+
grouped_dat <- incidence(
raw_dat,
date_index = "date_of_onset",
@@ -231,9 +213,8 @@
-
-
+#> # ℹ 110 more rows
+
out <- fit_curve(grouped_dat, model = "poisson", alpha = 0.05)
out
#> # A tibble: 6 × 9
@@ -246,13 +227,12 @@
-
-
+#> # prediction_error <list>
+
# plot with a prediction interval but not a confidence interval
plot(out, ci = FALSE, pi=TRUE, angle = 45, border_colour = "white")
-
+
growth_rate(out)
#> # A tibble: 6 × 10
#> count_variable hospital model r r_lower r_upper growth_or_decay time
@@ -267,7 +247,7 @@ Modeling incidenceis_ok()
,
is_warning()
and is_error()
to help filter the
output as necessary.
-
+
out <- fit_curve(grouped_dat, model = "negbin", alpha = 0.05)
is_warning(out)
#> # A tibble: 5 × 7
@@ -278,9 +258,8 @@
-
-unnest(is_warning(out), fitting_warning)
+#> # ℹ 1 more variable: prediction_warning <list>
+unnest(is_warning(out), fitting_warning)
#> # A tibble: 10 × 7
#> count_variable hospital data model estimates fitting_warning
#> <chr> <fct> <list<t> <list> <list> <chr>
@@ -302,7 +281,7 @@
@@ -322,15 +301,15 @@ Rolling average
-
-
+
+
diff --git a/dev/articles/index.html b/dev/articles/index.html
index 5e2b32c..9b1efec 100644
--- a/dev/articles/index.html
+++ b/dev/articles/index.html
@@ -1,53 +1,39 @@
-Articles • i2extras
+Articles • i2extras
Skip to contents
-
-