Always use explicit months (no newmoonnumbers)

lessons/R-complex-time-series-models-1/material.qmd

@@ -8,7 +8,7 @@ order: 1
 
 ### Data
 
-Download the [Desert Pocket Mouse data](/data/pp_abundance_timeseries.csv)
+Download the [Desert Pocket Mouse data](/data/pp_abundance_by_month.csv)
 
 ### Software Installation
 

lessons/R-complex-time-series-models-1/r_tutorial.qmd

@@ -51,7 +51,7 @@ library(dplyr)
 * Data on the population dynamics of the Desert Pocket Mouse
 
 ```r
-pp_data <- read.csv("pp_abundance_timeseries.csv")
+pp_data <- read.csv("pp_abundance_by_month.csv")
 ```
 
 * mvgam doesn't currently work with tsibbles
@@ -60,10 +60,12 @@ pp_data <- read.csv("pp_abundance_timeseries.csv")
 * Helps when analyzing multiple time series at once (e.g., multiple species)
 
 ```r
-pp_data <- read.csv("pp_abundance_timeseries.csv") |>
-  mutate(time = newmoonnumber) |>
-  mutate(series = as.factor('PP')) |>
-  select(time, series, abundance, mintemp, cool_precip)
+pp_data = read.csv("pp_abundance_by_month.csv") |>
+    mutate(month = yearmonth(month)) |>
+    mutate(time = as.numeric(month)) |>
+    mutate(series = as.factor('PP')) |>
+    tibble()
+pp_data
 ```
 
 * 124 months of data

lessons/R-complex-time-series-models-2/material.qmd

@@ -8,7 +8,7 @@ order: 1
 
 ### Data
 
-Download the [Desert Pocket Mouse data](/data/pp_abundance_timeseries.csv)
+Download the [Desert Pocket Mouse data](/data/pp_abundance_by_month.csv)
 
 ### Software Installation
 

lessons/R-complex-time-series-models-2/r_tutorial.qmd

@@ -51,7 +51,7 @@ library(dplyr)
 * Data on the population dynamics of the Desert Pocket Mouse
 
 ```r
-pp_data <- read.csv("pp_abundance_timeseries.csv")
+pp_data <- read.csv("pp_abundance_by_month.csv")
 ```
 
 * mvgam doesn't currently work with tsibbles
@@ -60,10 +60,12 @@ pp_data <- read.csv("pp_abundance_timeseries.csv")
 * Helps when analyzing multiple time series at once (e.g., multiple species)
 
 ```r
-pp_data <- read.csv("pp_abundance_timeseries.csv") |>
-  mutate(time = newmoonnumber) |>
-  mutate(series = as.factor('PP')) |>
-  select(time, series, abundance, mintemp, cool_precip)
+pp_data = read.csv("pp_abundance_by_month.csv") |>
+    mutate(month = yearmonth(month)) |>
+    mutate(time = as.numeric(month)) |>
+    mutate(series = as.factor('PP')) |>
+    tibble()
+pp_data
 ```
 
 * 124 months of data
@@ -91,7 +93,7 @@ $$y_t = c + \beta_1 x_{1,t} + \beta_2 y_{t-1} + \mathcal{N}(0,\sigma^{2})$$
 
 * Which can also be written as
 
-$$y_t = \mathcal{N}(\mu,\sigma^{2})$$
+$$y_t = \mathcal{N}(\mu_t,\sigma^{2})$$
 $$u_t =  c + \beta_1 x_{1,t} + \beta_2 y_{t-1}$$
 
 * Fit using the `mvgam()` function
@@ -157,14 +159,8 @@ plot(baseline_model)
 
 ### Bayesian model forecasting
 
-* So let's look at the forecasts
-* To make forecasts for Bayesian models we include data for `y` that is `NA`
-* This tells the model that we don't know the values and therefore the model estimates them as part of the fitting process
-* Handy because it means these models can handle missing data
-* To make a true forecast one `NA` is added to the end of `y` for each time step we want to forecast
-* To hindcast the values for `y` that are part of the test set are replaced with `NA`
-
-* We can do this automatically in mvgam using the `forecast()` function
+* To make forecasts in mvgam we use the `forecast()` function (just like fable)
+* Use `newdata` (not `new_data` as in fable) to include the test data for the driver forecasts
 
 ```r
 baseline_forecast = forecast(baseline_model, newdata = data_test)
@@ -189,33 +185,23 @@ plot(baseline_forecast)
 
 $$y_t = \mathrm{Pois}(\lambda_t)$$
 
-
 * The Poisson distribution has one parameter $\lambda$
 * Which is both the mean and the variance
 * It generates only integer draws based on a mean
 
 ```r
-rpois(n = 10, lambda = 5)
-hist(rpois(n = 1000, lambda = 5))
+rpois(n = 10, lambda = 4.5)
+hist(rpois(n = 1000, lambda = 4.5))
 ```
 
-* If the mean is 1.5 sometimes you'll draw a 1, sometimes a 2, sometimes a 0, etc.
+* If the mean is 4.5 sometimes you'll draw a 4, sometimes a 5, sometimes a 0 or a 10
 
 * $\lambda_t$ can be a decimal
-
-```r
-hist(rpois(n = 1000, lambda = 4.5))
-```
-
 * We could expect an average of 4.5 rodents based on the environment even though we can only observe an integer number
-* But $\lambda_t$ does have to be positive because we can't reasonably expect to see negative rodents
-
-```r
-hist(rpois(n = 1000, lambda = -4.5))
-```
+* But $\lambda_t$ it does have to be positive because we can't reasonably expect to see negative rodents
 
-* To handle this we use a log link function to give us only positive values of $\mu_t$
-* The log link means that instead of modeling $\mu_t$ directly we model $log(\mu_t)$
+* To handle this we use a log link function to give us only positive values of $\lambda_t$
+* The log link means that instead of modeling $\lambda_t$ directly we model $log(\lambda_t)$
 
 $$y_t = \mathrm{Pois}(\lambda_t)$$
 $$\mathrm{log(\lambda_t)} = c + \beta_1 x_{1,t} + \beta_2 y_{t-1}$$
@@ -326,7 +312,7 @@ plot(poisson_gam_model)
 
 * We can also see that the residual season autocorrelation is improved
 
-## State space models
+## State space models (optional)
 
 * To add an explicit observation model we use the
 
@@ -389,5 +375,5 @@ scores <- score(poisson_gam_forecast, interval_width = 0.5)
 
 ```r
 in_interval = scores$PP$in_interval
-length(in_interval[in_interval == TRUE]) / length(in_interval)
+length(in_interval[in_interval == 1]) / length(in_interval)
 ```

lessons/R-time-series-modeling-2/material.qmd

@@ -5,5 +5,7 @@ order: 1
 
 Before starting lesson:
 
-* Make sure the Portal time series data is available on your computer
-* No new packages need to be installed for this lesson
+```r
+install.packages(c('tsibble', 'fable', 'feasts', 'ggtime'))
+download.file("https://course.naturecast.org/data/portal_timeseries.csv", "portal_timeseries.csv")
+```

lessons/R-time-series-modeling-3/material.qmd

@@ -5,6 +5,7 @@ order: 1
 
 Before starting lesson:
 
-* Make sure the Portal time series data is available on your computer
-* Download the [Desert Pocket Mouse data](/data/pp_abundance_timeseries.csv)
-* No new packages need to be installed for this lesson
+```r
+install.packages(c('tsibble', 'fable', 'feasts', 'ggtime'))
+download.file("https://course.naturecast.org/data/pp_abundance_by_month.csv", "pp_abundance_by_month.csv")
+```

lessons/R-time-series-modeling-3/r_tutorial.qmd

@@ -21,8 +21,9 @@ library(ggtime)
 * Load the data
 
 ```r
-pp_data = read.csv("pp_abundance_timeseries.csv") |>
-  as_tsibble(index = newmoonnumber)
+pp_data = read.csv("pp_abundance_by_month.csv") |>
+  mutate(month = yearmonth(month)) |>
+  as_tsibble(index = month)
 pp_data
 ```
 

lessons/R-time-series-modeling/material.qmd

@@ -5,5 +5,7 @@ order: 1
 
 Before starting lesson:
 
-* Make sure the Portal time series data is available on your computer
-* No new packages need to be installed for this lesson
+```r
+install.packages(c('tsibble', 'fable', 'feasts', 'ggtime'))
+download.file("https://course.naturecast.org/data/portal_timeseries.csv", "portal_timeseries.csv")
+```