wald: Test significance of TRIM coefficients with the Wald test

In rtrim: Trends and Indices for Monitoring Data

Description

Test significance of TRIM coefficients with the Wald test

Usage

wald(x)

## S3 method for class 'trim'
wald(x)

Arguments

x	TRIM output structure (i.e., output of a call to trim)

Value

A model-dependent list of Wald statistics

See Also

Other analyses: coef.trim(), confint.trim(), gof(), index(), now_what(), overall(), overdispersion(), plot.trim.index(), plot.trim.overall(), results(), serial_correlation(), summary.trim(), totals(), trim(), vcov.trim()

Examples

data(skylark)
z2 <- trim(count ~ site + time, data=skylark, model=2)
# print info on significance of slope parameters
print(z2)
z3 <- trim(count ~ site + time, data=skylark, model=3)
# print info on significance of deviations from linear trend
wald(z3)

Example output

Call:
trim(count ~ site + time, data = skylark, model = 2)

 Convergence reached after 3 iterations 

Coefficients:
  from upto        add     se_add      mul     se_mul
1    1    8 0.05482546 0.01043636 1.056356 0.01102452
Wald test for significance of deviations from linear trend
  Wald = 20.29, df=6, p=0.002461

rtrim documentation built on April 21, 2020, 5:06 p.m.