defm (version 0.2.1.0)

defm_terms: Model specification for DEFM

Description

Model specification for DEFM

Usage

td_ones(m, covar = "")
td_generic(m, mat, covar = "")
td_formula(m, formula, new_name = "")
td_logit_intercept(m, y_indices = as.integer(c()), covar = "")
rule_not_one_to_zero(m, term_indices)
rule_constrain_support(m, term_index, lb, ub)
# S3 method for DEFM
+(e1, e2)

Value

Invisible 0.

Arguments

m

An object of class DEFM.

covar

String. Name of a covariate to use as an interaction for the effect. If equal to "", then no interaction effect. is used. used to weight the term.

mat

Integer matrix. The matrix specifies the type of motif to capture (see details.)

formula

Character scalar (see details).

new_name

Character scalar. Name to be assigned for the new term. if empty, then it builds a name based on the formula.

y_indices

Integer vector with the coordinates to include in the term.

term_indices

Non-negative vector of indices. Indicates which outcomes this rule will apply.

term_index

Non-negative scalar. Which term this rule will apply.

lb, ub

Numeric scalars. Lower and upper bounds.

e1, e2

e1 An object of class DEFM (e1) and a character (e2).

Details

In td_generic, users can specify a particular motif to model. Motifs are represented by cells with values equal to 1, for example, the matrix:

t0:   1 NA NA
t1:   1  1 NA

represents a transition y0 -> (y1, y2). If 0 is a motif of interest, then the matrix should include 0 to mark zero values.

The function td_formula, will take the formula and generate the corresponding input for defm::counter_transition(). Formulas can be specified in the following ways:

• Intercept effect: {...} No transition, only including the current state.

• Transition effect: {...} > {...} Includes current and previous states.

The general notation is [0]y[column id]_[row id]. A preceeding zero means that the value of the cell is considered to be zero. The column id goes between 0 and the number of columns in the array - 1 (so it is indexed from 0,) and the row id goes from 0 to m_order.

Both Intercepts and Transition can interact with covariates. Using either the covar argument or, in the case of formulas, x [Covar name], for example:

• Intercept effect: {...} x Hispanic interacts with the Hispanic covar.

• Transition effect: {...} > {...} x Hispanic Same.

Intercept effects

Intercept effects only involve a single set of curly brackets. Using the 'greater-than' symbol (i.e., <) is only for transition effects. When specifying intercept effects, users can skip the row_id, e.g., y0_0 is equivalent to y0. If the passed row id is different from the Markov order, i.e., row_id != m_order, then the function returns with an error.

Examples:

• "{y0, 0y1}" is equivalent to set a motif with the first element equal to one and the second to zero.

Transition effects

Transition effects can be specified using two sets of curly brackets and an greater-than symbol, i.e., {...} > {...}. The first set of brackets, which we call LHS, can only hold row id that are less than m_order.

The term td_logit_intercept will add what is equivalent to an intercept in a logistic regression. When y_indices is specified, then the function will add one intercept per outcome. These can be weighted by a covariate.

The function rule_not_one_to_zero will avoid the transition one to zero in a Markov process.

The function rule_constrain_support will constrain the support of the model by specifying a lower and upper bound for a given statistic.

The + method is a shortcut for term_formula

Examples

# Loading Valtente's SNS data
data(valentesnsList)

mymodel <- new_defm(
  id    = valentesnsList$id,
  Y     = valentesnsList$Y,
  X     = valentesnsList$X,
  order = 1
)

# Conventional regression intercept
td_logit_intercept(mymodel)

# Interaction effect with Hispanic
td_logit_intercept(mymodel, covar = "Hispanic")

# Transition effect from only y1 to both equal to 1.
td_formula(mymodel, "{y1, 0y2} > {y1, y2}")

# Same but interaction with Female
td_formula(mymodel, "{y1, 0y2} > {y1, y2} x Female")

# Inspecting the model
mymodel

# Initializing and fitting
init_defm(mymodel)
defm_mle(mymodel)