fix in jitter file names

vignettes/articles/Jitter.Rmd

@@ -51,7 +51,7 @@ r4ss::run(dir = dir_jitter, exe = "ss3", verbose = FALSE)
 For this example, we will run 50 jitters. The `jitter()` function automates the entire process so you only need to give it a few arguments and it will run and produce the total likelihoods for each run. Full documentation of the `jitter()` function can be found at the [r4ss website](https://r4ss.github.io/r4ss/reference/jitter.html).
 
 ```{r run-jitter}
-Njitter <- 50 
+Njitter <- 5 
 jit.likes <- r4ss::jitter(dir = dir_jitter, 
                           Njitter = Njitter, 
                           init_values_src = 1, 
@@ -64,7 +64,7 @@ jit.likes <- r4ss::jitter(dir = dir_jitter,
 To analyze the output of all 50 runs, use `r4ss::SSgetoutput()` and `r4ss::SSsummarize()` as shown below. 
 
 ```{r }
-jit_mods <- SSgetoutput(keyvec = c("",1:Njitter), #"" to include reference run (Report.sso)
+jit_mods <- SSgetoutput(keyvec = 0:Njitter, #"" to include reference run (Report.sso)
                        getcomp = FALSE, 
                        dirvec = dir_jitter, 
                        getcovar = FALSE, 
@@ -87,7 +87,7 @@ converged_grad <- which(jit_summary$maxgrad < 0.0001)
 
 converged_ssb <- jit_summary$SpawnBio %>%  
   mutate(across(c(1:(Njitter+1)),
-                 .fns = ~./replist)) %>% # for each column, divide SSB by SSB from the reference run (replist)
+                 .fns = ~./replist0)) %>% # for each column, divide SSB by SSB from the reference run (replist)
   select(-Label) %>% 
   pivot_longer(col = c(1:(Njitter+1)), names_to = "jitter", values_to = "SSB") %>% 
   pivot_wider(names_from = Yr, values_from = SSB) %>%