R: Yellow-bellied Prinia

prinia {VGAM} | R Documentation

## Yellow-bellied Prinia

### Description

A data frame with yellow-bellied Prinia.

### Usage

    data(prinia)

### Format

A data frame with 151 observations on the following 23 variables.

length

  a numeric vector, the scaled wing length (zero mean and unit variance).

fat

  a numeric vector, fat index; originally 1 (no fat) to 4 (very fat) but converted to 0 (no fat) versus 1 otherwise.

cap

  a numeric vector, number of times the bird was captured or recaptured.

noncap

  a numeric vector, number of times the bird was not captured.

y01, y02, y03, y04, y05, y06

  a numeric vector of 0s and 1s; for noncapture and capture resp.

y07, y08, y09, y10, y11, y12

  same as above.

y13, y14, y15, y16, y17, y18, y19

  same as above.

### Details

The yellow-bellied Prinia Prinia flaviventris is a common bird species located in Southeast Asia. A capture–recapture experiment was conducted at the Mai Po Nature Reserve in Hong Kong during 1991, where captured individuals had their wing lengths measured and fat index recorded. A total of 19 weekly capture occasions were considered, where 151 distinct birds were captured.

More generally, the prinias are a genus of small insectivorous birds, and are sometimes referred to as wren-warblers. They are a little-known group of the tropical and subtropical Old World, the roughly 30 species being divided fairly equally between Africa and Asia.

### Source

Thanks to Paul Yip for permission to make this data available.

Hwang, W.-H. and Huggins, R. M. (2007) Application of semiparametric regression models in the analysis of capture–recapture experiments. Australian and New Zealand Journal of Statistics 49, 191–202.

### Examples

    head(prinia)
    summary(prinia)
    rowSums(prinia[, c("cap", "noncap")])  # 19s

    #  Fit a positive-binomial distribution (M.h) to the data:
    fit1 <- vglm(cbind(cap, noncap) ~ length + fat, posbinomial, data = prinia)

    #  Fit another positive-binomial distribution (M.h) to the data:
    #  The response input is suitable for posbernoulli.*-type functions.
    fit2 <- vglm(cbind(y01, y02, y03, y04, y05, y06, y07, y08, y09, y10,
                       y11, y12, y13, y14, y15, y16, y17, y18, y19) ~
                 length + fat, posbernoulli.b(drop.b = FALSE ~ 0), data = prinia)

[Package VGAM version 1.1-3 Index]