From 666dff7cafcd3531f99aaac383cef54be42f7b9d Mon Sep 17 00:00:00 2001 From: Harrison Curtis Date: Fri, 12 Jul 2024 12:08:24 +0100 Subject: [PATCH] README edits for GAM model addition --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 4209733..419917a 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,7 @@ This a simple example of an ITS analysis similar to that applied within the Caus Of course, the assumption of linearity might be potentially a strong one. However, the seasonally adjusted data shows the general overall increase in the number of cattle slaughtered through time that coincides with generally increased size and productivity of the cattle industry within Australia through time. ![x](https://github.com/HPCurtis/causalcovidcattle/blob/main/img/timeseries.png?raw=true) -Fig 1: Overall timeseries for ABS data on cattle salughter numbers pre and post COVID lockdown date. +Fig 1: Overall timeseries for ABS data on cattle slaughter numbers pre and post COVID lockdown date. ## Forecasts ``` # Generate forecasts for the post-covid period. @@ -101,10 +101,10 @@ lost_revenue_lower = totallower * cattle_levy ``` ### Generalised additive modelling -The following plots are the result of using the MGCV package and associated methods to fit a additive model to avoid repetition not all of the code is not provided in detail here. See, the R directory within the repository for that. But much of it is just variation of the same general workflow. +The following plots are the result of using the MGCV package and associated methods to fit a additive model. To avoid repetition not all of the code is provided in detail here. See, the R directory within the repository for that. But much of it is just variation of the same general workflow. ``` -# Fit of genralied additive model smoother on timeseries data denoted s(t) using mgcv. +# Fit of generalied additive model smoother on timeseries data denoted s(t) using mgcv. fitgam <- gam(TotalState ~ s(t), method = "REML", data = df_sapre) ``` @@ -127,7 +127,7 @@ Table 1. Causal estimates of impact on the total number on cattle slaughtered po - Beef production equated to 20% of the Australian farm production a few year before Covid (2016-2017). Also, the beef industry was valued at $12.7 billion (Australian dollars) to the Australian economy - Tax levy -Within Australia the sale of each head cattle their is a $5 (Aus) dollar levy tax on each head of cattle sold. The model estimates here suggest a Covid-19 cost to the Australian goverment anywhere from 13,316,392 to 60,767,606 in lost tax revenue. +Within Australia the sale of each head cattle their is a $5 (Aus) dollar levy tax on each head of cattle sold. The model estimates here suggest a Covid-19 cost to the Australian goverment anywhere from 13,316,392 to 60,767,606 (based linear model) or 13,535,822 to 41,725,400 (GAM model) in lost tax revenue. ## References