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Update vignettes
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vignettes/articles/enteric_fever_example.Rmd

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@@ -234,7 +234,7 @@ Let's compare estimates across all countries and by age group.
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```{r estbycountry}
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#Using est.incidence.by (strata)
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est_country_age <- estimate_scr_by(
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est_country_age <- est_seroincidence_by(
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strata = c("Country", "ageCat"),
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pop_data = xs_data,
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curve_params = curves,

vignettes/articles/scrubTyphus_example.Rmd

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@@ -194,7 +194,7 @@ Now we are ready to begin estimating seroincidence. We will use `est.incidence.b
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```{r estby}
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# Using est.incidence.by (strata)
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est <- estimate_scr_by(
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est <- est_seroincidence_by(
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strata = c("country"),
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pop_data = xs_data,
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curve_params = curves,
@@ -214,7 +214,7 @@ Now we are ready to begin estimating seroincidence. We will use `est.incidence.b
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```{r estby2}
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# Using est.incidence.by (strata)
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est2 <- estimate_scr_by(
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est2 <- est_seroincidence_by(
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strata = c("country", "ageQ"),
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pop_data = xs_data,
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curve_params = curves,

vignettes/articles/simulate_xsectionalData.qmd

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@@ -351,7 +351,7 @@ sim_df |>
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```{r, "est-by-stratum"}
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ests <-
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estimate_scr_by(
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est_seroincidence_by(
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pop_data = sim_df,
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curve_params = dmcmc,
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noise_params = cond,
@@ -421,9 +421,9 @@ ests_summary |>
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Solutions to `nlm()` exit codes 3-5:
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* 3: decrease the `stepmin` argument to `estimate_scr()`/`estimate_scr_by()`
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* 4: increase the `iterlim` argument to `estimate_scr()`/`estimate_scr_by()`
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* 5: increase the `stepmax` argument to `estimate_scr()`/`estimate_scr_by()`
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* 3: decrease the `stepmin` argument to `estimate_scr()`/`est_seroincidence_by()`
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* 4: increase the `iterlim` argument to `estimate_scr()`/`est_seroincidence_by()`
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* 5: increase the `stepmax` argument to `estimate_scr()`/`est_seroincidence_by()`
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We can extract the indices of problematic strata, if there are any:
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@@ -491,7 +491,7 @@ sim_df_renew <-
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)
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ests_renew <-
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estimate_scr_by(
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est_seroincidence_by(
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pop_data = sim_df_renew,
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curve_params = dmcmc,
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noise_params = cond,

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