Fix minor errors

R/mc_fast_forward.R

@@ -8,6 +8,7 @@
 #' @param scope a vector specyfing the covariate to be tested.
 #' @param interaction Maximum number of covariates interacting.
 #' @param penalty penalty term (default = 2).
+#' @param n_max Maximum number of models to be fitted.
 #' @return The selected model.
 #' @export
 
@@ -18,7 +19,6 @@ mc_fast_forward <- function(object, scope, interaction = 1,
     for (i in 2:interaction) {
       int_terms[[c(i-1)]] <- combn(length(scope), i)
     }
-  }
     fun_temp <- function(int_terms) {
       output <- c()
       for(i in 1:dim(int_terms)[2]) {
@@ -26,8 +26,10 @@ mc_fast_forward <- function(object, scope, interaction = 1,
         }
       return(output)
       }
-  scope <- c(scope, do.call(c, lapply(int_terms, fun_temp)))
+  scope <- do.call(c, lapply(int_terms, fun_temp))
+  }
   for (i in 1:n_max) {
+    if(length(scope) == 0) break
     sic <- mc_sic(object = object, scope = scope,
                   data = data, response = 1)
     if (all(sic$SIC < 0)) break
@@ -35,11 +37,13 @@ mc_fast_forward <- function(object, scope, interaction = 1,
     cov_enter <- scope[as.numeric(rownames(sic[which.max(sic$SIC),]))]
     next_cov <- paste("~. +", cov_enter)
     new_formula <- update.formula(object$linear_pred[[1]], next_cov)
-  temp_models <- mcglm(c(new_formula), matrix_pred = matrix_pred,
+    temp_models <- mcglm(c(new_formula),
+                       matrix_pred = object$matrix_pred,
                        link = object$link, variance = object$variance,
                        covariance = object$covariance, data = data)
     object <- temp_models
     scope <- scope[-cov_new]
+    cat(paste("Iteration", i), "\n")
   }
   return(object)
 }

man/mc_fast_forward.Rd

@@ -15,6 +15,8 @@ mc_fast_forward(object, scope, interaction = 1, penalty = 2, n_max = 10)
 \item{interaction}{Maximum number of covariates interacting.}
 
 \item{penalty}{penalty term (default = 2).}
+
+\item{n_max}{Maximum number of models to be fitted.}
 }
 \value{
 The selected model.