Message: 1
Date: Fri, 20 Nov 2009 08:05:24 -0500
From: eva petkova <evap120 at gmail.com>
Subject: Re: [R-sig-ME] estimating contrasts and their standard errors
from mixed effects models in lme4
To: adik at ilovebacon.org
Cc: r-sig-mixed-models at r-project.org
Message-ID:
<754b10c00911200505u378741c5y3d01c5a0c236fa23 at mail.gmail.com>
Content-Type: text/plain
Thank you Adam,
i am actually interested in estimating various contrasts without having
to
re-parametrize the factors in order to estimate different contrasts. in
your example, the parametrization of the effect factor you are using will
estimate the contrasts hea vs.cri or ach vs.cri, but if you wanted to
estimate hea vs. ach, you will have to reparametrize the effect factor
and
refit the model. in your case it it just one more reparametrization and
fitting of the model but with factors that have more levels and
interactions
between them it is quite an elaborate process. so i wondered if there is
a
function that can use the fit from one model and take as an input a
linear
combination of the regression coefficients and estimate the standard
error
of teh linear combination -- something similar to the "estimate"
statement
in Proc Mixed in SAS does.
Thanks again
e
On Thu, Nov 19, 2009 at 9:18 PM, Adam D. I. Kramer
<adik at ilovebacon.org>wrote:
Hi Eva,
If your data frame contains the factor variables you are interested
in analyzing, use the contrasts() function to get and set which contrasts
you would like.
Then, just use the factor variable in your fixed-effects formula.
lmer will automatically use the contrasts you provided, and you will get a
effect line for each contrast (including estimate and standard error).
So, for example, I just completed this analysis and set it to my advisor.
We're looking at the perception of personal risk of an adverse outcome
occurring. There were three adverse outcomes, which I had contrast-coded in
the following manner:
contrasts(h3a2$effect)
heaVcri achVcri
cri 0 0
hea 1 0
ach 0 1
...so when I use the h3a2$effect variable using lmer:
summary( lmer(risk ~ effect*order + income + (1|subj), data=h3a2) )
... I get these fixed effects:
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.92254 0.21431 4.305
effectheaVcri -0.53837 0.24859 -2.166
effectachVcri -0.40662 0.25000 -1.627
order -0.31194 0.07823 -3.987
income -0.08551 0.03618 -2.363
effectheaVcri:order 0.27001 0.11736 2.301
effectachVcri:order 0.19991 0.11764 1.699
...output lines 2 and 3 are the two contrasts (labeled with the name of the
variable and then the name of the contrast: effectheaVcri is really
"effect""heaVcri"), with their estimates, and standard errors. Lines 6 and
7 are the same contrasts' interactions with the "order" variable.
Really, the order effect is what I'm interested in here--order predicted
less risk, meaning that people who rated a given disorder later on in the
experiment thought they were less at risk for the disorder (even after we
control for what the disorder is, so for me the contrasts are more or less
control variables).
Hope this helps!
--Adam
On Thu, 19 Nov 2009, eva petkova wrote:
This must have come up before, but i did not find it in the help archive.
in a mixed effects model fitted with lmer, i have an interaction term
between two factors, each with more than two levels and would like to
estimate and test various contrasts between different combinations of the
factor levels. i need the point estimates and the standard errors for
these
many contrasts. does anyone know if there is a function that calculates
the
standard errors of contrasts?
thank you
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