Generalized binomial N-mixture model for repeated count data

This function uses an ad-hoc averaging approach to impute missing entries in obsCovs. The missing entry is replaced by an average of the average for the site and the average for the visit number.

1 | ```
imputeMissing(umf, whichCovs = seq(length=ncol(obsCovs(umf))))
``` |

`umf` |
The data set who's obsCovs are being imputed. |

`whichCovs` |
An integer vector giving the indices of the covariates to be imputed.
This defaults to all covariates in |

A version of `umf`

that has the requested obsCovs imputed.

Ian Fiske

1 2 3 4 5 6 7 8 | ```
data(frogs)
pcru.obscovs <- data.frame(MinAfterSunset=as.vector(t(pcru.data[,,1])),
Wind=as.vector(t(pcru.data[,,2])),
Sky=as.vector(t(pcru.data[,,3])),
Temperature=as.vector(t(pcru.data[,,4])))
pcruUMF <- unmarkedFrameOccu(y = pcru.bin, obsCovs = pcru.obscovs)
pcruUMF.i1 <- imputeMissing(pcruUMF)
pcruUMF.i2 <- imputeMissing(pcruUMF, whichCovs = 2)
``` |

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