# Liland.test: A test for over represented short distances in the Liland... In fixedTimeEvents: The Distribution of Distances Between Discrete Events in Fixed Time

## Description

A binomial test is performed using probabilites from the Liland distribution to check if the number of distances shorter to or equal to `xlim` are significantly higher than the expected value. Critical value and power are supplied as separate functions.

## Usage

 ```1 2 3 4 5 6 7``` ```Liland.test(y, xlim, R, r) ## S3 method for class 'Ltest' print(x, ...) ## S3 method for class 'Ltest' summary(object, ...) Liland.crit(xlim, R, r, alpha = 0.05) Liland.pow(xlim, R, r, y = 1:(r-1), alpha = 0.05) ```

## Arguments

 `y` The number of observed short distances. `xlim` The maximum distance that is seen as short. `R` The number of trials. `r` The number of successes. `alpha` Significance level. `x` The object to printed. `object` The object to be summarized. `...` Additional arguments for print and summary (not used).

## Value

`Liland.test` returns a named vector of P-values with class `Ltest`. The other methods only print.

## References

Liland, KH & Snipen, L, FixedTimeEvents: An R package for the distribution of distances between discrete events in fixed time, SoftwareX 5 (2016).

`dLiland`, `Liland`, `simLiland`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```Lt <- Liland.test(12,1,1949,162) print(Lt) summary(Lt) # Critical value Liland.crit(1, 1949, 162) # Power plot(Liland.pow(1,1949,161, alpha = 0.05), type = 'l', xlab = '#(x<2)', ylab = 'power') ```

### Example output

```P-value for H0: y > E(Y)
12
0.5846628
R = 1949, r = 162, xlim = 1
E(Y) = 13.38225

P-value for H0: y > E(Y)
12
0.5846628
[1] 19
```

fixedTimeEvents documentation built on Jan. 4, 2022, 5:09 p.m.