nobs import

NAMESPACE

@@ -142,6 +142,7 @@ importFrom(stats,coef)
 importFrom(stats,complete.cases)
 importFrom(stats,logLik)
 importFrom(stats,model.matrix)
+importFrom(stats,nobs)
 importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)

R/bootstrap.R

@@ -96,8 +96,7 @@ bootstrap_coefs.nhmm <- function(model, B = 1000,
   if (method == "nonparametric") {
     for (i in seq_len(B)) {
       mod <- bootstrap_model(model)
-      fit <- fit_nhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, 
-                      ...)
+      fit <- fit_nhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, ...)
       fit$gammas <- permute_states(fit$gammas, gammas_mle)
       gamma_pi[[i]] <- fit$gammas$pi
       gamma_A[[i]] <- fit$gammas$A
@@ -119,8 +118,7 @@ bootstrap_coefs.nhmm <- function(model, B = 1000,
       mod <- simulate_nhmm(
         N, T_, M, S, formula_pi, formula_A, formula_B,
         data = d, time, id, init)$model
-      fit <- fit_nhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, 
-                      ...)
+      fit <- fit_nhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, ...)
       fit$gammas <- permute_states(fit$gammas, gammas_mle)
       gamma_pi[[i]] <- fit$gammas$pi
       gamma_A[[i]] <- fit$gammas$A
@@ -154,8 +152,7 @@ bootstrap_coefs.mnhmm <- function(model, B = 1000,
   if (method == "nonparametric") {
     for (i in seq_len(B)) {
       mod <- bootstrap_model(model)
-      fit <- fit_mnhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, 
-                       ...)
+      fit <- fit_mnhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, ...)
       fit <- permute_clusters(fit, pcp_mle)
       for (j in seq_len(D)) {
         out <- permute_states(
@@ -188,8 +185,7 @@ bootstrap_coefs.mnhmm <- function(model, B = 1000,
       mod <- simulate_mnhmm(
         N, T_, M, S, D, formula_pi, formula_A, formula_B, formula_omega,
         data = d, time, id, init)$model
-      fit <- fit_mnhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, 
-                       ...)
+      fit <- fit_mnhmm(mod, init, init_sd = 0, restarts = 0, threads = 1, ...)
       fit <- permute_clusters(fit, pcp_mle)
       for (j in seq_len(D)) {
         out <- permute_states(

R/nobs.R

@@ -1,16 +1,28 @@
-#'@export
+#' Number of Observations in Hidden Markov Model
+#'
+#' Extract the number of non-missing observations of HMM. When computing nobs 
+#' for a multichannel model with $C$ channels, each observed value in a single 
+#' channel amounts to $1/C$ observation, i.e. a fully observed time point for 
+#' a single sequence amounts to one observation.
+#' @param object An object of class `hmm`, `mhmm`, `nhmm`, or `mnhmm`.
+#' @param ... Ignored.
+#' @rdname nobs
+#' @export
 nobs.hmm <- function(object, ...) {
   attr(object, "nobs")
 }
-#'@export
+#' @rdname nobs
+#' @export
 nobs.mhmm <- function(object, ...) {
   attr(object, "nobs")
 }
-#'@export
+#' @rdname nobs
+#' @export
 nobs.nhmm <- function(object, ...) {
   attr(object, "nobs")
 }
-#'@export
+#' @rdname nobs
+#' @export
 nobs.mnhmm <- function(object, ...) {
   attr(object, "nobs")
 }

R/seqHMM-package.R

@@ -23,7 +23,7 @@
 #' @import nloptr
 #' @import Rcpp
 #' @importFrom Rcpp loadModule evalCpp
-#' @importFrom stats logLik complete.cases model.matrix BIC rnorm runif vcov predict update coef qnorm quantile
+#' @importFrom stats logLik complete.cases model.matrix BIC rnorm runif vcov predict update coef qnorm quantile nobs
 #' @importFrom TraMineR alphabet seqstatf seqdef seqlegend
 #' @importFrom graphics par plot.new
 #' @importFrom methods hasArg