plot.ddt_lcm {ddtlcm}

Create trace plots of DDT-LCM parameters

Description

Create trace plots of DDT-LCM parameters

Usage

## S3 method for class 'ddt_lcm'
plot(
  x,
  parameter_names = c("responseprob_1,1,1", "classprob_1", "c", "diffusionvar_1"),
  burnin = 50,
  ...
)

Arguments

x

a "ddt_lcm" object

parameter_names

a character vector to specify the parameters to be plotted. Each element can take the be 1) of format "parameter_index" to plot specific parameters with specific indices, 2) of format "parameter" to plot the parameters across all indices, or 3) equal to "all" to plot all parameters in the model. For 1), the item response probabilities should be named "responseprob_class,group,item"; the class probabilities should be named "classprob_class"; the divergence function parameter is "c"; the group-specific diffusion variances should be named "diffusionvar_group". For 2), "responseprob" to plot all item response probabilities; "classprob" to plot all class probabilities; "diffusionvar" to plot all diffusion variances.

burnin

the number of posterior samples as burn-in, which will not be plotted.

...

Further arguments passed to each method

Value

NULLs

Examples

data(result_diet_1000iters)
# Plot "c" for the divergence function parameter; "diffusionvar_1" for diffusion variance of group 1
plot(x = result_diet_1000iters, parameter_names = c("c", "diffusionvar_1"), burnin = 500)
# Plot "responseprob_1,1,1" for the class 1 response probability of item 3 in major group 2
plot(x = result_diet_1000iters, parameter_names = "responseprob_1,1,1", burnin = 500)
# Plot "classprob_1" for the probability of being assigned to class 1
plot(x = result_diet_1000iters, parameter_names = "classprob_1", burnin = 500)
# plot all class probabilities
plot(x = result_diet_1000iters, parameter_names = "classprob", burnin = 500)
# plot all diffusion variances
plot(x = result_diet_1000iters, "diffusionvar", burnin = 500)