Hello List
I have been asked to analyse some data for a colleague. The design consists of
a two sets of animals
First set of three - one leg is treated and the other is not under two
different conditions (control & overload are the same animals - control leg is
control (!) for treated leg;
Second set of three - one leg is treated and the other is not under two
different conditions (high_fat and high_fat_overload are the same animals with
high_fat being control leg for high_fat_overload).
Ideally I'd like to find differences between the treatments.
bip <- structure(list(group = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 4L, 4L, 4L), .Label = c("control", "overload", "high_fat",
"high_fat_overload"), class = "factor"), variable = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "BiP", class = "factor"),
animal = structure(c(1L, 3L, 5L, 1L, 3L, 5L, 2L, 4L, 6L,
2L, 4L, 6L), .Label = c("rat1_c", "rat1_hf", "rat2_c", "rat2_hf",
"rat3_c", "rat3_hf"), class = "factor"), value = c(404979.65625,
783511.8125, 677277.625, 1576900.375, 1460101.875, 1591022,
581313.75, 992724.1875, 1106941.5, 996600.375, 1101696.5,
1171004.375)), .Names = c("group", "variable", "animal",
"value"), row.names = c(NA, 12L), class = "data.frame")
I chose to analyse this as a mixed effects model with treatment as a fixed
effect and animal as random.
library(lme4)
model1 <- lmer(value~group + (1|animal), data=bip)
summary(model1)
And then compare this to no treatment with:
anova(model1)
>From this I wanted to work out whether 'treatment' was significantly affecting
>BiP levels by calculating the critical value of F for this design. I have 2
>groups of animals and 3 animals per group. My calculation for the degrees of
>freedom for treatment is 4-1=3.
I'm not sure about the degrees of freedom for the denominator though. Since I'm
comparing a model with treatment to one without (i.e. the grand mean) would the
df for my denominator be 6-1=5?
So I'd then have:
qf(0.95,3,5)
for my critical F value?
Best
iain
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