0.45989266 0.49577922 0.58227179 0.66944430 0.59902919
#> [355] 0.44665204 0.34166558 0.46434209 0.50782390 0.40743226 0.51129656
#> [361] 0.42603957 0.46911668 0.43102833 0.42157730 0.46687866 0.43265504
#> [367] 0.48870237 0.43221409 0.39875686 0.36157435 0.35230193 0.29089211
#> [373] 0.34843682 0.28521506 0.31282678 0.36970682 0.42516159 0.44280629
#> [379] 0.36117980 0.26977567 0.46226240 0.53281084 0.49700218 0.50052753
#> [385] 0.48565322 0.43854094 0.47036878 0.51950964 0.50350011 0.39736052
#> [391] 0.43523459 0.57187860 0.55094152 0.44877716 0.44615699 0.38490590
#> [397] 0.42606742 0.32739912 0.31893728 0.33013495 0.36173795 0.39825607
#> [403] 0.40104530 0.39849026 0.51719084 0.57531063 0.58699785 0.51591564
#> [409] 0.39834757 0.35339234 0.41170637 0.40548237 0.48772060 0.58029072
#> [415] 0.61769147 0.51037902 0.39419750 0.36366981 0.32280753 0.36188550
#> [421] 0.38360585 0.45394989 0.47325208 0.45772638 0.39189669 0.48359494
#> [427] 0.49077700 0.41346304 0.47895627 0.51075646 0.49475015 0.67206165
#> [433] 0.78617259 0.64813552 0.48056132 0.27584649 0.31367343 0.30295836
#> [439] 0.39236681 0.34235550 0.36990423 0.45437103 0.47702290 0.71272853
#> [445] 0.71868972 0.57651708 0.56752183 0.55950798 0.44422059 0.36069151
#> [451] 0.32745701 0.24551903 0.35380013 0.37427931 0.49368383 0.40456225
#> [457] 0.37048405 0.31617099 0.34029750 0.30436956 0.32229244 0.31665563
#> [463] 0.40844061 0.41094008 0.42591136 0.41494378 0.37189488 0.36088748
#> [469] 0.35844725 0.44536503 0.47805643 0.47955758 0.45077954 0.36294735
#> [475] 0.44412157 0.49745713 0.46698855 0.38225442 0.47695565 0.48997130
#> [481] 0.54433380 0.45102317 0.48759618 0.51987230 0.54270875 0.50587012
#> [487] 0.45381791 0.44889229 0.50718211 0.46641558 0.39455903 0.32197232
#> [493] 0.38476648 0.44501432 0.41767731 0.45520990 0.48393261 0.52286548
#> [499] 0.67142350 0.48122809