Longitudinal Data Analysis in Health Research, STAT 437/837, Winter 2014 (Term 1141)
Instructor:
Joel Dubin, [email protected], ext. 37318
Lecture schedule:
Office hours for Joel:
T,TH 1:00 2:20PM, in QNC 1507
T 3:45 5:15PM, in M3 4218 (starting Tues.,
Question 1:
Difference 1: Both figures are time plots of log(FEV/heights) verses age in years. In
Figure 3.7 joint line segments connect each individuals data points and therefore the
whole picture consists of all individuals zigzag trajectories. In Figur
# eda-lecture.R: updated code for eda lecture for STAT 437/837, winter 2014
#
# last edited: 01-15-14 (update of eda-using-R lecture (lecture #3) on 01-14-14)
# read in dataset using read.csv:
# code that will work on Windows or Mac for reading in an Exce
# glmm-gee-in-R-lecture.R: code for introduction to glmm (using lme4 package) and gee (using
# gee and geepack packages)
# in R for STAT 437/837, winter 2014 (02-27-14 lecture (#14)
#
# last edited: 02-26-14
library(lme4) # needed for glmer function
lib
# lme-introduce-lecture.R: code for introduction to lme function
# for STAT 437/837, winter 2014 (01-21-14 lecture (#5)
#
# last edited: 01-20-14
library(nlme)
rats.lda = read.csv(file='rats-lda.csv')
# assume rats.lda dataframe has already been created