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

Randomizes elements in each column in xcan and recalculates
`hier.part`

num.reps times

1 2 3 4 5 6 7 8 |

`y` |
a vector containing the dependent variables |

`xcan` |
a dataframe containing the n independent variables |

`family` |
a character string naming a family function used by |

`link` |
character specification of the link function, only used if family =
"beta" or "ordinal". For "beta", this argument equals the "link" argument
in |

`gof` |
Goodness-of-fit measure. Currently "RMSPE", Root-mean-square 'prediction' error, "logLik", Log-Likelihood or "Rsqu", R-squared. R-squared is only applicable if family = "Gaussian". |

`num.reps` |
Number of repeated randomizations |

`...` |
additional arguments to passed to |

This function is a randomization routine for the `hier.part`

function which returns a matrix of I values (the independent
contribution towards explained variance in a multivariate dataset) for
all values from num.reps randomizations. For each randomization, the
values in each variable (i.e each column of xcan) are randomized
independently, and hier.part is run on the randomized xcan. As well as
the randomized I matrix, the function returns a summary table listing
the observed I values, the 95th and 99th percentile values of I for
the randomized dataset.

a list containing

`Irands` |
matrix of num.reps + 1 rows of I values for each predictor variable. The first row contains the observed I values and the remaining num.reps rows contains the I values returned for each randomization. |

`Iprobs` |
data.frame of observed I values for each variable, Z-scores for the
generated distribution of randomized Is and an indication of
statistical significance. Z-scores are calculated as
(observed - mean(randomizations))/sd(randomizations),
and statistical significance (*) is based on upper 0.95 confidence
limit ( |

Chris Walsh cwalsh@unimelb.edu.au.

Hatt, B. E., Fletcher, T. D., Walsh, C. J. and Taylor, S. L. 2004 The
influence of urban density and drainage infrastructure on the
concentrations and loads of pollutants in small
streams. *Environmental Management* **34**, 112–124.

Mac Nally, R. 2000 Regression and model building in conservation
biology, biogeography and ecology: the distinction between and
reconciliation of 'predictive' and 'explanatory' models. *Biodiversity
and Conservation* **9**, 655–671.

Mac Nally, R. 2002 Multiple regression and inference in conservation
biology and ecology: further comments on identifying important
predictor variables. *Biodiversity and Conservation* **11**,
1397–1401.

Walsh, C. J., Papas, P. J., Crowther, D., Sim, P. T., and Yoo, J. 2004
Stormwater drainage pipes as a threat to a stream-dwelling amphipod of
conservation significance, *Austrogammarus australis*, in southeastern
Australia. *Biodiversity and Conservation* **13**, 781–793.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
#linear regression of log(electrical conductivity) in streams
#against four independent variables describing catchment
#characteristics (from Hatt et al. 2004).
## Not run: data(urbanwq)
env <- urbanwq[,2:5]
rand.hp(urbanwq$lec, env, fam = "gaussian",
gof = "Rsqu", num.reps = 999)$Iprobs
## End(Not run)
#logistic regression of an amphipod species occurrence in
#streams against four independent variables describing
#catchment characteristics (from Walsh et al. 2004).
## Not run: data(amphipod)
env1 <- amphipod[,2:5]
rand.hp(amphipod$australis, env1, fam = "binomial",
gof = "logLik", num.reps = 999)$Iprobs
## End(Not run)
``` |

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