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small changes in the Rcode #124

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25 changes: 8 additions & 17 deletions knitr/lotka-volterra/lotka-volterra-predator-prey.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ output:

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(reshape)
library(reshape2)
library(ggplot2)
library(rstan)
rstan_options(auto_write = TRUE)
Expand Down Expand Up @@ -55,18 +55,14 @@ head(lynx_hare_df, n = 3)
The number of pelts taken by the Hudson Bay Company is shown over time as follows (first, the data is melted using the reshape package, then plotted by species using ggplot).

```{r}
lynx_hare_melted_df <- melt(as.matrix(lynx_hare_df[, 2:3]))
colnames(lynx_hare_melted_df) <- c("year", "species", "pelts")
lynx_hare_melted_df$year <-
lynx_hare_melted_df$year +
rep(1899, length(lynx_hare_melted_df$year))
lynx_hare_melted_df <- melt(lynx_hare_df,id.vars = 'Year',variable.name = 'species',value.name = 'pelts')
head(lynx_hare_melted_df, n=3)
tail(lynx_hare_melted_df, n=3)
```
```{r}
population_plot2 <-
ggplot(data = lynx_hare_melted_df,
aes(x = year, y = pelts, color = species)) +
aes(x = Year, y = pelts, color = species)) +
geom_line() +
geom_point() +
ylab("pelts (thousands)")
Expand Down Expand Up @@ -125,6 +121,7 @@ $$
$$
As usual in writing differential equations, $u(t)$ and $v(t)$ are rendered as $u$ and $v$ to simplify notation.

[This](https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations#Physical_meaning_of_the_equations) wiki page provide some explanations of the (ecological) meaning of the four parameters.

## Error model: measurement and unexplained variation

Expand Down Expand Up @@ -400,34 +397,28 @@ for (k in 1:2) {
}
}

lynx_hare_melted_df <- melt(as.matrix(lynx_hare_df[, 2:3]))
colnames(lynx_hare_melted_df) <- c("year", "species", "pelts")
lynx_hare_melted_df$year <-
lynx_hare_melted_df$year +
rep(1899, length(lynx_hare_melted_df$year))

Nmelt <- dim(lynx_hare_melted_df)[1]
lynx_hare_observe_df <- lynx_hare_melted_df
lynx_hare_observe_df$source <- rep("measurement", Nmelt)

lynx_hare_predict_df <-
data.frame(year = rep(1900:1920, 2),
data.frame(Year = rep(1900:1920, 2),
species = c(rep("Lynx", 21), rep("Hare", 21)),
pelts = c(predicted_pelts[, 2],
predicted_pelts[, 1]),
min_pelts = c(min_pelts[, 2], min_pelts[, 1]),
max_pelts = c(max_pelts[, 2], max_pelts[, 1]),
source = rep("prediction", 42))

lynx_hare_observe_df$min_pelts = lynx_hare_predict_df$min_pelts
lynx_hare_observe_df$max_pelts = lynx_hare_predict_df$max_pelts
lynx_hare_observe_df$min_pelts <- lynx_hare_predict_df$min_pelts
lynx_hare_observe_df$max_pelts <- lynx_hare_predict_df$max_pelts

lynx_hare_observe_predict_df <-
rbind(lynx_hare_observe_df, lynx_hare_predict_df)

population_plot2 <-
ggplot(data = lynx_hare_observe_predict_df,
aes(x = year, y = pelts, color = source)) +
aes(x = Year, y = pelts, color = source)) +
facet_wrap( ~ species, ncol = 1) +
geom_ribbon(aes(ymin = min_pelts, ymax = max_pelts),
colour = NA, fill = "black", alpha = 0.1) +
Expand Down