Package 'indirect' reference manual

Title:	Elicitation of Independent Conditional Means Priors for Generalised Linear Models
Description:	Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
Authors:	Geoffrey R. Hosack
Maintainer:	Geoff Hosack <[email protected]>
License:	GPL-3
Version:	0.2.1
Built:	2025-02-24 03:01:22 UTC
Source:	https://github.com/cran/indirect

Helper function that checks for sensible covariate matrix.

Description

Helper function that checks for sensible covariate matrix.

Usage

checkX(X)
checkX(X)

Arguments

`X`	numeric matrix of covariates, $n$ design points by $p$ covariates, for a given model and design points.

Value

throws an error if not full rank.

Function to check condition number diagnostic.

Description

This function calculates the condition number of the rescaled $n x p$ design matrix $X$ such that each column has unit length.

Usage

CNdiag(X)
CNdiag(X)

Arguments

`X`	Design matrix

Value

a scalar giving the condition number of the rescaled design matrix

Examples

X <- matrix(rnorm(16), nrow = 4)
CNdiag(X)
X <- matrix(rnorm(16), nrow = 4)
CNdiag(X)

Create list with information for the elicitation session

Description

This builds the structure that will store elicited data. The linear predictor has a normal prior $g(\theta) ~ N(m, V)$ , $\theta$ is the elicitation target. Link functions $g(.)$ : logit, log, cloglog, identity.

Usage

designLink(
  design,
  link = "identity",
  target = "Target",
  CI.prob = 1/2,
  expertID = "Expert",
  facilitator = "Facilitator",
  rapporteur = "none",
  intro.comments = "This is a record of the elicitation session.",
  fit.method = "KL"
)
designLink(
  design,
  link = "identity",
  target = "Target",
  CI.prob = 1/2,
  expertID = "Expert",
  facilitator = "Facilitator",
  rapporteur = "none",
  intro.comments = "This is a record of the elicitation session.",
  fit.method = "KL"
)

Arguments

`design`	a dataframe with covariate values that will be displayed to the expert(s) during the elicitation session.
`link`	character `logit`, `log`, `cloglog`, `identity`
`target`	character, name of target parameter of elicitation exercise
`CI.prob`	numeric, a fraction between 0 and 1 that defines probability attributed to central credible interval. For example, 1/2 for a central credible interval of probability 0.5, or 1/3 for a central credible interval of probablity 0.333... The default is probability 1/2.
`expertID`	character, identifier for expert or group of experts
`facilitator`	character, facilitator identifier
`rapporteur`	character, rapporteur identifier. Default "none".
`intro.comments`	character, text with any prefacing comments. This may include, for example, the definition of the target parameter for the elictation session. Beware of non-ASCII text and special characters, which may affect the ability to save the elicitation record with function `saveRecord` or create a summary report with function `makeSweave` if called by the function `makeSweave` may affect ability to render by means of `Sweave` or `knitr` etc.
`fit.method`	character, method used to fit conditional means prior: `KL` (default), `moment`, `SS` (see vignette and `mV` for more information on these options)

Details

Assumption: at least two fractiles selected from the median, upper and lower bounds of hte central credible interval of probability CI.prob will be elicited at each design point. The probabilities assigned to the central credible intervals can vary across design points. The argument CI.prob can later be adjusted by design point during the elicitation exercise, see function elicitPt. In the first instance, it is set to a global value specified by CI.prob in function designLink with default value $0.5$ .

Value

list of design with entries: theta, a $n x 4$ matrix with columns that give lower, median and upper quantiles followed by CI.prob and $n$ equal to the number of design points (scenarios); link, the link function used; target; expert facilitator; rapporteur; date; intro.comments; fit.method.

Examples

X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X, link = "logit", target = "target",
 CI.prob = 1/2, expertID = "Expert", facilitator = "facilitator")
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X, link = "logit", target = "target",
 CI.prob = 1/2, expertID = "Expert", facilitator = "facilitator")

density for Gompertz transformed univariate Gaussian

Description

density for Gompertz transformed univariate Gaussian

Usage

dGompertzNorm(x, mu, sigma)
dGompertzNorm(x, mu, sigma)

Arguments

`x`	numeric real
`mu`	numeric real
`sigma`	numeric real positive

Value

tranformed density on support (0, 1)

Examples

mu <- -1
sigma <- 1
z <- rnorm(10000, mu, sigma)
hist(1 - exp(-exp(z)), freq = FALSE)
curve(dGompertzNorm(x, mu = mu, sigma = sigma), col = 'red', add = TRUE, from = 0.01, to = 0.99)
integrate(function(x) dGompertzNorm(x, mu = mu, sigma = sigma), lower = 0, upper = 1) # equals 1
mu <- -1
sigma <- 1
z <- rnorm(10000, mu, sigma)
hist(1 - exp(-exp(z)), freq = FALSE)
curve(dGompertzNorm(x, mu = mu, sigma = sigma), col = 'red', add = TRUE, from = 0.01, to = 0.99)
integrate(function(x) dGompertzNorm(x, mu = mu, sigma = sigma), lower = 0, upper = 1) # equals 1

density for logit transformed univariate Gaussian

Description

density for logit transformed univariate Gaussian

Usage

dLogitNorm(x, mu, sigma)
dLogitNorm(x, mu, sigma)

Arguments

`x`	numeric real
`mu`	numeric real
`sigma`	numeric real positive

Value

tranformed density on support (0, 1)

Examples

mu <- -1
sigma <- 1
z <- rnorm(10000, mu, sigma)
hist(exp(z)/(1 + exp(z)), freq = FALSE)
curve(dLogitNorm(x, mu = mu, sigma = sigma), col = 'red', add = TRUE, from = 0.01, to = 0.99)
integrate(function(x) dLogitNorm(x, mu = mu, sigma = sigma), lower = 0, upper = 1) # equals 1
mu <- -1
sigma <- 1
z <- rnorm(10000, mu, sigma)
hist(exp(z)/(1 + exp(z)), freq = FALSE)
curve(dLogitNorm(x, mu = mu, sigma = sigma), col = 'red', add = TRUE, from = 0.01, to = 0.99)
integrate(function(x) dLogitNorm(x, mu = mu, sigma = sigma), lower = 0, upper = 1) # equals 1

Function to create or update elicitation at a given design point.

Description

Function to create or update elicitation at a given design point.

Usage

elicitPt(
  Z,
  design.pt = NULL,
  lower.CI.bound = NA,
  median = NA,
  upper.CI.bound = NA,
  CI.prob = NULL,
  comment = " "
)
elicitPt(
  Z,
  design.pt = NULL,
  lower.CI.bound = NA,
  median = NA,
  upper.CI.bound = NA,
  CI.prob = NULL,
  comment = " "
)

Arguments

`Z`	list of `design` with entries: `theta`, a $n x 4$ matrix with columns that give lower, median and upper quantiles of the central credible interval followed by the probability `CI.prob` allocated to the interval; `link`, the link function used; and `target`. This list object is created by `designLink`
`design.pt`	single integer that denotes design point of interest
`lower.CI.bound`	scalar that gives the lower bound of the central credible interval, default `NA`.
`median`	scalar value, default `NA`
`upper.CI.bound`	scalar that gives the upper bound of the central credible interval, default `NA`.
`CI.prob`	numeric, a fraction between 0 and 1 that defines probability attributed to central credible interval. For example, 1/2 for quartiles or 1/3 for tertiles. Default `NULL` uses the initial `CI.prob` as defined by `designLink`.
`comment`	character, ASCII text providing contributed commentary associated with elicitation design point. It is recommended to avoid special characters such as quotation marks etc.

Value

Z, a list of design with entries: theta, a $n x 4$ matrix with columns that give lower, median and upper quantiles followed by CI.prob with updated entries for row specified by argument design.pt; link, the link function used; and target.

Examples

X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
Z <- elicitPt(Z, design.pt = 1,
  lower.CI.bound = -1,
  median = 0,
  upper.CI.bound = 1,
  comment = "A completed elicitation scenario.")
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
Z <- elicitPt(Z, design.pt = 1,
  lower.CI.bound = -1,
  median = 0,
  upper.CI.bound = 1,
  comment = "A completed elicitation scenario.")

indirect: A package for assisting indirect elicitation of priors for generalised linear models.

Description

The indirect package provides three categories of functions: elicitation functions, fitting functions and visualisation functions.

Elicitation functions

These are the functions that are used to record expert opinion. This is where edits will be made and so on. The key function is designLink, which defines a list object that contains information about the design and elicitation. The elicitations are recorded and updated via function elicitPt.

Fitting functions

These are generally helper functions except for the function muSigma that is used for estimating the mean vector and covariance matrix of the unknown coefficients for the multivariate normal prior. Helper functions include mV for the elicited moments of conditional means priors.

Visualisation functions

These are functions for visualisation. The core function is plotDesignPoint.

References

Hosack, G. R., Hayes, K. R., & Barry, S. C. (2017). Prior elicitation for Bayesian generalised linear models with application to risk control option assessment. Reliability Engineering and System Safety, 167:351-361. doi:10.1016/j.ress.2017.06.011

Function to create summary document from a saved elicitation record.

Description

Creates a Sweave file that can be used to generate a pdf document of the summary report.

Usage

makeSweave(
  filename.rds = "",
  reportname = "",
  title = "Elicitation record",
  contact.details = "none",
  fitted.fractiles = TRUE,
  cumul.prob.bounds = c(0.05, 0.95)
)
makeSweave(
  filename.rds = "",
  reportname = "",
  title = "Elicitation record",
  contact.details = "none",
  fitted.fractiles = TRUE,
  cumul.prob.bounds = c(0.05, 0.95)
)

Arguments

`filename.rds`	character, filename of the record saved as an RDS object, see `?saveRDS`.
`reportname`	character, filename without extension to be used for the generated Sweave (`.Rnw`) file. The Sweave file supports the creation of report (`.pdf`) documentation and accompanying files such as the `.tex` file generated by using `Sweave` followed by `tools::texi2pdf()`.
`title`	character, a title for the report
`contact.details`	character, an email address or other mechanism by which the expert may contact the facilitator or rapporteur
`fitted.fractiles`	logical or numeric vector. A logical value of `FALSE` will not plot any fitted fractiles from the fitted subjective probability distribution. A logical value of `TRUE` will plot the fitted fractiles that correspond to the final iteration of the raw elicited fractiles. Alternatively, a numeric vector can specify arbitrary fractiles for plotting from the fitted distribution, e.g., `c(1/10, 1/4, 1/2, 3/4, 9/10)`
`cumul.prob.bounds`	numeric vector that specifies the upper and lower plot bounds determined by this credible interval. The default is the 0.90 central credible interval, `c(0.05, 0.95)`

Examples

## Not run:  
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
Z <- elicitPt(Z, design.pt = 1,
  lower.CI.bound = -1,
  median = 0,
  upper.CI.bound = 1,
  comment = "A completed elicitation scenario.")
tmp.rds <- tempfile(pattern = "record", fileext =".rds")
saveRecord(Z, file = tmp.rds)
tmpReport <- tempfile(pattern = "report")
makeSweave(filename.rds = tmp.rds, reportname = tmpReport)
setwd(tempdir())
utils::Sweave(paste0(tmpReport, ".Rnw"))
tools::texi2pdf(paste0(tmpReport, ".tex")) 

## End(Not run)
## Not run:  
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
Z <- elicitPt(Z, design.pt = 1,
  lower.CI.bound = -1,
  median = 0,
  upper.CI.bound = 1,
  comment = "A completed elicitation scenario.")
tmp.rds <- tempfile(pattern = "record", fileext =".rds")
saveRecord(Z, file = tmp.rds)
tmpReport <- tempfile(pattern = "report")
makeSweave(filename.rds = tmp.rds, reportname = tmpReport)
setwd(tempdir())
utils::Sweave(paste0(tmpReport, ".Rnw"))
tools::texi2pdf(paste0(tmpReport, ".tex")) 

## End(Not run)

Function to estimate mean and covariance for unknown parameters $\beta$ .

Description

Function to estimate mean and covariance for unknown parameters $\beta$ .

Usage

muSigma(Z, X = NULL, fit.method = "KL", wls.method = "default")
muSigma(Z, X = NULL, fit.method = "KL", wls.method = "default")

Arguments

`Z`	list of design points and link function that is an output of function `designLink`
`X`	model matrix for model formula and design points. The covariates must correspond to the description of design points in `Z`, but can be transformed etc. If `NULL` then `X` will be coerced by applying `as.matrix()` to `Z$design`. The matrix `X` should be full rank when subsetted to the elicited design points. If a column of `X` has the name `offset` then this column is treated as an offset during estimation
`fit.method`	character, `moment`, `KL`. See `mV`. Default is `KL`.
`wls.method`	character giving the numerical solution method: `QR`, using the QR decomposition, `SVD`, using the singular value decomposition, or option `default` that uses `solve()`

Value

list of mu, numeric vector of location parameters for the normal prior; Sigma, the covariance matrix; and log.like, a scalar

Examples

X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
Z <- elicitPt(Z, design.pt = 1,
  lower.CI.bound = -1,
  median = 0,
  upper.CI.bound = 1,
  comment = "The first completed elicitation scenario.")
Z <- elicitPt(Z, design.pt = 2,
  lower.CI.bound = -2,
  median = 1,
  upper.CI.bound = 2,
  comment = "The second completed elicitation scenario.")
prior <- muSigma(Z, X, fit.method = "KL")
prior$mu
prior$Sigma   
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
Z <- elicitPt(Z, design.pt = 1,
  lower.CI.bound = -1,
  median = 0,
  upper.CI.bound = 1,
  comment = "The first completed elicitation scenario.")
Z <- elicitPt(Z, design.pt = 2,
  lower.CI.bound = -2,
  median = 1,
  upper.CI.bound = 2,
  comment = "The second completed elicitation scenario.")
prior <- muSigma(Z, X, fit.method = "KL")
prior$mu
prior$Sigma

Helper function that translates elicited quantiles of target into independent conditional means normal prior for a defined inverse link function.

Description

The default for fit.method is option KL. This option uses an objective function that minimises a discretised directed divergence from a cumulative distribution implied by raw elicited fractiles to a normal conditional mean prior for the linear predictor. An alterative method moment assigns the location parameter of the normal conditional mean prior to the elicited median on the linear predictor scale. The variance parameter is estimated as $V = ((g(f_u) - g(f_l)/(qnorm(u) - qnorm(l)))^2$ , where $l$ is the probability associated with the fractile $f_l$ that defines the lower bound for the central credible interval and $u$ is the probability associated with the fractile $f_u$ that defines the upper bound for the central credible interval. This is also used to initialise the optimisation for the KL method. Another optimsation method that minimises the sum of squares is also available as method SS. See the vignette for more details on the choice of objective function for KL and SS.

Usage

mV(Z, fit.method = "KL")
mV(Z, fit.method = "KL")

Arguments

`Z`	list object that contains matrix `theta` of elicitations and character `link`, see `plotDesignPoint`
`fit.method`	character, `moment`, `KL`, `SS`. Default is `KL`.

Value

A list with vector of means m and diagonal covariance matrix V.

Helper function that gives the probability distribution function for design point.

Description

Helper function that gives the probability distribution function for design point.

Usage

pdist(x, Z, design.pt = NULL, fit.method = "KL")
pdist(x, Z, design.pt = NULL, fit.method = "KL")

Arguments

`x`	numeric: coordinate
`Z`	list of design points and link function, see `designLink`
`design.pt`	integer: design point
`fit.method`	character: method for fit in `mV`, default is `KL`

Examples

# design matrix: two scenarios
X <- matrix(c(1, 1, 0, 1), nrow = 2) 
rownames(X) <- c("scenario1", "scenario2")
colnames(X) <- c("covariate1", "covariate2")
#' # logit link
# central credible intervals with probability = 1/2
Z <- designLink(design = X, link = "logit", CI.prob = 0.5)
#' # lower and upper quartiles and median
Z <- indirect::elicitPt(Z, design.pt = 1, 
  lower.CI.bound = 0.2,
  median = 0.4,
  upper.CI.bound = 0.6,
  comment = "Completed.")
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = TRUE, fitted.curve = TRUE)
  
# probability that target is below 0.1 and
# probability that target is below 0.9   
indirect::pdist(c(0.1, 0.9), Z, design.pt = 1)
# design matrix: two scenarios
X <- matrix(c(1, 1, 0, 1), nrow = 2) 
rownames(X) <- c("scenario1", "scenario2")
colnames(X) <- c("covariate1", "covariate2")
#' # logit link
# central credible intervals with probability = 1/2
Z <- designLink(design = X, link = "logit", CI.prob = 0.5)
#' # lower and upper quartiles and median
Z <- indirect::elicitPt(Z, design.pt = 1, 
  lower.CI.bound = 0.2,
  median = 0.4,
  upper.CI.bound = 0.6,
  comment = "Completed.")
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = TRUE, fitted.curve = TRUE)
  
# probability that target is below 0.1 and
# probability that target is below 0.9   
indirect::pdist(c(0.1, 0.9), Z, design.pt = 1)

Plot elicited data, fitted marginals or model output

Description

Plot elicited data, fitted marginals or model output

Usage

plotDesignPoint(
  Z,
  X = NULL,
  design.pt = NULL,
  elicited.fractiles = TRUE,
  fitted.fractiles = FALSE,
  fitted.curve = FALSE,
  CI.prob = NULL,
  estimated.probs = NULL,
  modelled.fractiles = FALSE,
  modelled.curve = FALSE,
  cumul.prob.bounds = c(0.05, 0.95),
  theta.bounds = NULL,
  ylim.max = NULL,
  xlog = FALSE,
  design.table = TRUE,
  n.pts = 101
)
plotDesignPoint(
  Z,
  X = NULL,
  design.pt = NULL,
  elicited.fractiles = TRUE,
  fitted.fractiles = FALSE,
  fitted.curve = FALSE,
  CI.prob = NULL,
  estimated.probs = NULL,
  modelled.fractiles = FALSE,
  modelled.curve = FALSE,
  cumul.prob.bounds = c(0.05, 0.95),
  theta.bounds = NULL,
  ylim.max = NULL,
  xlog = FALSE,
  design.table = TRUE,
  n.pts = 101
)

Arguments

`Z`	list object that contains matrix `theta` of elicitations, character `link` and character `target` as initialised by `designLink` and updated by `elicitPt`
`X`	design matrix (can be `NULL`, unless modelled output is requested)
`design.pt`	single integer that denotes design point of interest
`elicited.fractiles`	logical, plot vertical lines for elicited fractiles?
`fitted.fractiles`	logical, plot vertical lines for fitted conditional mean prior fractiles for this design point? Alternatively, a numeric vector of arbitrary fractiles to be plotted from the fitted elicitation distribution. If `TRUE` then the fractiles corresponding to the median, upper and lower level central CI are plotted
`fitted.curve`	logical plot fitted conditional mean prior density for this design point?
`CI.prob`	numeric scalar, locally specified probability assigned to the elicited central credible interval of the current design point. Defaults to `NULL` in which case the global value initially assigned by `designLink` or as updated by `elicitPt` is used
`estimated.probs`	numeric vector of values for which estimated probabilities are to be estimated from the fitted elicitation distribution for the target theta. Default is `NULL`. The result is output to the console.
`modelled.fractiles`	logical, plot vertical lines for modelled fractiles from the conditional mean prior distribution fit to all design points? This option requires a design matrix `X` of full column rank.
`modelled.curve`	logical, plot modelled conditional mean prior density for the entire model? This option requires a design matrix `X` of full column rank.
`cumul.prob.bounds`	numeric vector of length two, giving plot bounds by cumulative probability. This argument is ignored if there is not enough data to fit a parametric distribution or if `theta.bounds` is not `NULL`
`theta.bounds`	numeric vector giving support of response for plotting purposes (can be `NULL`). This will overwrite `cumul.prob.bounds`, if applicable
`ylim.max`	numeric maximum value of y-axis (can be `NULL`)
`xlog`	logical log x-axis
`design.table`	logical include design dataframe, elicited fractiles and modelled or fitted fractiles
`n.pts`	numeric giving number of point to evalate density curve (if plotted)

Value

a plot to the current device. See dev.cur() to check.

Examples

# design matrix: two scenarios
X <- matrix(c(1, 1, 0, 1), nrow = 2) 
rownames(X) <- c("scenario1", "scenario2")
colnames(X) <- c("covariate1", "covariate2")

# logit link
# central credible intervals with probability = 1/2
Z <- designLink(design = X, link = "logit", CI.prob = 0.5)

# 1st design point
# no elicited fractiles
indirect::plotDesignPoint(Z, design.pt = 1) 
# elicited median
Z <- indirect::elicitPt(Z, design.pt = 1, 
  lower.CI.bound = NA,
  median = 0.4,
  upper.CI.bound = NA,
  CI.prob = NULL)
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1))
# lower and upper quartiles and median
Z <- indirect::elicitPt(Z, design.pt = 1, 
  lower.CI.bound = 0.2,
  median = 0.4,
  upper.CI.bound = 0.6,
  comment = "Completed.")
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = TRUE, fitted.curve = TRUE)
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = c(1/10, 1/4, 1/2, 3/4, 9/10), 
  fitted.curve = TRUE)   
  
# second design point   
# central credible intervals with probability = 1/3 
# elicit upper and lower tertiles
Z <- elicitPt(Z, design.pt = 2,
  lower.CI.bound = 0.1,
  upper.CI.bound = 0.3,
  CI.prob = 1/3,
  comment = "Switched to tertiles.")
indirect::plotDesignPoint(Z, design.pt = 2,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1))   
indirect::plotDesignPoint(Z, design.pt = 2,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = TRUE, fitted.curve = TRUE)
indirect::plotDesignPoint(Z, design.pt = 2,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = c(1/10, 1/3, 1/2, 2/3, 9/10), 
  fitted.curve = TRUE) 
# design matrix: two scenarios
X <- matrix(c(1, 1, 0, 1), nrow = 2) 
rownames(X) <- c("scenario1", "scenario2")
colnames(X) <- c("covariate1", "covariate2")

# logit link
# central credible intervals with probability = 1/2
Z <- designLink(design = X, link = "logit", CI.prob = 0.5)

# 1st design point
# no elicited fractiles
indirect::plotDesignPoint(Z, design.pt = 1) 
# elicited median
Z <- indirect::elicitPt(Z, design.pt = 1, 
  lower.CI.bound = NA,
  median = 0.4,
  upper.CI.bound = NA,
  CI.prob = NULL)
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1))
# lower and upper quartiles and median
Z <- indirect::elicitPt(Z, design.pt = 1, 
  lower.CI.bound = 0.2,
  median = 0.4,
  upper.CI.bound = 0.6,
  comment = "Completed.")
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = TRUE, fitted.curve = TRUE)
indirect::plotDesignPoint(Z, design.pt = 1,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = c(1/10, 1/4, 1/2, 3/4, 9/10), 
  fitted.curve = TRUE)   
  
# second design point   
# central credible intervals with probability = 1/3 
# elicit upper and lower tertiles
Z <- elicitPt(Z, design.pt = 2,
  lower.CI.bound = 0.1,
  upper.CI.bound = 0.3,
  CI.prob = 1/3,
  comment = "Switched to tertiles.")
indirect::plotDesignPoint(Z, design.pt = 2,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1))   
indirect::plotDesignPoint(Z, design.pt = 2,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = TRUE, fitted.curve = TRUE)
indirect::plotDesignPoint(Z, design.pt = 2,   
  elicited.fractiles = TRUE, theta.bounds = c(0, 1),
  fitted.fractiles = c(1/10, 1/3, 1/2, 2/3, 9/10), 
  fitted.curve = TRUE)

Function to save elicitation record.

Description

Function to save elicitation record.

Usage

saveRecord(
  designLink.obj,
  conclusion.comments = "This concludes the elicitation record.",
  file = ""
)
saveRecord(
  designLink.obj,
  conclusion.comments = "This concludes the elicitation record.",
  file = ""
)

Arguments

`designLink.obj`	list object initally created by function `designLink` and subsequently updated by function `elicitPt`
`conclusion.comments`	character, comments to conclude session. Beware of non-ASCII text and special characters, which may affect ability to save or generate a `Sweave` document by using `makeSweave`
`file`	character providing filename.

Value

an RDS file is created with filename file. A timestamp is added to designLink.obj using Sys.time().

Examples

## Not run: 
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
tmp <- tempfile(pattern = "report", fileext =".rds")
saveRecord(Z, file = tmp)

## End(Not run)
## Not run: 
X <- matrix(c(1, 1, 0, 1), nrow = 2) # design
Z <- designLink(design = X)
tmp <- tempfile(pattern = "report", fileext =".rds")
saveRecord(Z, file = tmp)

## End(Not run)

Package 'indirect'

Help Index

Helper function that checks for sensible covariate matrix.

Description

Usage

Arguments

Value

Function to check condition number diagnostic.

Description

Usage

Arguments

Value

Examples

Create list with information for the elicitation session

Description

Usage

Arguments

Details

Value

Examples

density for Gompertz transformed univariate Gaussian

Description

Usage

Arguments

Value

Examples

density for logit transformed univariate Gaussian

Description

Usage

Arguments

Value

Examples

Function to create or update elicitation at a given design point.

Description

Usage

Arguments

Value

Examples

indirect: A package for assisting indirect elicitation of priors for generalised linear models.

Description

Elicitation functions

Fitting functions

Visualisation functions

References

Function to create summary document from a saved elicitation record.

Description

Usage

Arguments

Examples

Function to estimate mean and covariance for unknown parameters β\betaβ.

Description

Usage

Arguments

Value

Examples

Helper function that translates elicited quantiles of target into independent conditional means normal prior for a defined inverse link function.

Description

Usage

Arguments

Value

Helper function that gives the probability distribution function for design point.

Description

Usage

Arguments

Examples

Plot elicited data, fitted marginals or model output

Description

Usage

Arguments

Value

Examples

Function to save elicitation record.

Description

Usage

Arguments

Value

Examples

Function to estimate mean and covariance for unknown parameters $\beta$ .