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, ...)



The output of the agg_index function with method = "fisher" as parameter.


Not yet implemented.


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


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.

See also


# 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 #>