hopit (version 0.11.6)

anova.hopit: Likelihood Ratio Test Tables

Description

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

Usage

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

Value

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

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).

Author

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 ---------------------
# \donttest{
# 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 ---------------------
# \donttest{
# 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")
# }