Description Usage Arguments Details Value Author(s) References See Also Examples

Fits the FH-HETOP model described by Lockwood, Castellano and Shear
(2018) using the `jags`

function in R2jags.

1 2 |

`ngk` |
Numeric matrix of dimension |

`fixedcuts` |
A vector of length 2 providing the first two cutpoints, to identify
the location and scale of the group parameters. Note that this
suffices for any |

`p` |
Vector of length 2 giving degrees of freedom for cubic spline basis to parameterize Efron priors for group means and group standard deviations; see References. |

`m` |
Vector of length 2 giving number of grid points to parameterize Efron priors for group means and group standard deviations; see References. |

`gridL` |
Vector of length 2 of lower bounds for grids to parameterize Efron priors for group means and group standard deviations; see References. |

`gridU` |
Vector of length 2 of upper bounds for grids to parameterize Efron priors for group means and group standard deviations; see References. |

`Xm` |
Optional matrix of covariates for the group means. |

`Xs` |
Optional matrix of covariates for the log group standard deviations. |

`seed` |
Passed to |

`modelfileonly` |
If TRUE, function returns location of JAGS model file only, without running JAGS. Default is FALSE. |

`modloc` |
Optional character vector of length 1 providing the full path to the name of file where the JAGS model code will be written. Defaults to NULL, in which case the code will be written to a temporary file. |

` ... ` |
Additional arguments to |

The function is basically a wrapper for `jags`

, building
model code depending on the specification of the Efron priors and any
covariates for the group means and group standard deviations. Details
on the FH-HETOP model are provided by Lockwood, Castellano and Shear
(2018).

Covariates to predict the group means and group log standard
deviations are optional. However, `Xm`

and `Xs`

must both
be either NULL, or specified; the current version of this function
cannot use covariates to predict one set of parameters but not use any
covariates to predict the other set. While covariates in general must
be present or absent simultaneously for the two sets of parameters, it
is not necessary that the same covariates be used to predict the two
sets of parameters. All covariates must be centered so that they sum
to zero across groups.

A object of class `rjags`

, with additional information
specific to the FH-HETOP model. The additional information is stored
as a list called `fh_hetop_extras`

with the following components:

`Finfo` |
A list containing information used to estimate the population
distribution of the residuals from the FH-HETOP model. Note that
the posterior samples of the parameters defining the residual
distribution can be found in the |

`Dinfo` |
A list containing information about the data used to the fit the model, including the counts, covariates and fixed cutpoints. |

`waicinfo` |
A list containing information about the WAIC for the
estimated model; see help file for |

`est_star_samps` |
A list with posterior samples of parameters with
respect to the 'star' scale which defines the location and scale of
the group means and standard deviations that corresponds to a marginal
population mean of zero and marginal population standard deviation of
1. Additional details in help file for |

`est_star_mug` |
A dataframe containing various estimates of the
group means on the 'star' scale, including posterior means,
Constrained Bayes and Triple-Goal estimates. Additional details in
help file for |

`est_star_sigmag` |
A dataframe containing various estimates of the
group standard deviations on the 'star' scale, including posterior
means, Constrained Bayes and Triple-Goal estimates. Additional
details in help file for |

J.R. Lockwood jrlockwood@ets.org

Efron B. (2016).
“Empirical Bayes deconvolution estimates,”
*Biometrika* 103(1):1–20.

Lockwood J.R., Castellano K.E. and Shear B.R. (2018).
“Flexible Bayesian models for inferences from coarsened,
group-level achievement data,”
*Journal of Educational and Behavioral Statistics.* 43(6):663–692.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
set.seed(1001)
## define mean-centered covariates
G <- 12
z1 <- sample(c(0,1), size=G, replace=TRUE)
z2 <- 0.5*z1 + rnorm(G)
Z <- cbind(z1 - mean(z1), z2 = z2 - mean(z2))
## define true parameters dependent on covariates
beta_m <- c(0.3, 0.8)
beta_s <- c(0.1, -0.1)
mug <- Z[,1]*beta_m[1] + Z[,2]*beta_m[2] + rnorm(G, sd=0.3)
sigmag <- exp(0.3 + Z[,1]*beta_s[1] + Z[,2]*beta_s[2] + 0.2*rt(G, df=7))
cutpoints <- c(-1.0, 0.0, 1.2)
## generate data
ng <- rep(200,G)
ngk <- gendata_hetop(G, K = 4, ng, mug, sigmag, cutpoints)
print(ngk)
## fit FH-HETOP model including covariates
## NOTE: using an extremely small number of iterations for testing,
## so that convergence is not expected
m <- fh_hetop(ngk, fixedcuts = c(-1.0, 0.0), p = c(10,10),
m = c(100, 100), gridL = c(-5.0, log(0.10)),
gridU = c(5.0, log(5.0)), Xm = Z, Xs = Z,
n.iter = 100, n.burnin = 50)
print(m)
print(names(m$fh_hetop_extras))
s <- m$BUGSoutput$summary
print(data.frame(truth = c(beta_m, beta_s), s[grep("beta", rownames(s)),]))
print(cor(mug, s[grep("mu", rownames(s)),"mean"]))
print(cor(sigmag, s[grep("sigma", rownames(s)),"mean"]))
## manual calculation of WAIC (see help file for waic_hetop)
tmp <- waic_hetop(ngk, m$BUGSoutput$sims.matrix)
identical(tmp, m$fh_hetop_extras$waicinfo)
``` |

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