Hi all,
I have a data set that looks a bit like this.
feed1
RFU Site Vial Time lnRFU
1 44448 1 1 10 10.702075
2 47521 1 1 20 10.768927
3 42905 1 1 30 10.66674
4 46867 1 1 40 10.755069
5 42995 1 1 50 10.668839
6 43074 1 1 60 10.670675
7 41195 1 1 70 10.626072
8 47090 1 2 10 10.759816
9 48100 1 2 20 10.781037
10 43215 1 2 30 10.673943
11 39656 1 2 40 10.587998
12 38799 1 2 50 10.566150
13 38424 1 2 60 10.556438
14 35240 1 2 70 10.469937
15 46427 1 3 10 10.745636
16 46418 1 3 20 10.745443
17 42095 1 3 30 10.647684
......
There are 5 columns of data, three levels of "Site", 10 "Vials" per site,
and measurements were taken at 10 min intervals from 10-70.. I am primarily
interested in the relationship between "Time" and "lnRFU" to calculate the
rate at which lnRFU declines over time. I have a nice plot using a ggplot2
code that looks like this
p<-ggplot(data=feed1,aes(x=Time,y=lnRFU))
p+geom_point(size=4)+facet_grid(Site~Vial)+geom_smooth(method="lm")
The graph is useful to visualize the changes over time and grouped by both
Site and Vial, but I also need the slopes of the linear regressions for each
Vial, within a Site. This is where I run into a problem. I want to run a
linear regression of lnRFU as a function of Time grouped by both Site and
Vial. Its easy to visualize this comparison in ggplot using facet_grid(),
but I'm not sure how to do a similar comparison/analysis within lm()
I imagine something like
fit<-lm(lnRFU~Time | Vial * Site, data=feed1)
in which I group by both Vial and Site, but obviously this code doesn't
work. Does anyone have an idea for how to do a linear regression with two
grouping variables? Do I have to go back and combine Vial and Site into a
single grouping variable or can I leave the dataframe the way it is? I'm
trying to imagine a means of accomplishing the same type of thing that
facet_grid does when it allows you to plot the data as a function of two
"grouping" variables.
Thanks for you time. I greatly appreciate it.
Nate Miller
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