# Fitting models and displaying output

      library(ameras)

# Introduction

All functionality of the package is included in the function `ameras`. This vignette demonstrates the basic functionality and the generated output. For more information on obtaining confidence/credible intervals, see Confidence intervals.

## Model specification

Models are specified through formulas of the form `Y~dose(dose_expression, model="ERR", deg=1, modifier=M1+M2)+X1+X2`. Here `dose_expression` specifies the dose realization columns and is parsed by `eval_select` from the `tidyselect` package. Useful examples are `D1:D1000` if the doses are in a sequence of columns with sequential names such as `D1`-`D1000`, and `all_of(dosevars)` where `dosevars` is a vector with the names of all dose columns. Further, `model` specifies, for non-Gaussian families, whether to use the exponential dose-response model (`model="EXP"`), the linear-exponential model (`model="LINEXP"`) or the linear ERR model (`model="ERR"`). Next, `deg` is used to specify whether a quadratic dose term should (`deg=2`) or should not (`deg=1`) be estimated for the exponential or linear ERR dose-response model. The `modifier` term is optional and used to specify binary effect modification variables. Finally, `X1` and `X2` above represent optional additional covariates, which can include factor variables and interactions such as `X1*X2`. Note that interactions in the modifier term are not allowed, e.g. `M1*M2`. When `deg`, `modifier`, and `model` are not supplied, the defaults are `deg=1`, no effect modifiers, and `model="ERR"`.

## Example data

The included example dataset contains 3,000 individuals with 10 exposure replicates `V1`-`V10`, binary covariates `X1` and `X2` and effect modifiers `M1` and `M2`, and outcomes of all types (`Y.gaussian`, `Y.binomial`, `Y.poisson`, `status`, `time`, `Y.multinomial`, `Y.clogit`, and `setnr`).

    data(data, package="ameras")
    head(data)
    #>    Y.gaussian Y.binomial Y.poisson       time status setnr Y.clogit
    #> 1 -0.32647093          0         0 0.30276565      0     1        0
    #> 2 -0.18734036          1         0 0.19735142      1     1        1
    #> 3  0.08404044          0         2 0.30276565      0     1        0
    #> 4  0.22432504          0         0 0.23602584      1     1        0
    #> 5 -0.46317255          0         0 0.30276565      0     2        0
    #> 6 -1.41036573          0         0 0.07838133      1     2        0
    #>   Y.multinomial X1 X2 M1 M2         V1         V2         V3         V4
    #> 1             3  0  0  0  1 0.42868043 0.61542487 0.41960219 0.49265549
    #> 2             2  1  0  1  0 0.73321154 0.35512449 0.41876478 0.49235658
    #> 3             2  0  0  1  0 0.70369712 0.43407408 1.04115924 0.79882088
    #> 4             3  0  0  1  0 0.01845324 0.01373367 0.02733303 0.01912686
    #> 5             3  1  0  0  0 0.39389441 0.40087181 0.61932032 0.51715526
    #> 6             2  1  1  1  0 0.01493158 0.02335143 0.01828983 0.02705350
    #>           V5         V6         V7         V8         V9        V10
    #> 1 0.31363762 0.42218455 0.42464021 0.29630858 0.38211182 0.45751570
    #> 2 0.49515815 0.56837639 0.61126842 0.67723449 0.53361810 0.49393510
    #> 3 0.66613754 0.72346942 0.64077434 0.79894534 0.98278177 1.06068250
    #> 4 0.01917956 0.03056413 0.01536966 0.02135999 0.01548655 0.01596626
    #> 5 0.36440322 0.60255525 0.47512525 0.52567606 0.53391825 0.56026531
    #> 6 0.02298922 0.02399258 0.01890339 0.02094013 0.02303085 0.02091743

# Linear regression & displaying output

Now we fit all methods to the data through one function call:

    set.seed(12345)
    fit.ameras.linreg <- ameras(Y.gaussian~dose(V1:V10)+X1+X2, data=data,
                                family="gaussian", niter.BMA=5000, nburnin.BMA=1000,
                                methods=c("RC", "ERC", "MCML", "FMA", "BMA"))
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

The output is a list object with a `call` component, and one component for each method, each being a list:

    str(fit.ameras.linreg)
    #> List of 14
    #>  $ call              : language ameras(formula = Y.gaussian ~ dose(V1:V10) + X1 + X2, data = data, family = "gaussian",      methods = c("RC", "E| __truncated__ ...
    #>  $ formula           :Class 'formula'  language Y.gaussian ~ dose(V1:V10) + X1 + X2
    #>   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
    #>  $ num.rows          : int 3000
    #>  $ num.replicates    : int 10
    #>  $ transform         :function (params, boundcheck = FALSE, ...)
    #>  $ transform.jacobian:function (params, ...)
    #>  $ other.args        : list()
    #>  $ model             :List of 18
    #>   ..$ data        :'data.frame': 3000 obs. of  23 variables:
    #>   .. ..$ Y.gaussian   : num [1:3000] -0.326 -0.187 0.084 0.224 -0.463 ...
    #>   .. ..$ Y.binomial   : int [1:3000] 0 1 0 0 0 0 1 0 0 0 ...
    #>   .. ..$ Y.poisson    : int [1:3000] 0 0 2 0 0 0 4 1 0 0 ...
    #>   .. ..$ time         : num [1:3000] 0.303 0.197 0.303 0.236 0.303 ...
    #>   .. ..$ status       : num [1:3000] 0 1 0 1 0 1 0 0 1 0 ...
    #>   .. ..$ setnr        : int [1:3000] 1 1 1 1 2 2 2 2 3 3 ...
    #>   .. ..$ Y.clogit     : num [1:3000] 0 1 0 0 0 0 1 0 0 1 ...
    #>   .. ..$ Y.multinomial: Factor w/ 3 levels "1","2","3": 3 2 2 3 3 2 1 3 1 1 ...
    #>   .. ..$ X1           : int [1:3000] 0 1 0 0 1 1 0 0 1 1 ...
    #>   .. ..$ X2           : int [1:3000] 0 0 0 0 0 1 0 0 0 0 ...
    #>   .. ..$ M1           : int [1:3000] 0 1 1 1 0 1 0 1 1 0 ...
    #>   .. ..$ M2           : int [1:3000] 1 0 0 0 0 0 1 0 0 0 ...
    #>   .. ..$ V1           : num [1:3000] 0.4287 0.7332 0.7037 0.0185 0.3939 ...
    #>   .. ..$ V2           : num [1:3000] 0.6154 0.3551 0.4341 0.0137 0.4009 ...
    #>   .. ..$ V3           : num [1:3000] 0.4196 0.4188 1.0412 0.0273 0.6193 ...
    #>   .. ..$ V4           : num [1:3000] 0.4927 0.4924 0.7988 0.0191 0.5172 ...
    #>   .. ..$ V5           : num [1:3000] 0.3136 0.4952 0.6661 0.0192 0.3644 ...
    #>   .. ..$ V6           : num [1:3000] 0.4222 0.5684 0.7235 0.0306 0.6026 ...
    #>   .. ..$ V7           : num [1:3000] 0.4246 0.6113 0.6408 0.0154 0.4751 ...
    #>   .. ..$ V8           : num [1:3000] 0.2963 0.6772 0.7989 0.0214 0.5257 ...
    #>   .. ..$ V9           : num [1:3000] 0.3821 0.5336 0.9828 0.0155 0.5339 ...
    #>   .. ..$ V10          : num [1:3000] 0.458 0.494 1.061 0.016 0.56 ...
    #>   .. ..$ rcdose_ameras: num [1:3000] 0.4253 0.5379 0.7851 0.0197 0.4993 ...
    #>   ..$ keep.data   : logi TRUE
    #>   ..$ family      : chr "gaussian"
    #>   ..$ dosevars    : chr [1:10] "V1" "V2" "V3" "V4" ...
    #>   ..$ Y           : chr "Y.gaussian"
    #>   ..$ M           : NULL
    #>   ..$ X_formula   :Class 'formula'  language ~X1 + X2
    #>   .. .. ..- attr(*, ".Environment")=<environment: 0xb78cf2120>
    #>   ..$ X           : int [1:2] 9 10
    #>   ..$ offset      : NULL
    #>   ..$ entry       : NULL
    #>   ..$ exit        : NULL
    #>   ..$ status      : NULL
    #>   ..$ setnr       : NULL
    #>   ..$ deg         : num 1
    #>   ..$ doseRRmod   : chr "ERR"
    #>   ..$ loglim      : num 1e-30
    #>   ..$ optim.method: chr "Nelder-Mead"
    #>   ..$ control     : NULL
    #>  $ CI.computed       : logi FALSE
    #>  $ RC                :List of 7
    #>   ..$ coefficients: Named num [1:5] -1.362 0.481 -0.519 1.166 1.108
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ sd          : Named num [1:5] 0.0367 0.0405 0.0497 0.0204 0.0143
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ vcov        : num [1:5, 1:5] 1.34e-03 -8.36e-04 -4.86e-04 -4.15e-04 1.55e-09 ...
    #>   .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. ..$ : chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   .. .. ..$ : chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ optim       :List of 4
    #>   .. ..$ par        : num [1:5] -1.362 0.481 -0.519 1.166 0.102
    #>   .. ..$ hessian    : num [1:5, 1:5] 2.45e+03 1.23e+03 5.12e+02 2.43e+03 2.15e-03 ...
    #>   .. ..$ convergence: int 0
    #>   .. ..$ counts     : Named num [1:2] 509 1
    #>   .. .. ..- attr(*, "names")= chr [1:2] "function" "gradient"
    #>   ..$ loglik      : num -4563
    #>   ..$ runtime     : chr "0.1 seconds"
    #>   ..$ ERC         : logi FALSE
    #>  $ ERC               :List of 7
    #>   ..$ coefficients: Named num [1:5] -1.361 0.48 -0.519 1.165 1.106
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ sd          : Named num [1:5] 0.0366 0.0404 0.0497 0.0204 0.0142
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ vcov        : num [1:5, 1:5] 1.34e-03 -8.34e-04 -4.85e-04 -4.14e-04 1.53e-06 ...
    #>   .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. ..$ : chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   .. .. ..$ : chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ optim       :List of 4
    #>   .. ..$ par        : num [1:5] -1.361 0.48 -0.519 1.165 0.101
    #>   .. ..$ hessian    : num [1:5, 1:5] 2448.9 1232.8 513.4 2428.2 7.1 ...
    #>   .. ..$ convergence: int 0
    #>   .. ..$ counts     : Named num [1:2] 546 9
    #>   .. .. ..- attr(*, "names")= chr [1:2] "function" "gradient"
    #>   ..$ loglik      : num -4559
    #>   ..$ runtime     : chr "41.7 seconds"
    #>   ..$ ERC         : logi TRUE
    #>  $ MCML              :List of 6
    #>   ..$ coefficients: Named num [1:5] -1.28 0.484 -0.517 1.079 1.138
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ sd          : Named num [1:5] 0.0371 0.0416 0.0511 0.0199 0.0147
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ vcov        : num [1:5, 1:5] 1.38e-03 -8.83e-04 -5.14e-04 -3.98e-04 -1.91e-08 ...
    #>   .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. ..$ : chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   .. .. ..$ : chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ optim       :List of 4
    #>   .. ..$ par        : num [1:5] -1.28 0.484 -0.517 1.079 0.129
    #>   .. ..$ hessian    : num [1:5, 1:5] 2316.337 1165.788 484.911 2300.887 -0.322 ...
    #>   .. ..$ convergence: int 0
    #>   .. ..$ counts     : Named num [1:2] 555 7
    #>   .. .. ..- attr(*, "names")= chr [1:2] "function" "gradient"
    #>   ..$ loglik      : num -4646
    #>   ..$ runtime     : chr "0.3 seconds"
    #>  $ FMA               :List of 6
    #>   ..$ coefficients       : Named num [1:5] -1.28 0.484 -0.517 1.079 1.138
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ sd                 : Named num [1:5] 0.0373 0.0418 0.0512 0.0202 0.0147
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ included.replicates: int [1:10] 1 2 3 4 5 6 7 8 9 10
    #>   ..$ included.samples   : int 100006
    #>   ..$ samples            :'data.frame':  100006 obs. of  5 variables:
    #>   .. ..$ (Intercept): num [1:100006] -1.27 -1.32 -1.32 -1.26 -1.24 ...
    #>   .. ..$ X1         : num [1:100006] 0.516 0.511 0.534 0.415 0.509 ...
    #>   .. ..$ X2         : num [1:100006] -0.525 -0.535 -0.49 -0.529 -0.538 ...
    #>   .. ..$ dose       : num [1:100006] 1.08 1.09 1.1 1.11 1.03 ...
    #>   .. ..$ sigma      : num [1:100006] 1.17 1.14 1.13 1.15 1.12 ...
    #>   ..$ runtime            : chr "0.8 seconds"
    #>  $ BMA               :List of 6
    #>   ..$ coefficients       : Named num [1:5] -1.28 0.483 -0.517 1.079 1.139
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ sd                 : Named num [1:5] 0.038 0.0424 0.0525 0.0202 0.015
    #>   .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ Rhat               :'data.frame':  5 obs. of  2 variables:
    #>   .. ..$ Rhat : num [1:5] 1 1.01 1 1 1.02
    #>   .. ..$ n.eff: num [1:5] 871 822 800 800 744
    #>   ..$ samples            :List of 2
    #>   .. ..$ chain1: num [1:400, 1:6] -1.32 -1.27 -1.33 -1.29 -1.33 ...
    #>   .. .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. .. ..$ : NULL
    #>   .. .. .. ..$ : chr [1:6] "(Intercept)" "X1" "X2" "dose" ...
    #>   .. ..$ chain2: num [1:400, 1:6] -1.3 -1.29 -1.23 -1.3 -1.26 ...
    #>   .. .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. .. ..$ : NULL
    #>   .. .. .. ..$ : chr [1:6] "(Intercept)" "X1" "X2" "dose" ...
    #>   ..$ included.replicates: int [1:10] 1 2 3 4 5 6 7 8 9 10
    #>   ..$ runtime            : chr "33.9 seconds"
    #>  - attr(*, "class")= chr "amerasfit"

Access the results for e.g. regression calibration as follows:

    fit.ameras.linreg$RC
    #> $coefficients
    #> (Intercept)          X1          X2        dose       sigma
    #>  -1.3622239   0.4807463  -0.5187877   1.1660368   1.1075696
    #>
    #> $sd
    #> (Intercept)          X1          X2        dose       sigma
    #>  0.03665711  0.04045137  0.04971981  0.02039612  0.01429864
    #>
    #> $vcov
    #>               (Intercept)            X1            X2          dose
    #> (Intercept)  1.343744e-03 -8.358763e-04 -4.863064e-04 -4.154402e-04
    #> X1          -8.358763e-04  1.636314e-03 -1.376614e-05  1.526165e-05
    #> X2          -4.863064e-04 -1.376614e-05  2.472060e-03 -2.443289e-05
    #> dose        -4.154402e-04  1.526165e-05 -2.443289e-05  4.160019e-04
    #> sigma        1.547492e-09 -1.113331e-08  8.801420e-09  2.066813e-09
    #>                     sigma
    #> (Intercept)  1.547492e-09
    #> X1          -1.113331e-08
    #> X2           8.801420e-09
    #> dose         2.066813e-09
    #> sigma        2.044510e-04
    #>
    #> $optim
    #> $optim$par
    #> [1] -1.3622239  0.4807463 -0.5187877  1.1660368  0.1021681
    #>
    #> $optim$hessian
    #>              [,1]         [,2]        [,3]          [,4]          [,5]
    #> [1,] 2.445565e+03 1.230934e+03 511.9382502 2427.17149141  2.153847e-03
    #> [2,] 1.230934e+03 1.230934e+03 260.8602549 1199.43455144  3.805407e-02
    #> [3,] 5.119383e+02 2.608603e+02 511.9382504  531.74442352 -1.892140e-02
    #> [4,] 2.427171e+03 1.199435e+03 531.7444235 4814.95714497 -2.727101e-02
    #> [5,] 2.153847e-03 3.805407e-02  -0.0189214   -0.02727101  6.000020e+03
    #>
    #> $optim$convergence
    #> [1] 0
    #>
    #> $optim$counts
    #> function gradient
    #>      509        1
    #>
    #>
    #> $loglik
    #> [1] -4563.325
    #>
    #> $runtime
    #> [1] "0.1 seconds"
    #>
    #> $ERC
    #> [1] FALSE

For a summary of results, use `summary` (note that `Rhat` and `n.eff` only apply to BMA results):

    summary(fit.ameras.linreg)
    #> Call:
    #> ameras(formula = Y.gaussian ~ dose(V1:V10) + X1 + X2, data = data,
    #>     family = "gaussian", methods = c("RC", "ERC", "MCML", "FMA",
    #>         "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 76.8 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.1
    #>     ERC    41.7
    #>    MCML     0.3
    #>     FMA     0.8
    #>     BMA    33.9
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method        Term Estimate      SE Rhat  n.eff
    #>      RC (Intercept)  -1.3622 0.03666   NA     NA
    #>      RC          X1   0.4807 0.04045   NA     NA
    #>      RC          X2  -0.5188 0.04972   NA     NA
    #>      RC        dose   1.1660 0.02040   NA     NA
    #>      RC       sigma   1.1076 0.01430   NA     NA
    #>     ERC (Intercept)  -1.3611 0.03660   NA     NA
    #>     ERC          X1   0.4795 0.04041   NA     NA
    #>     ERC          X2  -0.5188 0.04969   NA     NA
    #>     ERC        dose   1.1649 0.02038   NA     NA
    #>     ERC       sigma   1.1062 0.01425   NA     NA
    #>    MCML (Intercept)  -1.2803 0.03714   NA     NA
    #>    MCML          X1   0.4837 0.04156   NA     NA
    #>    MCML          X2  -0.5171 0.05108   NA     NA
    #>    MCML        dose   1.0790 0.01993   NA     NA
    #>    MCML       sigma   1.1378 0.01469   NA     NA
    #>     FMA (Intercept)  -1.2803 0.03732   NA     NA
    #>     FMA          X1   0.4837 0.04181   NA     NA
    #>     FMA          X2  -0.5173 0.05124   NA     NA
    #>     FMA        dose   1.0792 0.02015   NA     NA
    #>     FMA       sigma   1.1377 0.01467   NA     NA
    #>     BMA (Intercept)  -1.2796 0.03797 1.00 871.00
    #>     BMA          X1   0.4831 0.04240 1.01 822.00
    #>     BMA          X2  -0.5169 0.05254 1.00 800.00
    #>     BMA        dose   1.0788 0.02024 1.00 800.00
    #>     BMA       sigma   1.1391 0.01503 1.02 744.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

To extract model coefficients and compare between methods, use `coef`:

    coef(fit.ameras.linreg)
    #>                     RC        ERC       MCML        FMA        BMA
    #> (Intercept) -1.3622239 -1.3610557 -1.2802532 -1.2803273 -1.2796163
    #> X1           0.4807463  0.4795308  0.4836506  0.4836869  0.4830670
    #> X2          -0.5187877 -0.5188299 -0.5171452 -0.5173188 -0.5169488
    #> dose         1.1660368  1.1649227  1.0790214  1.0791951  1.0787855
    #> sigma        1.1075696  1.1062486  1.1377732  1.1377321  1.1391166

To produce traceplots and density plots for BMA results, use `traceplot`:

    traceplot(fit.ameras.linreg)

# Logistic regression

To fit a logistic regression model, the syntax is very similar. However, `ameras` offers three functional forms for modeling the exposure-outcome relationship. Here, we will illustrate the standard exponential relationship `model="EXP"`. For more information, see the vignette Relative risk models.

First, we fit models including a quadratic exposure term by setting `deg=2`.

    set.seed(33521)
    fit.ameras.logreg <- ameras(Y.binomial~dose(V1:V10, deg=2, model="EXP")+X1+X2,
                                data=data, family="binomial",
                                methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> Warning in ameras.bma(family = family, dosevars = dosevars, data = data, :
    #> WARNING: Potential problems with MCMC convergence, consider using longer chains

    summary(fit.ameras.logreg)
    #> Call:
    #> ameras(formula = Y.binomial ~ dose(V1:V10, deg = 2, model = "EXP") +
    #>     X1 + X2, data = data, family = "binomial", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 63.5 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.1
    #>     ERC    31.5
    #>    MCML     0.5
    #>     FMA     1.5
    #>     BMA    29.9
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method         Term Estimate      SE Rhat   n.eff
    #>      RC  (Intercept) -0.94461 0.08409   NA      NA
    #>      RC           X1  0.44552 0.07667   NA      NA
    #>      RC           X2 -0.33376 0.09601   NA      NA
    #>      RC         dose  0.37904 0.10388   NA      NA
    #>      RC dose_squared  0.01943 0.02750   NA      NA
    #>     ERC  (Intercept) -0.93189 0.08533   NA      NA
    #>     ERC           X1  0.44998 0.07678   NA      NA
    #>     ERC           X2 -0.33924 0.09614   NA      NA
    #>     ERC         dose  0.33858 0.10745   NA      NA
    #>     ERC dose_squared  0.03528 0.02841   NA      NA
    #>    MCML  (Intercept) -0.91147 0.08356   NA      NA
    #>    MCML           X1  0.44619 0.07674   NA      NA
    #>    MCML           X2 -0.34431 0.09625   NA      NA
    #>    MCML         dose  0.31654 0.10412   NA      NA
    #>    MCML dose_squared  0.03800 0.02774   NA      NA
    #>     FMA  (Intercept) -0.91244 0.08386   NA      NA
    #>     FMA           X1  0.44619 0.07686   NA      NA
    #>     FMA           X2 -0.34400 0.09627   NA      NA
    #>     FMA         dose  0.31871 0.10523   NA      NA
    #>     FMA dose_squared  0.03735 0.02816   NA      NA
    #>     BMA  (Intercept) -0.90779 0.08101 1.03  214.00
    #>     BMA           X1  0.44552 0.07775 1.01  370.00
    #>     BMA           X2 -0.34361 0.09652 1.00 1242.00
    #>     BMA         dose  0.30658 0.09894 1.08   92.00
    #>     BMA dose_squared  0.04134 0.02564 1.11  105.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.logreg)
    #>                       RC         ERC        MCML         FMA         BMA
    #> (Intercept)  -0.94461327 -0.93188863 -0.91146831 -0.91243544 -0.90778810
    #> X1            0.44552273  0.44997904  0.44619209  0.44619391  0.44552058
    #> X2           -0.33375991 -0.33923955 -0.34430935 -0.34400234 -0.34360534
    #> dose          0.37904346  0.33857980  0.31654277  0.31870705  0.30657532
    #> dose_squared  0.01943381  0.03528262  0.03799845  0.03735107  0.04134337

Without the quadratic term (`deg=1`):

    set.seed(3521216)
    fit.ameras.logreg.lin <- ameras(Y.binomial~dose(V1:V10, deg=1, model="EXP")+X1+X2,
                                    data=data, family="binomial",
                                    methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                    niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.logreg.lin)
    #> Call:
    #> ameras(formula = Y.binomial ~ dose(V1:V10, deg = 1, model = "EXP") +
    #>     X1 + X2, data = data, family = "binomial", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 46.2 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.1
    #>     ERC    18.3
    #>    MCML     0.3
    #>     FMA     0.8
    #>     BMA    26.7
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method        Term Estimate      SE Rhat  n.eff
    #>      RC (Intercept)  -0.9760 0.07190   NA     NA
    #>      RC          X1   0.4460 0.07667   NA     NA
    #>      RC          X2  -0.3359 0.09596   NA     NA
    #>      RC        dose   0.4471 0.04101   NA     NA
    #>     ERC (Intercept)  -0.9898 0.07216   NA     NA
    #>     ERC          X1   0.4533 0.07667   NA     NA
    #>     ERC          X2  -0.3437 0.09609   NA     NA
    #>     ERC        dose   0.4632 0.04086   NA     NA
    #>    MCML (Intercept)  -0.9725 0.07154   NA     NA
    #>    MCML          X1   0.4469 0.07674   NA     NA
    #>    MCML          X2  -0.3467 0.09618   NA     NA
    #>    MCML        dose   0.4498 0.04105   NA     NA
    #>     FMA (Intercept)  -0.9725 0.07138   NA     NA
    #>     FMA          X1   0.4472 0.07664   NA     NA
    #>     FMA          X2  -0.3465 0.09602   NA     NA
    #>     FMA        dose   0.4497 0.04101   NA     NA
    #>     BMA (Intercept)  -0.9791 0.06869 1.03 386.00
    #>     BMA          X1   0.4555 0.07669 1.00 482.00
    #>     BMA          X2  -0.3486 0.09589 1.02 882.00
    #>     BMA        dose   0.4523 0.04042 1.03 518.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.logreg.lin)
    #>                     RC        ERC       MCML        FMA        BMA
    #> (Intercept) -0.9759817 -0.9897555 -0.9724854 -0.9725462 -0.9791394
    #> X1           0.4459778  0.4533083  0.4468997  0.4471557  0.4555060
    #> X2          -0.3358992 -0.3436867 -0.3467298 -0.3465319 -0.3485601
    #> dose         0.4471346  0.4632116  0.4497526  0.4497072  0.4522620

# Poisson regression

For Poisson regression, an offset can be optionally used by including an `offset` term in the formula, e.g., `Y~dose(V1:V10)~X1+offset(PYR)`. Here, we show models without using an offset, first including a quadratic exposure term:

    set.seed(332101)
    fit.ameras.poisson <- ameras(Y.poisson~dose(V1:V10, deg=2, model="EXP")+X1+X2,
                                 data=data, family="poisson",
                                 methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                 niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> warning: logProb of data node Y[247]: logProb less than -1e12.
    #> warning: logProb of data node Y[676]: logProb less than -1e12.
    #> warning: logProb of data node Y[833]: logProb less than -1e12.
    #> warning: logProb of data node Y[1635]: logProb less than -1e12.
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.poisson)
    #> Call:
    #> ameras(formula = Y.poisson ~ dose(V1:V10, deg = 2, model = "EXP") +
    #>     X1 + X2, data = data, family = "poisson", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 32 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.2
    #>     ERC     0.6
    #>    MCML     0.7
    #>     FMA     1.7
    #>     BMA    28.8
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method         Term Estimate       SE Rhat  n.eff
    #>      RC  (Intercept) -1.09456 0.048655   NA     NA
    #>      RC           X1  0.49070 0.041922   NA     NA
    #>      RC           X2 -0.37625 0.055638   NA     NA
    #>      RC         dose  0.61976 0.040375   NA     NA
    #>      RC dose_squared -0.03849 0.007566   NA     NA
    #>     ERC  (Intercept) -1.09068 0.048474   NA     NA
    #>     ERC           X1  0.49180 0.042008   NA     NA
    #>     ERC           X2 -0.37855 0.055639   NA     NA
    #>     ERC         dose  0.61138 0.039328   NA     NA
    #>     ERC dose_squared -0.03626 0.007177   NA     NA
    #>    MCML  (Intercept) -1.07519 0.046954   NA     NA
    #>    MCML           X1  0.49897 0.041918   NA     NA
    #>    MCML           X2 -0.37711 0.055643   NA     NA
    #>    MCML         dose  0.60089 0.034218   NA     NA
    #>    MCML dose_squared -0.03644 0.005719   NA     NA
    #>     FMA  (Intercept) -1.07542 0.047056   NA     NA
    #>     FMA           X1  0.49896 0.041800   NA     NA
    #>     FMA           X2 -0.37716 0.055479   NA     NA
    #>     FMA         dose  0.60138 0.035163   NA     NA
    #>     FMA dose_squared -0.03656 0.005998   NA     NA
    #>     BMA  (Intercept) -1.07944 0.044741 1.02 215.00
    #>     BMA           X1  0.49801 0.039956 1.03 435.00
    #>     BMA           X2 -0.38167 0.055527 1.00 800.00
    #>     BMA         dose  0.60805 0.035663 1.03  87.00
    #>     BMA dose_squared -0.03798 0.006215 1.03  79.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.poisson)
    #>                       RC         ERC        MCML         FMA        BMA
    #> (Intercept)  -1.09455980 -1.09068092 -1.07519346 -1.07541780 -1.0794372
    #> X1            0.49070108  0.49179875  0.49897425  0.49895993  0.4980120
    #> X2           -0.37624508 -0.37854805 -0.37711401 -0.37715581 -0.3816682
    #> dose          0.61975742  0.61138406  0.60089474  0.60137955  0.6080481
    #> dose_squared -0.03849039 -0.03626348 -0.03643505 -0.03656317 -0.0379750

Without the quadratic term (`deg=1`):

    set.seed(24252)
    fit.ameras.poisson.lin <- ameras(Y.poisson~dose(V1:V10, deg=1, model="EXP")+X1+X2,
                                     data=data, family="poisson",
                                     methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                     niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.poisson.lin)
    #> Call:
    #> ameras(formula = Y.poisson ~ dose(V1:V10, deg = 1, model = "EXP") +
    #>     X1 + X2, data = data, family = "poisson", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 28 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.1
    #>     ERC     0.4
    #>    MCML     0.6
    #>     FMA     0.8
    #>     BMA    26.1
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method        Term Estimate      SE Rhat  n.eff
    #>      RC (Intercept)  -0.9650 0.04127   NA     NA
    #>      RC          X1   0.5054 0.04195   NA     NA
    #>      RC          X2  -0.3640 0.05560   NA     NA
    #>      RC        dose   0.4204 0.01346   NA     NA
    #>     ERC (Intercept)  -0.9667 0.04134   NA     NA
    #>     ERC          X1   0.5049 0.04202   NA     NA
    #>     ERC          X2  -0.3667 0.05559   NA     NA
    #>     ERC        dose   0.4222 0.01361   NA     NA
    #>    MCML (Intercept)  -0.9173 0.04048   NA     NA
    #>    MCML          X1   0.5129 0.04231   NA     NA
    #>    MCML          X2  -0.3579 0.05582   NA     NA
    #>    MCML        dose   0.3823 0.01231   NA     NA
    #>     FMA (Intercept)  -0.9174 0.04044   NA     NA
    #>     FMA          X1   0.5121 0.04257   NA     NA
    #>     FMA          X2  -0.3584 0.05587   NA     NA
    #>     FMA        dose   0.3826 0.01255   NA     NA
    #>     BMA (Intercept)  -0.9180 0.04041 1.00 301.00
    #>     BMA          X1   0.5100 0.04121 1.01 364.00
    #>     BMA          X2  -0.3581 0.05584 1.00 800.00
    #>     BMA        dose   0.3833 0.01343 1.01 371.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.poisson.lin)
    #>                     RC        ERC       MCML        FMA        BMA
    #> (Intercept) -0.9649529 -0.9666735 -0.9173445 -0.9173785 -0.9179930
    #> X1           0.5054270  0.5049256  0.5129102  0.5120519  0.5099994
    #> X2          -0.3639707 -0.3666592 -0.3579466 -0.3583942 -0.3581445
    #> dose         0.4204226  0.4222084  0.3822723  0.3826304  0.3833150

# Proportional hazards regression

Proportional hazards regression uses syntax similar to the `survival` package, with models specified using formulas with `Surv(exit, status)` or `Surv(entry, exit, status)` on the left hand side. Note that BMA fits a piecewise constant baseline hazard `h0` as the proportional hazards model is not directly supported. By default, the observed time interval is divided into 10 intervals using quantiles of the observed event times among cases. This number of such intervals can be specified through the `prophaz.numints.BMA` argument. The BMA output contains the `prophaz.numints.BMA+1` cutpoints defining the intervals in addition to `h0`.

Again, we first fit models including a quadratic exposure term by setting `deg=2`.

    set.seed(332120000)
    fit.ameras.prophaz <- ameras(Surv(time, status)~
                                   dose(V1:V10, deg=2, model="EXP")+X1+X2,
                                 data=data, family="prophaz",
                                 methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                 niter.BMA = 5000, nburnin.BMA = 1000)
    #> Warning in ameras.rc(family = family, dosevars = dosevars, data = data, :
    #> WARNING: Hessian was not invertible or inverse was not positive definite,
    #> variance matrix could not be obtained
    #> warning: logProb of data node zeros[7]: logProb less than -1e12.
    #> warning: logProb of data node zeros[17]: logProb less than -1e12.
    #> warning: logProb of data node zeros[41]: logProb less than -1e12.
    #> warning: logProb of data node zeros[46]: logProb less than -1e12.
    #> warning: logProb of data node zeros[52]: logProb less than -1e12.
    #> warning: logProb of data node zeros[58]: logProb less than -1e12.
    #> warning: logProb of data node zeros[71]: logProb less than -1e12.
    #> warning: logProb of data node zeros[157]: logProb less than -1e12.
    #> warning: logProb of data node zeros[166]: logProb less than -1e12.
    #> warning: logProb of data node zeros[175]: logProb less than -1e12.
    #> warning: logProb of data node zeros[178]: logProb less than -1e12.
    #> warning: logProb of data node zeros[212]: logProb less than -1e12.
    #> warning: logProb of data node zeros[238]: logProb less than -1e12.
    #> warning: logProb of data node zeros[243]: logProb less than -1e12.
    #> warning: logProb of data node zeros[246]: logProb less than -1e12.
    #> warning: logProb of data node zeros[247]: logProb less than -1e12.
    #> warning: logProb of data node zeros[260]: logProb less than -1e12.
    #> warning: logProb of data node zeros[261]: logProb less than -1e12.
    #> warning: logProb of data node zeros[263]: logProb less than -1e12.
    #> warning: logProb of data node zeros[267]: logProb less than -1e12.
    #> warning: logProb of data node zeros[270]: logProb less than -1e12.
    #> warning: logProb of data node zeros[278]: logProb less than -1e12.
    #> warning: logProb of data node zeros[311]: logProb less than -1e12.
    #> warning: logProb of data node zeros[335]: logProb less than -1e12.
    #> warning: logProb of data node zeros[341]: logProb less than -1e12.
    #> warning: logProb of data node zeros[348]: logProb less than -1e12.
    #> warning: logProb of data node zeros[352]: logProb less than -1e12.
    #> warning: logProb of data node zeros[370]: logProb less than -1e12.
    #> warning: logProb of data node zeros[372]: logProb less than -1e12.
    #> warning: logProb of data node zeros[378]: logProb less than -1e12.
    #> warning: logProb of data node zeros[386]: logProb less than -1e12.
    #> warning: logProb of data node zeros[420]: logProb less than -1e12.
    #> warning: logProb of data node zeros[432]: logProb less than -1e12.
    #> warning: logProb of data node zeros[433]: logProb less than -1e12.
    #> warning: logProb of data node zeros[438]: logProb less than -1e12.
    #> warning: logProb of data node zeros[440]: logProb less than -1e12.
    #> warning: logProb of data node zeros[449]: logProb less than -1e12.
    #> warning: logProb of data node zeros[450]: logProb less than -1e12.
    #> warning: logProb of data node zeros[458]: logProb less than -1e12.
    #> warning: logProb of data node zeros[508]: logProb less than -1e12.
    #> warning: logProb of data node zeros[509]: logProb less than -1e12.
    #> warning: logProb of data node zeros[528]: logProb less than -1e12.
    #> warning: logProb of data node zeros[535]: logProb less than -1e12.
    #> warning: logProb of data node zeros[546]: logProb less than -1e12.
    #> warning: logProb of data node zeros[547]: logProb less than -1e12.
    #> warning: logProb of data node zeros[552]: logProb less than -1e12.
    #> warning: logProb of data node zeros[554]: logProb less than -1e12.
    #> warning: logProb of data node zeros[577]: logProb less than -1e12.
    #> warning: logProb of data node zeros[608]: logProb less than -1e12.
    #> warning: logProb of data node zeros[611]: logProb less than -1e12.
    #> warning: logProb of data node zeros[620]: logProb less than -1e12.
    #> warning: logProb of data node zeros[627]: logProb less than -1e12.
    #> warning: logProb of data node zeros[631]: logProb less than -1e12.
    #> warning: logProb of data node zeros[656]: logProb less than -1e12.
    #> warning: logProb of data node zeros[661]: logProb less than -1e12.
    #> warning: logProb of data node zeros[676]: logProb less than -1e12.
    #> warning: logProb of data node zeros[684]: logProb less than -1e12.
    #> warning: logProb of data node zeros[698]: logProb less than -1e12.
    #> warning: logProb of data node zeros[714]: logProb less than -1e12.
    #> warning: logProb of data node zeros[716]: logProb less than -1e12.
    #> warning: logProb of data node zeros[727]: logProb less than -1e12.
    #> warning: logProb of data node zeros[749]: logProb less than -1e12.
    #> warning: logProb of data node zeros[806]: logProb less than -1e12.
    #> warning: logProb of data node zeros[809]: logProb less than -1e12.
    #> warning: logProb of data node zeros[821]: logProb less than -1e12.
    #> warning: logProb of data node zeros[833]: logProb less than -1e12.
    #> warning: logProb of data node zeros[839]: logProb less than -1e12.
    #> warning: logProb of data node zeros[864]: logProb less than -1e12.
    #> warning: logProb of data node zeros[873]: logProb less than -1e12.
    #> warning: logProb of data node zeros[910]: logProb less than -1e12.
    #> warning: logProb of data node zeros[962]: logProb less than -1e12.
    #> warning: logProb of data node zeros[971]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1002]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1004]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1009]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1014]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1036]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1040]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1052]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1070]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1074]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1076]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1081]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1116]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1122]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1137]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1151]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1179]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1187]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1208]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1214]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1245]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1247]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1282]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1296]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1300]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1319]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1322]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1327]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1340]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1354]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1357]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1368]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1373]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1375]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1406]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1425]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1449]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1464]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1470]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1475]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1480]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1492]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1496]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1517]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1524]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1527]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1541]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1553]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1566]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1569]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1572]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1588]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1603]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1617]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1635]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1661]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1663]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1664]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1693]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1716]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1755]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1768]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1783]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1796]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1807]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1818]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1836]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1838]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1851]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1888]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1900]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1921]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1930]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1948]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1968]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1971]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1972]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1979]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1983]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1991]: logProb less than -1e12.
    #> warning: logProb of data node zeros[1997]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2021]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2035]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2054]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2087]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2089]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2101]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2112]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2129]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2131]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2134]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2156]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2198]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2208]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2216]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2227]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2237]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2239]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2255]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2294]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2312]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2338]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2339]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2360]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2368]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2373]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2395]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2398]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2407]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2409]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2413]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2422]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2444]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2452]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2463]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2475]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2478]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2490]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2495]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2524]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2530]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2559]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2562]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2573]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2605]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2617]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2655]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2665]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2671]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2684]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2686]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2688]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2694]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2715]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2723]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2728]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2733]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2743]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2747]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2757]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2771]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2774]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2787]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2807]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2813]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2824]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2838]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2839]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2907]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2909]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2915]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2937]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2945]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2968]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2971]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2993]: logProb less than -1e12.
    #> warning: logProb of data node zeros[2998]: logProb less than -1e12.
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.prophaz)
    #> Call:
    #> ameras(formula = Surv(time, status) ~ dose(V1:V10, deg = 2, model = "EXP") +
    #>     X1 + X2, data = data, family = "prophaz", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 339.6 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.2
    #>     ERC   283.9
    #>    MCML     0.8
    #>     FMA     1.3
    #>     BMA    53.4
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method         Term  Estimate      SE Rhat  n.eff
    #>      RC           X1  0.629674 0.08485   NA     NA
    #>      RC           X2 -0.423116 0.11150   NA     NA
    #>      RC         dose  0.587614 0.08915   NA     NA
    #>      RC dose_squared -0.033682 0.01807   NA     NA
    #>     ERC           X1  0.636755      NA   NA     NA
    #>     ERC           X2 -0.422042      NA   NA     NA
    #>     ERC         dose  0.295488      NA   NA     NA
    #>     ERC dose_squared -0.003357      NA   NA     NA
    #>    MCML           X1  0.629162 0.08518   NA     NA
    #>    MCML           X2 -0.425156 0.11196   NA     NA
    #>    MCML         dose  0.590217 0.08046   NA     NA
    #>    MCML dose_squared -0.038038 0.01523   NA     NA
    #>     FMA           X1  0.629250 0.08557   NA     NA
    #>     FMA           X2 -0.425507 0.11190   NA     NA
    #>     FMA         dose  0.589537 0.08127   NA     NA
    #>     FMA dose_squared -0.037872 0.01546   NA     NA
    #>     BMA           X1  0.635157 0.08523 1.00 397.00
    #>     BMA           X2 -0.423897 0.11063 1.01 747.00
    #>     BMA         dose  0.617073 0.08181 1.01 101.00
    #>     BMA dose_squared -0.042851 0.01554 1.01 116.00
    #>     BMA        h0[1]  0.316373 0.05147 1.00 384.00
    #>     BMA        h0[2]  0.343831 0.05275 1.00 394.00
    #>     BMA        h0[3]  0.262707 0.04153 1.00 281.00
    #>     BMA        h0[4]  0.294011 0.04621 1.00 410.00
    #>     BMA        h0[5]  0.314660 0.04963 1.01 362.00
    #>     BMA        h0[6]  0.396503 0.06335 1.00 411.00
    #>     BMA        h0[7]  0.269226 0.03952 1.00 373.00
    #>     BMA        h0[8]  0.309864 0.04606 1.00 326.00
    #>     BMA        h0[9]  0.268182 0.04019 1.00 398.00
    #>     BMA       h0[10]  0.304145 0.04624 1.01 367.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.prophaz)
    #>                       RC          ERC        MCML         FMA         BMA
    #> X1            0.62967434  0.636754979  0.62916199  0.62925032  0.63515662
    #> X2           -0.42311583 -0.422041734 -0.42515576 -0.42550683 -0.42389658
    #> dose          0.58761408  0.295487972  0.59021691  0.58953694  0.61707317
    #> dose_squared -0.03368205 -0.003356734 -0.03803829 -0.03787174 -0.04285062
    #> h0[1]                 NA           NA          NA          NA  0.31637277
    #> h0[2]                 NA           NA          NA          NA  0.34383087
    #> h0[3]                 NA           NA          NA          NA  0.26270690
    #> h0[4]                 NA           NA          NA          NA  0.29401059
    #> h0[5]                 NA           NA          NA          NA  0.31466013
    #> h0[6]                 NA           NA          NA          NA  0.39650271
    #> h0[7]                 NA           NA          NA          NA  0.26922630
    #> h0[8]                 NA           NA          NA          NA  0.30986447
    #> h0[9]                 NA           NA          NA          NA  0.26818217
    #> h0[10]                NA           NA          NA          NA  0.30414532

The BMA output now contains the intervals with piecewise constant baseline hazards, corresponding to the estimates `h0`:

    fit.ameras.prophaz$BMA$prophaz.timepoints
    #> NULL

Without the quadratic term (`deg=1`):

    set.seed(24978252)
    fit.ameras.prophaz.lin <- ameras(Surv(time, status)~
                                       dose(V1:V10, deg=1, model="EXP")+X1+X2,
                                     data=data, family="prophaz",
                                     methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                     niter.BMA = 5000, nburnin.BMA = 1000)
    #> Warning in ameras.rc(family = family, dosevars = dosevars, data = data, :
    #> WARNING: Hessian was not invertible or inverse was not positive definite,
    #> variance matrix could not be obtained
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.prophaz.lin)
    #> Call:
    #> ameras(formula = Surv(time, status) ~ dose(V1:V10, deg = 1, model = "EXP") +
    #>     X1 + X2, data = data, family = "prophaz", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 183.2 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.1
    #>     ERC   129.9
    #>    MCML     0.3
    #>     FMA     0.7
    #>     BMA    52.2
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method   Term Estimate      SE Rhat  n.eff
    #>      RC     X1   0.6358 0.08488   NA     NA
    #>      RC     X2  -0.4161 0.11144   NA     NA
    #>      RC   dose   0.4284 0.03004   NA     NA
    #>     ERC     X1   0.6416      NA   NA     NA
    #>     ERC     X2  -0.4220      NA   NA     NA
    #>     ERC   dose   0.2812      NA   NA     NA
    #>    MCML     X1   0.6462 0.08565   NA     NA
    #>    MCML     X2  -0.4125 0.11253   NA     NA
    #>    MCML   dose   0.4002 0.02908   NA     NA
    #>     FMA     X1   0.6457 0.08567   NA     NA
    #>     FMA     X2  -0.4125 0.11263   NA     NA
    #>     FMA   dose   0.4001 0.02901   NA     NA
    #>     BMA     X1   0.6474 0.08153 1.00 388.00
    #>     BMA     X2  -0.4248 0.11473 1.00 800.00
    #>     BMA   dose   0.3987 0.02884 1.01 478.00
    #>     BMA  h0[1]   0.3646 0.05506 1.00 644.00
    #>     BMA  h0[2]   0.3926 0.05694 1.01 540.00
    #>     BMA  h0[3]   0.3036 0.04541 1.00 549.00
    #>     BMA  h0[4]   0.3378 0.04899 1.00 692.00
    #>     BMA  h0[5]   0.3615 0.05340 1.00 720.00
    #>     BMA  h0[6]   0.4547 0.06635 1.00 546.00
    #>     BMA  h0[7]   0.3080 0.04573 1.01 690.00
    #>     BMA  h0[8]   0.3551 0.05105 1.00 589.00
    #>     BMA  h0[9]   0.3091 0.04455 1.00 907.00
    #>     BMA h0[10]   0.3496 0.05211 1.01 542.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.prophaz.lin)
    #>                RC        ERC       MCML        FMA        BMA
    #> X1      0.6358422  0.6415799  0.6462073  0.6457466  0.6474031
    #> X2     -0.4160762 -0.4219654 -0.4124721 -0.4125357 -0.4247814
    #> dose    0.4283893  0.2811645  0.4001775  0.4000975  0.3987353
    #> h0[1]          NA         NA         NA         NA  0.3645755
    #> h0[2]          NA         NA         NA         NA  0.3925686
    #> h0[3]          NA         NA         NA         NA  0.3035865
    #> h0[4]          NA         NA         NA         NA  0.3378431
    #> h0[5]          NA         NA         NA         NA  0.3615119
    #> h0[6]          NA         NA         NA         NA  0.4547288
    #> h0[7]          NA         NA         NA         NA  0.3079510
    #> h0[8]          NA         NA         NA         NA  0.3550875
    #> h0[9]          NA         NA         NA         NA  0.3091293
    #> h0[10]         NA         NA         NA         NA  0.3495984

# Multinomial logistic regression

For multinomial logistic regression, the last category (in the case of the example data, `Y.multinomial='3'`) is used as the referent category.

Again, we first fit models including a quadratic exposure term by setting `deg=2`.

    set.seed(33)
    fit.ameras.multinomial <- ameras(Y.multinomial~
                                       dose(V1:V10, deg=2, model="EXP")+X1+X2,
                                     data=data, family="multinomial",
                                     methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                     niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.multinomial)
    #> Call:
    #> ameras(formula = Y.multinomial ~ dose(V1:V10, deg = 2, model = "EXP") +
    #>     X1 + X2, data = data, family = "multinomial", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 222.7 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.5
    #>     ERC    74.4
    #>    MCML     4.2
    #>     FMA     5.0
    #>     BMA   138.6
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method             Term  Estimate      SE Rhat  n.eff
    #>      RC  (1)_(Intercept) -1.134879 0.11395   NA     NA
    #>      RC           (1)_X1  0.521885 0.10631   NA     NA
    #>      RC           (1)_X2 -0.347821 0.14935   NA     NA
    #>      RC         (1)_dose  0.541114 0.13250   NA     NA
    #>      RC (1)_dose_squared -0.028978 0.03377   NA     NA
    #>      RC  (2)_(Intercept) -0.007547 0.08795   NA     NA
    #>      RC           (2)_X1 -0.513675 0.08620   NA     NA
    #>      RC           (2)_X2  0.705782 0.10738   NA     NA
    #>      RC         (2)_dose  0.555006 0.11504   NA     NA
    #>      RC (2)_dose_squared -0.037842 0.03053   NA     NA
    #>     ERC  (1)_(Intercept) -1.139984 0.11278   NA     NA
    #>     ERC           (1)_X1  0.524098 0.10651   NA     NA
    #>     ERC           (1)_X2 -0.351142 0.14952   NA     NA
    #>     ERC         (1)_dose  0.561788 0.12424   NA     NA
    #>     ERC (1)_dose_squared -0.031891 0.02990   NA     NA
    #>     ERC  (2)_(Intercept) -0.008660 0.08626   NA     NA
    #>     ERC           (2)_X1 -0.511536 0.08638   NA     NA
    #>     ERC           (2)_X2  0.703688 0.10757   NA     NA
    #>     ERC         (2)_dose  0.563142 0.10587   NA     NA
    #>     ERC (2)_dose_squared -0.036409 0.02606   NA     NA
    #>    MCML  (1)_(Intercept) -1.121541 0.11214   NA     NA
    #>    MCML           (1)_X1  0.522503 0.10638   NA     NA
    #>    MCML           (1)_X2 -0.349944 0.14943   NA     NA
    #>    MCML         (1)_dose  0.526846 0.12548   NA     NA
    #>    MCML (1)_dose_squared -0.027194 0.03020   NA     NA
    #>    MCML  (2)_(Intercept) -0.001584 0.08613   NA     NA
    #>    MCML           (2)_X1 -0.513787 0.08620   NA     NA
    #>    MCML           (2)_X2  0.703779 0.10746   NA     NA
    #>    MCML         (2)_dose  0.559807 0.10680   NA     NA
    #>    MCML (2)_dose_squared -0.041118 0.02628   NA     NA
    #>     FMA  (1)_(Intercept) -1.125692 0.11268   NA     NA
    #>     FMA           (1)_X1  0.522203 0.10653   NA     NA
    #>     FMA           (1)_X2 -0.349524 0.14951   NA     NA
    #>     FMA         (1)_dose  0.536663 0.12823   NA     NA
    #>     FMA (1)_dose_squared -0.030264 0.03136   NA     NA
    #>     FMA  (2)_(Intercept) -0.001039 0.08607   NA     NA
    #>     FMA           (2)_X1 -0.513380 0.08602   NA     NA
    #>     FMA           (2)_X2  0.704601 0.10763   NA     NA
    #>     FMA         (2)_dose  0.557269 0.10747   NA     NA
    #>     FMA (2)_dose_squared -0.040745 0.02652   NA     NA
    #>     BMA  (1)_(Intercept) -1.120533 0.11510 1.02 173.00
    #>     BMA           (1)_X1  0.518102 0.11473 1.00 330.00
    #>     BMA           (1)_X2 -0.342415 0.14116 1.01 663.00
    #>     BMA         (1)_dose  0.522811 0.12793 1.04  73.00
    #>     BMA (1)_dose_squared -0.024286 0.03270 1.01  73.00
    #>     BMA  (2)_(Intercept)  0.003372 0.09478 1.00 101.00
    #>     BMA           (2)_X1 -0.522597 0.08709 1.00 364.00
    #>     BMA           (2)_X2  0.708877 0.10811 1.00 462.00
    #>     BMA         (2)_dose  0.554817 0.12798 1.01  47.00
    #>     BMA (2)_dose_squared -0.037946 0.03326 1.01  45.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.multinomial)
    #>                            RC          ERC        MCML          FMA
    #> (1)_(Intercept)  -1.134879146 -1.139983536 -1.12154130 -1.125692409
    #> (1)_X1            0.521885281  0.524097727  0.52250322  0.522202729
    #> (1)_X2           -0.347820786 -0.351142281 -0.34994376 -0.349524398
    #> (1)_dose          0.541114276  0.561787528  0.52684585  0.536663279
    #> (1)_dose_squared -0.028978278 -0.031891085 -0.02719420 -0.030263819
    #> (2)_(Intercept)  -0.007546883 -0.008659617 -0.00158420 -0.001039459
    #> (2)_X1           -0.513675252 -0.511536376 -0.51378683 -0.513379778
    #> (2)_X2            0.705781705  0.703687593  0.70377938  0.704600521
    #> (2)_dose          0.555005580  0.563142229  0.55980729  0.557268589
    #> (2)_dose_squared -0.037841810 -0.036408828 -0.04111845 -0.040744704
    #>                           BMA
    #> (1)_(Intercept)  -1.120532838
    #> (1)_X1            0.518102027
    #> (1)_X2           -0.342415237
    #> (1)_dose          0.522811057
    #> (1)_dose_squared -0.024285677
    #> (2)_(Intercept)   0.003371856
    #> (2)_X1           -0.522596807
    #> (2)_X2            0.708876654
    #> (2)_dose          0.554817132
    #> (2)_dose_squared -0.037945537

Without the quadratic term (`deg=1`):

    set.seed(44)
    fit.ameras.multinomial.lin <- ameras(Y.multinomial~
                                           dose(V1:V10, deg=1, model="EXP")+X1+X2,
                                         data=data, family="multinomial",
                                         methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                         niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.multinomial.lin)
    #> Call:
    #> ameras(formula = Y.multinomial ~ dose(V1:V10, deg = 1, model = "EXP") +
    #>     X1 + X2, data = data, family = "multinomial", methods = c("RC",
    #>     "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000, niter.BMA = 5000)
    #>
    #> Total run time: 187.1 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.8
    #>     ERC    57.1
    #>    MCML     3.8
    #>     FMA     5.0
    #>     BMA   120.4
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method            Term Estimate      SE Rhat  n.eff
    #>      RC (1)_(Intercept) -1.10156 0.10093   NA     NA
    #>      RC          (1)_X1  0.52203 0.10628   NA     NA
    #>      RC          (1)_X2 -0.34710 0.14929   NA     NA
    #>      RC        (1)_dose  0.45264 0.05805   NA     NA
    #>      RC (2)_(Intercept)  0.04417 0.07667   NA     NA
    #>      RC          (2)_X1 -0.51312 0.08614   NA     NA
    #>      RC          (2)_X2  0.70793 0.10728   NA     NA
    #>      RC        (2)_dose  0.43049 0.05170   NA     NA
    #>     ERC (1)_(Intercept) -1.10285 0.10195   NA     NA
    #>     ERC          (1)_X1  0.52577 0.10650   NA     NA
    #>     ERC          (1)_X2 -0.35176 0.14953   NA     NA
    #>     ERC        (1)_dose  0.46441 0.06099   NA     NA
    #>     ERC (2)_(Intercept)  0.03947 0.07756   NA     NA
    #>     ERC          (2)_X1 -0.51003 0.08634   NA     NA
    #>     ERC          (2)_X2  0.70435 0.10749   NA     NA
    #>     ERC        (2)_dose  0.44543 0.05364   NA     NA
    #>    MCML (1)_(Intercept) -1.09147 0.10115   NA     NA
    #>    MCML          (1)_X1  0.52239 0.10633   NA     NA
    #>    MCML          (1)_X2 -0.34934 0.14941   NA     NA
    #>    MCML        (1)_dose  0.44410 0.05912   NA     NA
    #>    MCML (2)_(Intercept)  0.05917 0.07624   NA     NA
    #>    MCML          (2)_X1 -0.51290 0.08614   NA     NA
    #>    MCML          (2)_X2  0.70604 0.10735   NA     NA
    #>    MCML        (2)_dose  0.41753 0.05156   NA     NA
    #>     FMA (1)_(Intercept) -1.09114 0.10101   NA     NA
    #>     FMA          (1)_X1  0.52245 0.10612   NA     NA
    #>     FMA          (1)_X2 -0.34937 0.14960   NA     NA
    #>     FMA        (1)_dose  0.44381 0.05893   NA     NA
    #>     FMA (2)_(Intercept)  0.05869 0.07627   NA     NA
    #>     FMA          (2)_X1 -0.51272 0.08608   NA     NA
    #>     FMA          (2)_X2  0.70611 0.10708   NA     NA
    #>     FMA        (2)_dose  0.41769 0.05137   NA     NA
    #>     BMA (1)_(Intercept) -1.09319 0.09280 1.00 245.00
    #>     BMA          (1)_X1  0.52119 0.10307 1.02 390.00
    #>     BMA          (1)_X2 -0.34994 0.15277 1.01 447.00
    #>     BMA        (1)_dose  0.44801 0.05670 1.00 254.00
    #>     BMA (2)_(Intercept)  0.05267 0.07548 1.00 324.00
    #>     BMA          (2)_X1 -0.51037 0.08339 1.01 373.00
    #>     BMA          (2)_X2  0.70371 0.11185 1.01 650.00
    #>     BMA        (2)_dose  0.42386 0.05084 1.00 313.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.multinomial.lin)
    #>                          RC         ERC        MCML         FMA         BMA
    #> (1)_(Intercept) -1.10156112 -1.10285018 -1.09147154 -1.09114141 -1.09318983
    #> (1)_X1           0.52203186  0.52577389  0.52239330  0.52245186  0.52118788
    #> (1)_X2          -0.34709866 -0.35175606 -0.34933932 -0.34937267 -0.34993934
    #> (1)_dose         0.45263847  0.46441427  0.44410129  0.44380519  0.44801466
    #> (2)_(Intercept)  0.04416972  0.03946571  0.05916562  0.05869433  0.05266985
    #> (2)_X1          -0.51311892 -0.51003494 -0.51290413 -0.51271583 -0.51036888
    #> (2)_X2           0.70793136  0.70434790  0.70604308  0.70611143  0.70370686
    #> (2)_dose         0.43049029  0.44543353  0.41753398  0.41768779  0.42386430

# Conditional logistic regression

For conditional logistic regression, the set number variable is specified through a `strata` term in the formula. Again, we first fit models including a quadratic exposure term by setting `deg=2`.

    set.seed(3301)
    fit.ameras.clogit <- ameras(Y.clogit~dose(V1:V10, deg=2, model="EXP")+X1+X2+
                                  strata(setnr), data=data, family="clogit",
                                methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|

    summary(fit.ameras.clogit)
    #> Call:
    #> ameras(formula = Y.clogit ~ dose(V1:V10, deg = 2, model = "EXP") +
    #>     X1 + X2 + strata(setnr), data = data, family = "clogit",
    #>     methods = c("RC", "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000,
    #>     niter.BMA = 5000)
    #>
    #> Total run time: 313.8 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.4
    #>     ERC   268.8
    #>    MCML     3.6
    #>     FMA     5.2
    #>     BMA    35.8
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method         Term Estimate      SE Rhat  n.eff
    #>      RC           X1  0.54553 0.08896   NA     NA
    #>      RC           X2 -0.53392 0.11711   NA     NA
    #>      RC         dose  0.68029 0.10131   NA     NA
    #>      RC dose_squared -0.05146 0.02242   NA     NA
    #>     ERC           X1  0.61917 0.09205   NA     NA
    #>     ERC           X2 -0.51784 0.11993   NA     NA
    #>     ERC         dose  0.35155 0.08030   NA     NA
    #>     ERC dose_squared  0.03687 0.01013   NA     NA
    #>    MCML           X1  0.55083 0.08923   NA     NA
    #>    MCML           X2 -0.53547 0.11712   NA     NA
    #>    MCML         dose  0.69334 0.09315   NA     NA
    #>    MCML dose_squared -0.05581 0.01950   NA     NA
    #>     FMA           X1  0.55060 0.08916   NA     NA
    #>     FMA           X2 -0.53508 0.11710   NA     NA
    #>     FMA         dose  0.69731 0.09408   NA     NA
    #>     FMA dose_squared -0.05670 0.01995   NA     NA
    #>     BMA           X1  0.54979 0.08586 1.00 800.00
    #>     BMA           X2 -0.53448 0.11963 1.01 800.00
    #>     BMA         dose  0.68990 0.09957 1.00 183.00
    #>     BMA dose_squared -0.05469 0.02140 1.00 160.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.clogit)
    #>                       RC         ERC        MCML         FMA         BMA
    #> X1            0.54552627  0.61917374  0.55082666  0.55059936  0.54979498
    #> X2           -0.53391889 -0.51784345 -0.53546549 -0.53508245 -0.53448252
    #> dose          0.68029437  0.35155174  0.69333718  0.69730863  0.68989926
    #> dose_squared -0.05145934  0.03687427 -0.05581249 -0.05670202 -0.05468785

Without the quadratic term (`deg=1`):

    set.seed(4401)
    fit.ameras.clogit.lin <- ameras(Y.clogit~dose(V1:V10, deg=2, model="EXP")+X1+X2+
                                      strata(setnr), data=data, family="clogit",
                                    methods=c("RC", "ERC", "MCML", "FMA", "BMA"),
                                    niter.BMA = 5000, nburnin.BMA = 1000)
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> |-------------|-------------|-------------|-------------|
    #> |-------------------------------------------------------|
    #> Warning in ameras.bma(family = family, dosevars = dosevars, data = data, :
    #> WARNING: Potential problems with MCMC convergence, consider using longer chains

    summary(fit.ameras.clogit.lin)
    #> Call:
    #> ameras(formula = Y.clogit ~ dose(V1:V10, deg = 2, model = "EXP") +
    #>     X1 + X2 + strata(setnr), data = data, family = "clogit",
    #>     methods = c("RC", "ERC", "MCML", "FMA", "BMA"), nburnin.BMA = 1000,
    #>     niter.BMA = 5000)
    #>
    #> Total run time: 322.6 seconds
    #>
    #> Runtime in seconds by method:
    #>
    #>  Method Runtime
    #>      RC     0.4
    #>     ERC   277.5
    #>    MCML     3.5
    #>     FMA     5.2
    #>     BMA    36.0
    #>
    #> Summary of coefficients by method:
    #>
    #>  Method         Term Estimate      SE Rhat  n.eff
    #>      RC           X1  0.54553 0.08896   NA     NA
    #>      RC           X2 -0.53392 0.11711   NA     NA
    #>      RC         dose  0.68029 0.10131   NA     NA
    #>      RC dose_squared -0.05146 0.02242   NA     NA
    #>     ERC           X1  0.61917 0.09205   NA     NA
    #>     ERC           X2 -0.51784 0.11993   NA     NA
    #>     ERC         dose  0.35155 0.08030   NA     NA
    #>     ERC dose_squared  0.03687 0.01013   NA     NA
    #>    MCML           X1  0.55083 0.08923   NA     NA
    #>    MCML           X2 -0.53547 0.11712   NA     NA
    #>    MCML         dose  0.69334 0.09315   NA     NA
    #>    MCML dose_squared -0.05581 0.01950   NA     NA
    #>     FMA           X1  0.55020 0.08918   NA     NA
    #>     FMA           X2 -0.53494 0.11730   NA     NA
    #>     FMA         dose  0.69705 0.09400   NA     NA
    #>     FMA dose_squared -0.05667 0.01991   NA     NA
    #>     BMA           X1  0.55101 0.08956 1.00 800.00
    #>     BMA           X2 -0.53995 0.12115 1.03 800.00
    #>     BMA         dose  0.71397 0.09840 1.08 206.00
    #>     BMA dose_squared -0.06054 0.02124 1.05 157.00
    #>
    #> Note: confidence intervals not yet computed. Use confint() to add them.

    coef(fit.ameras.clogit.lin)
    #>                       RC         ERC        MCML         FMA         BMA
    #> X1            0.54552627  0.61917374  0.55082666  0.55019667  0.55100571
    #> X2           -0.53391889 -0.51784345 -0.53546549 -0.53494109 -0.53995242
    #> dose          0.68029437  0.35155174  0.69333718  0.69705313  0.71396567
    #> dose_squared -0.05145934  0.03687427 -0.05581249 -0.05667078 -0.06054304