Generalized linear models (GLM)

Introduction to GLM

Until now, we have worked with statistical analysis (anova, lm) that supposed a normal distribution of the response variable.

Normal distribution

Data: Daily maximum temperature in Roskilde between 2015-01-01 and 2015-12-18.

Source: http://www.wunderground.com/history/

temperature <- read_csv("data/temp_roskilde_2015.csv")
temperature <- janitor::clean_names(temperature)
temperature$cet <- as.Date(temperature$cet, format = "%Y-%m-%d")

The data

	cet	max_temperature_c	mean_temperature_c	min_temperature_c	dew_point_c	mean_dew_point_c	min_dewpoint_c
1	16436.00	6.00	4.00	3.00	5.00	4.00	2.00
2	16437.00	9.00	7.00	4.00	7.00	3.00	0.00
3	16438.00	6.00	3.00	2.00	3.00	2.00	0.00
4	16439.00	4.00	2.00	1.00	2.00	-1.00	-3.00
5	16440.00	4.00	2.00	0.00	3.00	2.00	0.00
6	16441.00	3.00	1.00	-1.00	3.00	1.00	0.00
7	16442.00	6.00	3.00	1.00	4.00	2.00	0.00
8	16443.00	7.00	4.00	3.00	6.00	4.00	2.00
9	16444.00	7.00	5.00	3.00	5.00	2.00	1.00
10	16445.00	10.00	7.00	3.00	9.00	3.00	-2.00

ggplot(temperature, aes(x = cet, y = mean_temperature_c)) + 
    geom_point() + geom_smooth(method = "loess") + 
    scale_x_date()