anova.hopit {hopit}

Likelihood Ratio Test Tables

Description

Perform the likelihood ratio test(s) for two or more hopit objects.

Usage

## S3 method for class 'hopit'
anova(object, ..., method = c("sequential",
"with.most.complex", 'with.least.complex'),
direction = c("decreasing", "increasing"))

Arguments

object

an object containing the results returned by a hopit.

...

an additional object(s) of the same type.

method

the method of ordered model comparisons. Choose "sequential" for 1-2, 2-3, 3-4, ... comparisons or "with.most.complex" for 1-2, 1-3, 1-4, ... comparisons, where 1 is the most complex model (the least complex for "with.least.complex").

direction

determine if the complexity of listed models is "increasing" or "decreasing" (default).

Value

a vector or a matrix with the results of the test(s).

Author(s)

Maciej J. Danko

See Also

print.lrt.hopit, lrt.hopit, hopit.

Examples

# DATA
data(healthsurvey)

# the order of response levels decreases from the best health to
# the worst health; hence the hopit() parameter decreasing.levels
# is set to TRUE
levels(healthsurvey$health)

# Example 1 ---------------------

# fitting two nested models
model1 <- hopit(latent.formula = health ~ hypertension + high_cholesterol +
                heart_attack_or_stroke + poor_mobility + very_poor_grip +
                depression + respiratory_problems +
                IADL_problems + obese + diabetes + other_diseases,
              thresh.formula = ~ sex + ageclass + country,
              decreasing.levels = TRUE,
              control = list(trace = FALSE),
              data = healthsurvey)

# a model with an interaction between hypertension and high_cholesterol
model2 <- hopit(latent.formula = health ~ hypertension * high_cholesterol +
                heart_attack_or_stroke + poor_mobility + very_poor_grip +
                depression + respiratory_problems +
                IADL_problems + obese + diabetes + other_diseases,
              thresh.formula = ~ sex + ageclass + country,
              decreasing.levels = TRUE,
              control = list(trace = FALSE),
              data = healthsurvey)

# a likelihood ratio test
lrt1 <- anova(model1, model2)
lrt1

# print results in a shorter form
print(lrt1, short = TRUE)

# or equivalently
lrt.hopit(model2, model1)

# Example 2 ---------------------

# fitting additional nested models
model3 <- hopit(latent.formula = health ~ hypertension * high_cholesterol +
                heart_attack_or_stroke + poor_mobility + very_poor_grip +
                depression + respiratory_problems +
                IADL_problems + obese * diabetes + other_diseases,
              thresh.formula = ~ sex + ageclass + country,
              decreasing.levels = TRUE,
              control = list(trace = FALSE),
              data = healthsurvey)

model4 <- hopit(latent.formula = health ~ hypertension * high_cholesterol +
                heart_attack_or_stroke + poor_mobility + very_poor_grip +
                depression + respiratory_problems +
                IADL_problems + obese * diabetes + other_diseases,
              thresh.formula = ~ sex * ageclass + country,
              decreasing.levels = TRUE,
              control = list(trace = FALSE),
              data = healthsurvey)

# sequential likelihood ratio tests
# model complexity increases so direction = "increasing"
anova(model1, model2, model3, model4,
      direction = "increasing", method = "sequential")

# likelihood ratio tests of the most complex model with the rest of the models
anova(model1, model2, model3, model4,
      direction = "increasing", method = "with.most.complex")

# likelihood ratio tests of the least complex model with the rest of the models
anova(model1, model2, model3, model4,
      direction = "increasing", method = "with.least.complex")