Polynomial dose-response function

dpoly(
  degree = 1,
  beta.1 = "rel",
  beta.2 = "rel",
  beta.3 = "rel",
  beta.4 = "rel"
)

Arguments

degree: The degree of the polynomial - e.g. degree=1 for linear, degree=2 for quadratic, degree=3 for cubic.
beta.1: Pooling for the 1st polynomial coefficient. Can take "rel", "common", "random" or be assigned a numeric value (see details).
beta.2: Pooling for the 2nd polynomial coefficient. Can take "rel", "common", "random" or be assigned a numeric value (see details).
beta.3: Pooling for the 3rd polynomial coefficient. Can take "rel", "common", "random" or be assigned a numeric value (see details).
beta.4: Pooling for the 4th polynomial coefficient. Can take "rel", "common", "random" or be assigned a numeric value (see details).

Value

An object of class("dosefun")

Details

$\beta_1$ represents the 1st coefficient.
$\beta_2$ represents the 2nd coefficient.
$\beta_3$ represents the 3rd coefficient.
$\beta_4$ represents the 4th coefficient.

Linear model: $$\beta_1{x}$$

Quadratic model: $$\beta_1{x} + \beta_2{x^2}$$

Cubic model: $$\beta_1{x} + \beta_2{x^2} + \beta_3{x^3}$$

Quartic model: $$\beta_1{x} + \beta_2{x^2} + \beta_3{x^3} + \beta_4{x^4}$$

Dose-response parameters

Argument	Model specification
`"rel"`	Implies that relative effects should be pooled for this dose-response parameter separately for each agent in the network.
`"common"`	Implies that all agents share the same common effect for this dose-response parameter.
`"random"`	Implies that all agents share a similar (exchangeable) effect for this dose-response parameter. This approach allows for modelling of variability between agents.
`numeric()`	Assigned a numeric value, indicating that this dose-response parameter should not be estimated from the data but should be assigned the numeric value determined by the user. This can be useful for fixing specific dose-response parameters (e.g. Hill parameters in Emax functions) to a single value.

When relative effects are modelled on more than one dose-response parameter, correlation between them is automatically estimated using a vague inverse-Wishart prior. This prior can be made slightly more informative by specifying the scale matrix omega and by changing the degrees of freedom of the inverse-Wishart prior using the priors argument in mbnma.run().

References

There are no references for Rd macro \insertAllCites on this help page.

Examples

# Linear model with random effects
dpoly(beta.1="rel")
#> $name
#> [1] "poly"
#> 
#> $fun
#> ~beta.1 * dose
#> <environment: 0x55e26dea1590>
#> 
#> $params
#> [1] "beta.1"
#> 
#> $nparam
#> [1] 1
#> 
#> $jags
#> [1] "s.beta.1[agent[i,k]] * dose[i,k]"
#> 
#> $apool
#> beta.1 
#>  "rel" 
#> 
#> $bname
#>   beta.1 
#> "beta.1" 
#> 
#> attr(,"class")
#> [1] "dosefun"

# Quadratic model dose-response function
# with an exchangeable (random) absolute parameter estimated for the 2nd coefficient
dpoly(beta.1="rel", beta.2="random")
#> $name
#> [1] "poly"
#> 
#> $fun
#> ~beta.1 * dose
#> <environment: 0x55e26afc5378>
#> 
#> $params
#> [1] "beta.1"
#> 
#> $nparam
#> [1] 1
#> 
#> $jags
#> [1] "s.beta.1[agent[i,k]] * dose[i,k]"
#> 
#> $apool
#> beta.1 
#>  "rel" 
#> 
#> $bname
#>   beta.1 
#> "beta.1" 
#> 
#> attr(,"class")
#> [1] "dosefun"