fix typos

R/plot.designresult.R

@@ -4,12 +4,12 @@
 #' Plot a one- or two-dimensional graph of the result.
 #'
 #' @param design Specify a design as a list. Can be used to make a 1D plot for a two-dimensional simfun. Set NA for the dimension that should be plotted and set a value for all others. For example: design=list(n=NA,k=9)
-#' @param adderrorbars logical. Plots errorbars in the 1D plot if TRUE. Default is FALSE (also if specified as NULL).
+#' @param adderrorbars logical. Plots error bars in the 1D plot if TRUE. Default is FALSE (also if specified as NULL).
 #' @param addribbon logical. Adds ribbon in the 1D plot if TRUE. Default is TRUE (also if specified as NULL).
 #' @param trim logical. Option to trim the plotting area for the 2D line plot. The trimmed area is the area where the line is plotted. Default is TRUE.
 #' @param type character indicating the type of the 2D plot. Can be 'heat'(default) or 'line'.
 #' @param color.gradient character indicating whether the 2D plot should have a "diverging" color gradient (white-blue-white, default) or a "linear" color gradient (blue-red)
-#' @param color.width numeric. Option for the diverging colormap in the 2D plot. Width of the blue-white colorband.
+#' @param color.width numeric. Option for the diverging color map in the 2D plot. Width of the blue-white color band.
 #' @param x Object of class designresult as created by the find.design function.
 #' @param ... additional arguments to be passed.
 #'

R/plot2d_heat.R

@@ -16,14 +16,14 @@ plot2d_heat <- function(ds,color.width,color.gradient) {
     names(powerfit) <- c("n", "k")
     powerfit$power <- apply(powerfit, 1, fit$fitfun)
 
-    # control colorband width (only for diverging)
+    # control color band width (only for diverging)
     if (color.gradient == "diverging") {
     dist = color.width/2
     toofar <- abs(powerfit$power - final$power) > dist
     powerfit$power[toofar] <- NA
     }
 
-    # control colorband width (only for linear)
+    # control color band width (only for linear)
     if (color.gradient == "linear") {
       dist = .3
       toofar <- abs(powerfit$power - final$power) > dist

README.Rmd

@@ -51,7 +51,7 @@ This is a basic demonstration to the mlpwr package by going through a toy exampl
 The package can be loaded via
 
 ```{r}
-library(mlpwr)
+library(mlpwr) 
 ```
 
 

README.md

@@ -98,7 +98,7 @@ We can perform the search with the above arguments in use.
 ``` r
 ds <- find.design(simfun = simfun_ttest, boundaries = c(100,
     300), power = 0.95, evaluations = 4000)
-#>  Updates: 1, Evaluations: 1000, Time: 0.2 Updates: 2, Evaluations: 1200, Time: 0.3 Updates: 3, Evaluations: 1400, Time: 0.4 Updates: 4, Evaluations: 1600, Time: 0.4 Updates: 5, Evaluations: 1800, Time: 0.5 Updates: 6, Evaluations: 2000, Time: 0.6 Updates: 7, Evaluations: 2200, Time: 0.7 Updates: 8, Evaluations: 2400, Time: 0.8 Updates: 9, Evaluations: 2600, Time: 0.8 Updates: 10, Evaluations: 2800, Time: 0.9 Updates: 11, Evaluations: 3000, Time: 0.9 Updates: 12, Evaluations: 3200, Time: 1 Updates: 13, Evaluations: 3400, Time: 1.1 Updates: 14, Evaluations: 3600, Time: 1.1 Updates: 15, Evaluations: 3800, Time: 1.2 Updates: 16, Evaluations: 4000, Time: 1.3
+#>  Updates: 1, Evaluations: 1000, Time: 0.2 Updates: 2, Evaluations: 1200, Time: 0.2 Updates: 3, Evaluations: 1400, Time: 0.3 Updates: 4, Evaluations: 1600, Time: 0.4 Updates: 5, Evaluations: 1800, Time: 0.5 Updates: 6, Evaluations: 2000, Time: 0.5 Updates: 7, Evaluations: 2200, Time: 0.6 Updates: 8, Evaluations: 2400, Time: 0.6 Updates: 9, Evaluations: 2600, Time: 0.7 Updates: 10, Evaluations: 2800, Time: 0.8 Updates: 11, Evaluations: 3000, Time: 0.8 Updates: 12, Evaluations: 3200, Time: 0.9 Updates: 13, Evaluations: 3400, Time: 0.9 Updates: 14, Evaluations: 3600, Time: 1 Updates: 15, Evaluations: 3800, Time: 1.1 Updates: 16, Evaluations: 4000, Time: 1.1
 ```
 
 While it is running, the function gives us some updates regarding the
@@ -117,7 +117,7 @@ summary(ds)
 #> Design: N = 201
 #> 
 #> Power: 0.95064,  SE: 0.00379
-#> Evaluations: 4000,  Time: 1.47,  Updates: 16
+#> Evaluations: 4000,  Time: 1.23,  Updates: 16
 #> Surrogate: Logistic regression
 ```
 

vignettes/extensions.Rmd

@@ -156,7 +156,7 @@ aggregate_fun(a)
 ```
 
 
-# Inhomogenous Cost Functions
+# Inhomogeneous Cost Functions
 
 Some uses cases feature more complex cost functions. We cover an example here. It builds on the "ANOVA" simulation function in the "simulation_functions" vignette.