1. (& Generalized Linear Models)
Nathaniel MacHardy
Fall 2014

2. Why care about data management?
Everything in R is technically data
“classic” data: tables
Model Results
Functions

3. Cooking with Data Simple recipes we’ll learn today
Subset according to some rule
Code a variable into simpler categories
Merge data on a key value (ID)
Recode particular values of a variable

4. Recipe 1: Subset object.to.subset[subset instructions]
By index vector[ c(1,2,3) ]
data [ 2, 4 ]
data [ 3, ]
data [ , 2 ]
By name cars[ c(“speed”,”dist”) ]
By logical (1:3)[ c(TRUE,FALSE,TRUE) ]
By expression cars$dist[ cars$speed== 7]

5. Operators: Generate TRUE/FALSE
Feed these into square brackets!
foods %in% c(“pizza”,”pie”)
age > 18
eyecolor == “red”

6. Basic Functions: Combine/Transform
c() combine
data.frame() bind into data frame
rbind() stack data
merge() merge data by key
t() swap rows/columns

7. Functions II: Shortcuts
# the long way to classify
cat <- rep(NA,length(cat)) # empty vector
cat[values<30] <- “high”
cat[values>=30 & values<70] <- “medium”
cat[values>=70] <- “high”
table(cat)
high low medium

8. Recipe 2: Classifying Data with Cut
cat <- cut(values,c(0,30,70,100))
table(cat)
(0,30] (30,70] (70,100]
cat <- cut(values,c(0,30,70,100),
labels=c(“low”,”med”,”high”))
# if desired

9. Recipe 3: Merge Data health <- data.frame(
id=c(“a”,”b”,”c”), age=c(33,56,13) )
out <- data.frame(
id=c(“b”,”c”,”a”), outcome=c(0,0,1))
merged <- merge(health,out,by=“id”)

10. Recipe 4: Replacing Data
What if you want to recode part of an object?
Easy: subset the “destination” of <-
x <- c(1,NA,0,NA,3,NA,4,6) # replace NA
x[is.na(x)] <- 0
y <- c(1,1,4,10,99,100) # clip range
y[y>10] <- 10

11. Part II: Generalized Linear Models

12. Why care about Generalized Linear Models (GLMs)?
Almost all Epi models are GLMs
Odds ratios (logistic)
Risk ratios (log-binomial)
“Fancy” models are usually just extensions of GLMs
GWR (geographically weighted regression) adds a “weights matrix”
MCMC (Markov Chain Monte Carlo) solves GLMs with priors

13. Basic Parts of GLMs Y: Outcome, Dependent Variable
X: Predictor(s), Covariates, Exposure(s), Independent Variable(s)
Y = mX + b

14. Specifying a model in R A “formula” with Outcome on left
Exposures on right
Y ~ X

15. Example: Using glm() glm(uptake ~ Type) data(CO2); attach(CO2)
Call: glm(formula = uptake ~ Type)
Coefficients:
(Intercept) TypeMississippi
Degrees of Freedom: 83 Total (i.e. Null); 82 Residual
Null Deviance:
Residual Deviance: AIC: 607.6

16. Example: Using glm() glm(uptake ~ type + conc) data(CO2); attach(CO2)
Call: glm(formula = uptake ~ Type + conc)
Coefficients:
(Intercept) TypeMississippi conc
Degrees of Freedom: 83 Total (i.e. Null); 81 Residual
Null Deviance:
Residual Deviance: AIC: 572.1

17. More info: glm() object
m1 <- glm(uptake ~ type + conc)
summary(m1)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(intercept) < 2e-16 ***
Mississippi e-12 ***
conc e-09 ***
Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

18. More info: glm() object
> confint(m1)
Waiting for profiling to be done...
2.5 % %
(Intercept)
TypeMississippi
conc

19. Getting Epidemiological Measures
Specify the family argument
glm(y~x) # default linear, risk difference
glm(y~x, family=binomial(“logit”) # odds ratio
For odds ratio and risk ratio, make sure to exponentiate the output with exp() first
R gives you the beta coefficient

20. Lab: Practice with GLMs
You'll learn more about model-building in courses
What goes in the model (confounders)
Choosing a “best” model
Practice data management
Preparing data for analyses
Formatting/extracting results