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

This function calculates the phenotypic mean on the observed scale from the latent mean and variance.

1 |

`mu` |
Latent intercept estimated from a GLMM (ignored if predict is not |

`var` |
Latent total variance estimated from a GLMM. Usually, the sum of the estimated variances of the random effects, plus the "residual" variance. (numeric of length 1) |

`link.inv` |
Inverse function of the link function. (function) |

`predict` |
Optional vector of predicted values on the latent scale (i.e. matrix product |

`width` |
Parameter for the integral computation. The integral is evaluated from |

This function needs the latent population mean (`mu`

) or the marginal predicted values (`predict`

) and the total latent variance (i.e. total latent variance `var`

) to compute the observed phenotypic mean. To do so, it also requires the inverse function of the link function.

For example, if the link function is the natural logarithm, the inverse-link function will be the exponential. The inverse-link functions for many models are yielded by the `QGlink.funcs`

function.

Contrary to `QGparams`

, `QGmean.obs`

never uses the closed form solutions, but always compute the integrals.

This function yields the phenotypic mean on the observed scale. (numeric)

Pierre de Villemereuil & Michael B. Morrissey

`QGmvmean`

, `QGparams`

, `QGpred`

, `QGlink.funcs`

, `QGvar.dist`

, `QGvar.exp`

, `QGpsi`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Computing the observed mean for a probit link
QGmean(mu = 0.3, var = 1, link.inv = pnorm)
# The theoretical expectation is
1 - pnorm(0, 0.3, sqrt(1 + 1))
# Or, using the QGlink.funcs function
QGmean(mu = 0.3, var = 1, link.inv = QGlink.funcs(name = "binom1.probit")$inv.link)
## Computing the observed mean for a logarithm link
QGmean(mu = 1, var = 1, link.inv = exp)
# The theoretical expectation is
exp(1 + 0.5 * 1)
# This computation is automatically performed by QGparams
# but directly using the closed form solution when available
QGparams(mu = 1, var.p = 1, var.a = 0.5, model = "Poisson.log")
``` |

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