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The goal of ameras is to provide a user-friendly interface to analyze association studies with multiple replicates of a noisy exposure using a variety of methods. ameras supports continuous, count, binary, multinomial, and right-censored time-to-event outcomes. For binary outcomes, the nested case-control design is also accommodated. Besides the common exponential relative risk model \(RR=\exp(\beta D)\) for the exposure-outcome association with noisy exposure \(D\), linear excess relative risk \(RR=1+\beta D\) and linear-exponential excess relative risk models \(RR=1+\beta_1 D \exp(\beta_2 D)\) can be used.
To install from CRAN:
# install.packages("ameras")This is a basic example which shows you how to fit a simple logistic regression model:
library(ameras)
#> Loading required package: nimble
#> nimble version 1.4.1 is loaded.
#> For more information on NIMBLE and a User Manual,
#> please visit https://R-nimble.org.
#>
#> Attaching package: 'nimble'
#> The following object is masked from 'package:stats':
#>
#> simulate
#> The following object is masked from 'package:base':
#>
#> declare
data(data, package="ameras")
dosevars <- paste0("V", 1:10)
fit <- ameras(data, family="binomial", Y="Y.binomial", methods=c("RC","ERC","MCML", "FMA", "BMA"),
dosevars=dosevars)
#> Fitting RC
#> Fitting ERC
#> Fitting MCML
#> Fitting FMA
#> Fitting BMA
#> Defining model
#> Building model
#> Setting data and initial values
#> Running calculate on model
#> [Note] Any error reports that follow may simply reflect missing values in model variables.
#> Checking model sizes and dimensions
#> [Note] This model is not fully initialized. This is not an error.
#> To see which variables are not initialized, use model$initializeInfo().
#> For more information on model initialization, see help(modelInitialization).
#> Compiling
#> [Note] This may take a minute.
#> [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> Compiling
#> [Note] This may take a minute.
#> [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> running chain 1...
#> |-------------|-------------|-------------|-------------|
#> |-------------------------------------------------------|
#> running chain 2...
#> |-------------|-------------|-------------|-------------|
#> |-------------------------------------------------------|
summary(fit)
#> Call:
#> ameras(data = data, family = "binomial", Y = "Y.binomial", dosevars = dosevars,
#> methods = c("RC", "ERC", "MCML", "FMA", "BMA"))
#>
#> Total run time: 31 seconds
#>
#> Runtime in seconds by method:
#>
#> Method Runtime
#> RC 0.0
#> ERC 8.5
#> MCML 0.1
#> FMA 0.2
#> BMA 22.2
#>
#> Summary of coefficients by method:
#>
#> Method Term Estimate SE CI.lowerbound CI.upperbound Rhat n.eff
#> RC (Intercept) -0.8847 0.07378 -1.0293 -0.7401 NA NA
#> RC dose 0.8020 0.13751 0.5324 1.0715 NA NA
#> ERC (Intercept) -0.8849 0.07477 -1.0315 -0.7384 NA NA
#> ERC dose 0.8214 0.14304 0.5411 1.1018 NA NA
#> MCML (Intercept) -0.8758 0.07323 -1.0193 -0.7323 NA NA
#> MCML dose 0.7910 0.13644 0.5236 1.0584 NA NA
#> FMA (Intercept) -0.8758 0.07321 -1.0197 -0.7329 NA NA
#> FMA dose 0.7913 0.13635 0.5245 1.0580 NA NA
#> BMA (Intercept) -0.8718 0.07342 -1.0201 -0.7342 1.00 291.00
#> BMA dose 0.7920 0.14133 0.5546 1.0999 1.00 281.00These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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