Z-test.

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

Usage

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,
  ...
)

Arguments

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

Value

Same kind of object as the one returns by the stats chisq.test function for example.

Details

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.

References

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.

Examples

# 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
#>