In this section, we will cover everything to do with scatterplots. The main focus of this section is plotting the results of a linear regression and as such most of this will be aimed at lines. In the future, line graphs and scatterplots will be separated to their own “modules”.
For this section, we will be using the tadpoles.csv data set The second dataset we analysed tadpole abundance in different sized ponds using a linear model/regression. Plotting linear regressions is really straightforward, but can be done a couple of different ways, depending on what you wish to accomplish. First, letâ€™s run the basic analysis again (excluding the reeds factor). tadpoles.lm <- lm(abundance ~ pondsize, data = tadpoles) summary(tadpoles.lm) ## ## Call: ## lm(formula = abundance ~ pondsize, data = tadpoles) ## ## Residuals: ## Min 1Q Median 3Q Max ## -73.
To produce a line on our graph, the easiest solution is using geom_smooth(method=lm). geom_smooth() by default will produce a loess smooth through our graph with confidence intervals. Since we have run a linear model, we specify the method of the geometric shape to fit that of a linear model (lm). ggplot(tadpoles, aes(x=pondsize, y=abundance)) + geom_point(alpha = 0.5)+ geom_smooth(method=lm) method=lm tells the smooth line to plot a linear relationship between the variables in the graph environment.
For this section, we will be using the nestpredation.csv data set In our third dataset, we analysed the nest predation dataset using a generalised linear model with a binomial distribution, also known as a Logistic Regression. In this scenario, our data is measuring whether a nest was attacked or not in areas of different shrubcover. When we analyse this using a GLM, it is calculating the probability of a nest being attacked, given different values of shrubcover.