Basic lm() functions with glm()

Generalized Linear Models in R

Richard Erickson

Instructor

Interacting with model objects

Allow interaction with outputs
Base R functions apply to glm()
Useful shortcuts

Model print

print() usually default

print(poisson_out)

Call:  glm(formula = y ~ x, family = "poisson", data = dat)

Coefficients:
(Intercept)            x  
   -1.43036      0.05815  

Degrees of Freedom: 29 Total (i.e. Null);  28 Residual
Null Deviance:        35.63 
Residual Deviance: 30.92     AIC: 66.02

Model summary

summary() provides more details

summary(poisson_out)

#...
Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6547  -0.9666  -0.7226   0.3830   2.3022

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept) -1.43036    0.59004  -2.424   0.0153 *
x            0.05815    0.02779   2.093   0.0364 *

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 35.627  on 29  degrees of freedom
Residual deviance: 30.918  on 28  degrees of freedom
AIC: 66.024

Number of Fisher Scoring iterations: 5

Tidy output

Tidyverse provides standardized model outputs
tidy() from Broom package

library(broom)
tidy(poisson_out)

         term    estimate  std.error statistic    p.value
1 (Intercept) -1.43035579 0.59003923 -2.424171 0.01534339
2           x  0.05814858 0.02778801  2.092578 0.03638686

Regression coefficients

coef() prints regression coefficients

coef(poisson_out)

(Intercept)           x 
-1.43035579  0.05814858

Confidence intervals

confint() estimates the confidence intervals

confint(poisson_out)

Waiting for profiling to be done...
               2.5 %     97.5 %
(Intercept) -2.725545344 -0.3897748
x            0.005500767  0.1155564

Predictions

predict(model, new_data)
new_data argument:
- Unspecified: predict() returns predictions based on original data used to fit the model.
- Specified: predict() returns predictions for new_data.

Fire injury dataset

Daily civilian injuries
Louisville, KY
Count data, many zeros

Daily fire injuries from Louisville, KY

Let's practice!

Generalized Linear Models in R