I'm familiar with post-hoc testing with
ANOVA for exploring differences between a sequence of groups, but recently I've been reading about Change Point Analysis (especially the
It looks like those packages only handle data where there is one data point per unit of time. I'm curious if they can be used with data where there are multiple data points per unit of time. Here's some example data representing the measurement of a single continuous variable on a number of specimens that have been dated to specific moments in time (no repeated measurements):
a<-data.frame(time="1000",x=rnorm(10,12,3)) b<-data.frame(time="2000",x=rnorm(50,13,4)) c<-data.frame(time="3500",x=rnorm(50,12,4)) d<-data.frame(time="5000",x=rnorm(7,14,5)) e<-data.frame(time="7000",x=rnorm(20,10,3)) f<-data.frame(time="7500",x=rnorm(15,11,3)) g<-data.frame(time="9000",x=rnorm(15,10,5)) h<-data.frame(time="9500",x=rnorm(35,30,2)) i<-data.frame(time="10000",x=rnorm(30,28,4)) a2i<-rbind(a,b,c,d,e,f,g,h,i) library(ggplot2) a2i$time<-as.numeric(levels(a2i$time))[a2i$time] ggplot(a2i,aes(time,x))+stat_smooth()+geom_point()
Here's what I'd be most grateful for some advice on...
Q1. Would it be valid to do the Change Point Analysis on a vector like the means or medians of the groups? That would allow me to start with a 'one data point per unit of time' input format which would suit the
R packages, as I understand them. I've seen it done with environmental data like monthly gas concentrations (from daily observations), but I thought I'd check.
Q2. Is there a kind of Change Point Analysis that I can do on the raw data in
a2g that will give me some measures of the probabilities of changes across the sequence? For example, something that will detect the change from time=9000 to time=9500, using all the data points in the sample? I'm guessing that if it was possible, someone would already have implemented and I just need a pointer to the relevant function.
Q3. In case Q2 can be answered 'yes', would the method change if the distribution of each group's values was non-normal (unlike my sample data)?
Q4. If Change Point Analysis is completely the wrong approach here, please let me know. I'm basically just curious about methods other than
ANOVA for these kinds of data. Any other suggestions would be most welcome.