Performs z-tests for Fisher's aggregation indices (computed with either count or incidence data).

z.test(x, ...) # S3 method for default z.test(x, ...) # S3 method for fisher z.test(x, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, ...)

x | The output of the |
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... | Not yet implemented. |

alternative | A character string specifying the alternative hypothesis. It must be one of "two.sided" (default), "less" or "greater". |

conf.level | The confidence level of the interval. |

For two-sided tests with a confidence level of 95 the spatial pattern would be random. If z < -1.96 or z > 1.96, it would be uniform or aggregated, respectively.

For count and incidence data:

Moradi-Vajargah M, Golizadeh A, Rafiee-Dastjerdi H, Zalucki MP, Hassanpour M, Naseri B. 2011. Population density and spatial distribution pattern of Hypera postica (Coleoptera: Curculionidae) in Ardabil, Iran. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 39(2): 42-48.

Sun P, Madden LV. 1997. Using a normal approximation to test for the binomial distribution. Biometrical journal, 39(5): 533-544.

# For incidence data: my_incidence <- incidence(tobacco_viruses) my_fisher <- agg_index(my_incidence, method = "fisher") z.test(my_fisher)#> #> One-sample z-test #> #> data: my_fisher #> z = 13.207, p-value < 2.2e-16 #> alternative hypothesis: two.sided #>