@@ -14,19 +14,21 @@
 14 14 #' An object of class \code{"ols_step_all_possible"} is a data frame containing the 15 15 #' following components: 16 16 #' 17 + #' \item{mindex}{model index} 17 18 #' \item{n}{model number} 18 19 #' \item{predictors}{predictors in the model} 19 20 #' \item{rsquare}{rsquare of the model} 20 21 #' \item{adjr}{adjusted rsquare of the model} 22 + #' \item{rmse}{root mean squared error of the model} 21 23 #' \item{predrsq}{predicted rsquare of the model} 22 24 #' \item{cp}{mallow's Cp} 23 25 #' \item{aic}{akaike information criteria} 24 26 #' \item{sbic}{sawa bayesian information criteria} 25 27 #' \item{sbc}{schwarz bayes information criteria} 26 - #' \item{gmsep}{estimated MSE of prediction, assuming multivariate normality} 27 - #' \item{jp}{final prediction error} 28 - #' \item{pc}{amemiya prediction criteria} 29 - #' \item{sp}{hocking's Sp} 28 + #' \item{msep}{estimated MSE of prediction, assuming multivariate normality} 29 + #' \item{fpe}{final prediction error} 30 + #' \item{apc}{amemiya prediction criteria} 31 + #' \item{hsp}{hocking's Sp} 30 32 #' 31 33 #' @references 32 34 #' Mendenhall William and Sinsich Terry, 2012, A Second Course in Statistics Regression Analysis (7th edition).
@@ -65,6 +67,7 @@
 65 67  predictors = unlist(metrics$preds), 66 68  rsquare = unlist(metrics$rsq), 67 69  adjr = unlist(metrics$adjrsq), 70 +  rmse = unlist(metrics$rmse), 68 71  predrsq = unlist(metrics$predrsq), 69 72  cp = unlist(metrics$cp), 70 73  aic = unlist(metrics$aic), @@ -156,10 +159,10 @@ Loading  156 159 157 160  if (print_plot) { 158 161  marrangeGrob(myplots, nrow = 2, ncol = 2) 159 -  } else { 160 -  return(myplots) 161 162  } 162 163 164 +  return(myplots) 165 + 163 166 } 164 167 165 168 #' All possible regression plot @@ -366,6 +369,7 @@ Loading  366 369  mcount <- 0 367 370  rsq <- list() 368 371  adjrsq <- list() 372 +  sigma <- list() 369 373  predrsq <- list() 370 374  cp <- list() 371 375  aic <- list() @@ -393,6 +397,7 @@ Loading  393 397  lpreds[mcount] <- lp 394 398  rsq[[mcount]] <- out$rsq 395 399  adjrsq[[mcount]] <- out$adjr 400 +  sigma[[mcount]] <- out$sigma 396 401  predrsq[[mcount]] <- ols_pred_rsq(out$model) 397 402  cp[[mcount]] <- ols_mallows_cp(out$model, model) 398 403  aic[[mcount]] <- ols_aic(out\$model)
@@ -408,7 +413,7 @@
 408 413  } 409 414 410 415  result <- list( 411 -  lpreds = lpreds, rsq = rsq, adjrsq = adjrsq, 416 +  lpreds = lpreds, rsq = rsq, adjrsq = adjrsq, rmse = sigma, 412 417  predrsq = predrsq, cp = cp, aic = aic, sbic = sbic, 413 418  sbc = sbc, msep = msep, fpe = fpe, apc = apc, hsp = hsp, 414 419  preds = preds, lc = lc, q = q, t = t, betas = betas
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