Regression with categorical variables

Packages for this section

library(tidyverse)
library(broom)

The pigs revisited

  • Recall pig feed data, after we tidied it:
my_url <- "http://ritsokiguess.site/datafiles/pigs2.txt"
pigs <- read_delim(my_url, " ")
pigs

Summaries

pigs %>%
  group_by(feed) %>%
  summarize(n = n(), mean_wt = mean(weight), 
            sd_wt = sd(weight))

Running through aov and lm

  • What happens if we run this through lm rather than aov?
  • Recall aov first:
pigs.1 <- aov(weight ~ feed, data = pigs)
summary(pigs.1)
            Df Sum Sq Mean Sq F value   Pr(>F)    
feed         3   3521  1173.5   119.1 3.72e-11 ***
Residuals   16    158     9.9                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

and now lm

pigs.2 <- lm(weight ~ feed, data = pigs)
summary(pigs.2)

Call:
lm(formula = weight ~ feed, data = pigs)

Residuals:
   Min     1Q Median     3Q    Max 
-3.900 -2.025 -0.570  1.845  5.000 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   60.620      1.404  43.190  < 2e-16 ***
feedfeed2      8.680      1.985   4.373 0.000473 ***
feedfeed3     33.480      1.985  16.867 1.30e-11 ***
feedfeed4     25.620      1.985  12.907 7.11e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.138 on 16 degrees of freedom
Multiple R-squared:  0.9572,    Adjusted R-squared:  0.9491 
F-statistic: 119.1 on 3 and 16 DF,  p-value: 3.72e-11
tidy(pigs.2)
glance(pigs.2)

Understanding those slopes

  • Get one slope for each category of categorical variable feed, except for first.
  • feed1 treated as “baseline”, others measured relative to that.
  • Thus prediction for feed 1 is intercept, 60.62 (mean weight for feed 1).
  • Prediction for feed 2 is 60.62 + 8.68 = 69.30 (mean weight for feed 2).
  • Or, mean weight for feed 2 is 8.68 bigger than for feed 1.
  • Mean weight for feed 3 is 33.48 bigger than for feed 1.
  • Slopes can be negative, if mean for a feed had been smaller than for feed 1.

Reproducing the ANOVA

  • Pass the fitted model object into anova:
anova(pigs.2)
  • Same as before.
  • But no Tukey this way:
TukeyHSD(pigs.2)
Error in UseMethod("TukeyHSD"): no applicable method for 'TukeyHSD' applied to an object of class "lm"

The crickets

  • Male crickets rub their wings together to produce a chirping sound.
  • Rate of chirping, called “pulse rate”, depends on species and possibly on temperature.
  • Sample of crickets of two species’ pulse rates measured; temperature also recorded.
  • Does pulse rate differ for species, especially when temperature accounted for?

The crickets data

Read the data:

my_url <- "http://ritsokiguess.site/datafiles/crickets2.csv"
crickets <- read_csv(my_url)
crickets %>% slice_sample(n = 10) # display sample of rows

Fit model with lm

crickets.1 <- lm(pulse_rate ~ temperature + species, 
                 data = crickets)

Can I remove anything? No:

drop1(crickets.1, test = "F")

drop1 is right thing to use in a regression with categorical (explanatory) variables in it: “can I remove this categorical variable as a whole?”

The summary

summary(crickets.1)

Call:
lm(formula = pulse_rate ~ temperature + species, data = crickets)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0128 -1.1296 -0.3912  0.9650  3.7800 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -7.21091    2.55094  -2.827  0.00858 ** 
temperature     3.60275    0.09729  37.032  < 2e-16 ***
speciesniveus -10.06529    0.73526 -13.689 6.27e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.786 on 28 degrees of freedom
Multiple R-squared:  0.9896,    Adjusted R-squared:  0.9888 
F-statistic:  1331 on 2 and 28 DF,  p-value: < 2.2e-16

Conclusions

  • Slope for temperature says that increasing temperature by 1 degree increases pulse rate by 3.6 (same for both species)
  • Slope for speciesniveus says that pulse rate for niveus about 10 lower than that for exclamationis at same temperature (latter species is baseline).
  • R-squared of almost 0.99 is very high, so that the prediction of pulse rate from species and temperature is very good.

To end with a graph

  • Two quantitative variables and one categorical: scatterplot with categories distinguished by colour.
  • This graph seems to need a title, which I define first.
t1 <- "Pulse rate against temperature for two species of crickets"
t2 <- "Temperature in degrees Celsius"
ggplot(crickets, aes(x = temperature, y = pulse_rate,
  colour = species)) +
  geom_point() + geom_smooth(method = "lm", se = FALSE) +
  ggtitle(t1, subtitle = t2) -> g

The graph

g