Hi, I would like to test the impact of a treatment of some variable using
regression (e.g. lm(var ~ trt + cov)). However I only have four observations
per factor level. Is it still possible to apply a regression with such a small
sample size. I think that i should be difficult to correctly esti
I think you can, but the confidence intervals will be rather large due to
number of samples.
Notice how standard errors change for sample size (per group) from 4 to 30.
> pg <- 4 # pg = per group
> my.df <- data.frame(var = c(rnorm(pg, mean = 3), rnorm(pg, mean = 1),
rnorm(pg, mean = 11), rnorm(p
Thank you very much. If I get it right, the CI get wider, my test has less
power and the probability of getting a significant relation decreases. What
about the significant coefficients, are they reliable?
> Message du 20/10/14 à 11h30
> De : "Roman Luštrik"
> A : "V. Coudrain"
> Copie à :
Hi,
coefficients and their p-values are reliable if your data are OK and you
do know enough about the process that generated them, so you can choose
appropriate model. With 4 points per line, it may be really difficult to
identify bad fit or outliers.
For example: simple linear regression needs
Hi Listers,
I am trying to run a logistic regression to look at the effects of
experiment type and age on the behavior of fish in a choice chamber
experiment.
I am using the glm approach and would like some advice on how or whether to
perform contrasts to work out what levels of Factor1 (Age) and
You are more or less preforming an ANOVA/ANCOVA on your data? As pointed
out earlier, all of the normal theory regression assumptions apply.
Assuming all of those things are satisfied then if you have large
confidence intervals and there are significant differences between groups I
don't see why yo
Dear Andrew,
anova() and summary() test different hypotheses. anova() tests is at least one
level is different from the others. summary() tests if the coefficient is
different from zero.
Multiple comparison of different interaction levels is probably the most
relevant in this case. The easiest
Thank you for this helpful thought. So if I get it correctly it is hopeless to
try testing an interaction, but we neverless may assess if a covariate has an
impact, providing it is the same in all treatments.
> Message du 20/10/14 à 16h46
> De : "Elgin Perry"
> A : v_coudr...@voila.fr
> Copi
Yes, but as I fear, the residuals behave badly as soon as the model get a
little bit more complex (e.g., with two covariables or an interactions). The
scope for performing an ANCOVA is thus very limited. That's why I was thinking
about a potential non-parametric model. But I do not want to artif
Yes, the analysis with a small sample size would be valid (under the assumption
that the model - both fixed and random effects are correctly specified) but at
some point there must be a practical assessment as to the desired precision and
the costs of the consequences of either inadequate estima
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