Be a bit more precise in the description of standard plots

README.Rmd

@@ -60,7 +60,7 @@ nlmixr2, please see the [nlmixr2 blog](https://blog.nlmixr2.org/).
 
 For all versions of R, we need to have a compiler setup to run `nlmixr2` and `rxode2`
 
-### Windows compilation tools setup 
+### Windows compilation tools setup
 
 For Windows the compilers come from RTools. Download and the install the version
 of RTools for your version of R from
@@ -75,10 +75,10 @@ To setup the mac compilers, simply
 2. Install gfortran:
 
    a. Download and install from https://mac.r-project.org/tools/
-   
+
    b. Add gfortran directory to the path with: `export PATH=$PATH:/usr/local/gfortran/bin`
 
-## R package installation 
+## R package installation
 
 Installation nlmixr2 itself is easiest in R-4.3.x because no further compilation
 is required and all supporting packages are available. From R, run:
@@ -299,8 +299,8 @@ print(fit)
 
 ## Base R Graphics
 
-You can use base plots with the fit and it will produce a standard set of
-goodness of fit plots:
+You can use the built-in `plot` with the fit and it will produce a
+standard set of goodness of fit plots:
 
 ```{r base-r-plot, eval=FALSE}
 pdf(file="myplots.pdf")

README.md

@@ -5,22 +5,29 @@
 
 <!--
 ## CRAN updating
-&#10;[![CRAN-status](https://img.shields.io/badge/CRAN-Updating-red)](https://github.com/nlmixr2/rxode2/actions/workflows/R-CMD-check.yaml)
-&#10;Currently we are updating `PreciseSums` on CRAN; `nlmixr2est` is
+
+[![CRAN-status](https://img.shields.io/badge/CRAN-Updating-red)](https://github.com/nlmixr2/rxode2/actions/workflows/R-CMD-check.yaml)
+
+Currently we are updating `PreciseSums` on CRAN; `nlmixr2est` is
 linked to `rxode2` which in turn is linked to `PreciseSums`.  While
 `PreciseSums`, `rxode2` and `nlmixr2est` are being submitted, the most stable
 version of `nlmixr2` comes from the `r-universe`:
-&#10;```r
+
+```r
 install.packages(c("PreciseSums", "dparser", "nlmixr2data", "lotri",
                    "rxode2ll", "rxode2parse", "rxode2random", "rxode2et",
                    "rxode2",  "nlmixr2est", "nlmixr2extra", "nlmixr2plot",
                    "nlmixr2"),
                  repos = c('ttps://nlmixr2.r-universe.dev',
                            'https://cloud.r-project.org'))
 ```
-&#10;This is temporary and should resolve itself in a couple of weeks.
-&#10;You could also compile from source to work around the issue.
-&#10;-->
+
+This is temporary and should resolve itself in a couple of weeks.
+
+You could also compile from source to work around the issue.
+
+-->
+
 <!-- badges: start -->
 
 ![Cran updating
@@ -63,11 +70,11 @@ To setup the mac compilers, simply
 1.  Install Xcode from app store
 
 2.  Install gfortran:
-
+    
     1.  Download and install from <https://mac.r-project.org/tools/>
-
-    2.  Add gfortran directory to the path with:
-        `export PATH=$PATH:/usr/local/gfortran/bin`
+    
+    2.  Add gfortran directory to the path with: `export
+        PATH=$PATH:/usr/local/gfortran/bin`
 
 ## R package installation
 
@@ -285,6 +292,18 @@ one.compartment <- function() {
 ## The fit is performed by the function nlmixr/nlmixr2 specifying the model, data and estimate
 fit <- nlmixr2(one.compartment, theo_sd,  est="saem", saemControl(print=0))
 #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
+#> 
+#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
+#> 
+#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
+#> 
+#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
+#> 
+#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
+#> 
+#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
+#> 
+#> [====|====|====|====|====|====|====|====|====|====] 0:00:00
 print(fit)
 #> ── nlmixr² SAEM OBJF by FOCEi approximation ──
 #> 
@@ -293,8 +312,8 @@ print(fit)
 #> 
 #> ── Time (sec $time): ──
 #> 
-#>         setup optimize covariance saem table compress other
-#> elapsed 0.006    0.001      0.031 7.44  0.07     0.06 4.822
+#>           setup covariance  saem table compress    other
+#> elapsed 0.00104   0.018005 2.409 0.048    0.018 2.468955
 #> 
 #> ── Population Parameters ($parFixed or $parFixedDf): ──
 #> 
@@ -326,8 +345,8 @@ print(fit)
 
 ## Base R Graphics
 
-You can use base plots with the fit and it will produce a standard set
-of goodness of fit plots:
+You can use the built-in `plot` with the fit and it will produce a
+standard set of goodness of fit plots:
 
 ``` r
 pdf(file="myplots.pdf")