Hi,

I am working on my dissertation currently, and have been using the lme4
package to analyze the data. I have been able to use the lmer command to
generate models just fine, but have been unable to find a way to get the
predicted mean values for the fixed effects.

A bit of background about the data:
Participants were given 16 messages to read. Each message can be described
as being:
- Self-disclosure or gossip (message type)
- Positive or negative in content (valence)
- Low or high in terms of the intimacy of the content (intimacy)
- Sent to one person or everyone (Audience)

The participants were assigned to one of two conditions, which was simply
the counterbalancing variable.

Thus, I have five fixed effects: Message type, valence, intimacy, audience,
and condition. In addition, I have two random effects, i.e., the subject
and the specific message (1 - 16).

The model I finally arrived at is:

model10 <- lmer(Sender_Eval ~ SD_GO*Pos_Neg*Low_Hi*One_All*Condition + (1 +
SD_GO + Pos_Neg + Low_Hi |subject) + (1 |MessageNum))

So how do I go about getting the predicted means and standard deviations
for the various effects? I am interested in the means for the "main
effects" of each of the fixed variables, as well as the higher order
interactions.

I have tried every method that I came across online (including using
packages like MixMod, the plotLMER.fnc, etc.) but nothing seems to give me
what I need.

Please help!

Sincerely,
Ishani

Doctoral Candidate | Social Psychology
Department of Psychological and Brain Sciences
Indiana University, Bloomington

	[[alternative HTML version deleted]]

