Automatic readme update

README.md

@@ -86,8 +86,11 @@ with `summary()`. Running `summary()` on the output of
 
 Objects returned from `simulate_chains()` can be aggregated into a
 `<data.frame>` of cases per time or generation with the function
-`aggregate()`. The aggregated results can also be passed on to `plot()`
-with its own arguments to customize the resulting plots.
+`aggregate()`.
+
+The simulated `<epichains>` object can be plotted in various ways using
+`plot()`. See the plotting section in `vignette("epichains")` for two
+use cases.
 
 ### Simulation
 
@@ -96,7 +99,7 @@ we simulate a transmission chain with $20$ index cases, a constant
 generation time of $3$, and a poisson offspring distribution with mean
 $1$. We are tracking the chain “size” statistic and will cap all chain
 sizes at $25$ cases. We will then look at the summary of the simulation,
-and aggregate it into cases per generation, and plot it.
+and aggregate it into cases per generation.
 
 ``` r
 set.seed(32)
@@ -138,14 +141,26 @@ summary(sim_chains)
 ``` r
 
 # Aggregate the simulation into cases per generation
-chains_agrgegated <- aggregate(sim_chains, by = "generation")
-
-# plot the aggregated results
-plot(chains_agrgegated, type = "b")
+chains_agregegated <- aggregate(sim_chains, by = "generation")
+
+# view the time series of cases per generation
+chains_agregegated
+#>    generation cases
+#> 1           1    20
+#> 2           2    26
+#> 3           3    36
+#> 4           4    43
+#> 5           5    31
+#> 6           6    25
+#> 7           7    20
+#> 8           8     9
+#> 9           9     3
+#> 10         10     1
+#> 11         11     1
+#> 12         12     1
+#> 13         13     1
 ```
 
-<img src="man/figures/README-simulate_chains-1.png" width="100%" />
-
 ### Inference
 
 Let’s look at the following example where we estimate the log-likelihood