diff --git a/scripts/ce089-09.R b/scripts/ce089-09.R
index 6e680d54b980bfe3d67bfadbfe87780eaf3d484e..535ddf227976c718e5b776e88310f5f7ffe95206 100644
--- a/scripts/ce089-09.R
+++ b/scripts/ce089-09.R
@@ -4,8 +4,6 @@
 #-----------------------------------------------------------------------
 # Cria a função com lista de todos os argumentos e use mapply().
 
-library(microbenchmark)
-
 # Curva de poder.
 power_k_groups <- function(k = 3,
                            r = 4,
@@ -17,37 +15,59 @@ power_k_groups <- function(k = 3,
     results <- replicate(simulations, {
         b <- rnorm(k, mean = 0, sigma_group)
         y <- rnorm(k * r, mean = X %*% b, sd = 1)
-        anova(lm(y ~ X))[1, 5]
+        anova(lm(y ~ trt))[1, 5]
     })
     mean(results <= alpha)
 }
 
 # Criar o grid de condições (prototipar com um grid pequeno).
-grid <- expand.grid(k = 3:5,
-                    r = c(3, 5, 8),
-                    sigma_group = seq(0.01, 0.5, length.out = 10),
+grid <- expand.grid(k = c(3, 5, 8),
+                    r = c(3, 8),
+                    sigma_group = seq(0.01, 2, length.out = 20),
                     KEEP.OUT.ATTRS = FALSE)
 nrow(grid)
+grid
 
 # Aplicar.
 grid$taxa <- mapply(FUN = power_k_groups,
                     k = grid$k,
                     r = grid$r,
                     sigma_group = grid$sigma_group,
-                    MoreArgs = list(simulations = 10,
+                    MoreArgs = list(simulations = 100,
                                     alpha = 0.05),
                     SIMPLIFY = TRUE)
 
+#-----------------------------------------------------------------------
+
+library(latticeExtra)
+
+xyplot(taxa ~ sigma_group | ordered(r),
+       groups = k,
+       auto.key = TRUE,
+       data = grid,
+       type = "o")
+
+apropos("power")
+apropos("write")
+
 #-----------------------------------------------------------------------
 # Usando purrr.
 
 library(purrr)
+ls("package:purrr")
 
+grid$taxa <- NULL
 grid$taxa <- pmap_dbl(.l = grid,
                       .f = power_k_groups,
-                      simulations = 10,
+                      simulations = 100,
                       alpha = 0.05)
 
+xyplot(taxa ~ sigma_group | ordered(r),
+       groups = k,
+       auto.key = TRUE,
+       data = grid,
+       type = "o")
+
 #-----------------------------------------------------------------------
 
 library(microbenchmark)
@@ -69,3 +89,5 @@ microbenchmark(
                  alpha = 0.05)
     },
     replications = 20)
+
+#-----------------------------------------------------------------------