**difference between fixed effect model and random effect model**Hi,
What is the difference between a fixed effect model and random effect model
when the data have a multilevel structure, individuals within countres (2 level for example)
if I have Y as a continuous depedent variable and X1 a categorical variable representing countries (10 countries for example), and X2,X3 are 2 continuous variables
What will be the difference between Model1 (considering X1 as a set of dummy variables )
Model1: Y= X1 +X2+X3 +X1*X2+X1*X2 + e
e: individuals residulas
Model 2: Y= X2+X3 +v1*X2+w1*X2 + u1+e
u1,v1 and w1 country residual (random intercept a...

**Re: difference between fixed effect model and random effect model**>>> "adel F." <adel_tangi@YAHOO.FR> 5/18/2006 12:58 pm >>> wrote
<<<
What is the difference between a fixed effect model and random effect
model when the data have a multilevel structure, individuals within
countres (2 level for example) if I have Y as a continuous depedent
variable and X1 a categorical variable representing countries (10
countries for example), and X2,X3 are 2 continuous variables
What will be the difference between Model1 (considering X1 as a set
of dummy variables )
Model1: Y= X1 +X2+X3 +X1*X2+X1*X2 + e
e: individuals ...

**Re: difference between fixed effect model and random effect model #3**adel_tangi@YAHOO.FR wrote back:
>Thanks to all
> Yes Peter
> w1 and v1 are the terms for the interactions (random slopes) and u1 is
>the term for the random intercept
>
> my understanding is if we have individuals within clusters, the first
>model is not valid, because it doe not take into account the homogeneity of
>the individuals within a cluster and consider them as independents (when
>performing the estimation procedure for the parameters )
>
> The second model take into account the association between individual
>within a cluster and provides ...

**Re: difference between fixed effect model and random effect model #2**Thanks to all
Yes Peter
w1 and v1 are the terms for the interactions (random slopes) and u1 is the term for the random intercept
my understanding is if we have individuals within clusters, the first model is not valid, because it doe not take into account the homogeneity of the individuals within a cluster and consider them as independents (when performing the estimation procedure for the parameters )
The second model take into account the association between individual within a cluster and provides the within cluster and between clusters variation
How much variation in the depen...

**probit-probit and random effects**Hi;
Does anyone know if it is accurate to use predicted values with
Bayesian error terms in probit-probit models estimated with random
effect?
think you
...

**main effect model and interaction model**Hi,
Variable "age" was significant in multiple hierarchical regression for
main effect model. So, I thought age was important for posttraumatic
stress disorder (PTSD).
However, age was significant in multiple hierarchical regression for
interaction effect model.
I mean
1st step: traumatic experience --> significant
2nd step: age --> significant
3rd step: traumatic experience * age --> significant
In this case, how can I interpret this result of age? Does the
existence of interaction effect suggest that the data are more
supportive of the interaction m...

**Interaction Effect Without One of the Main Effects in Mixed Models**Hi,
I am using a mixed model to determine if 2 independent variables (A, B) are
interacting with each other. I am using proc mixed because I have to adjust
for clustered data.
When I include A, B, and A*B separately in 3 different model, they all are
significant (p<0.05). However, when I include all 3 of them in the same
model, only one (B) is highly signifcant. When I include, A and A*B in the
model, then both A and A*B become highly significant. Does anyone know what
this means? I think it might be a multicollinearity problem.
Are there any instances where you can include just one...

**random effect regression model**I tried to look for a function in matlab to perform
random effect regression model. Or with an example in matlab using
other functions would be great.
If anyone knows such function, please let me know.
Thanks in advance.
paul wrote:
> I tried to look for a function in matlab to perform
> random effect regression model. Or with an example in matlab using
> other functions would be great.
>
> If anyone knows such function, please let me know.
>
> Thanks in advance.
STEPWISEFIT allows the specification of a fixed subset
of predictors.
You can randomly choose an additiona...

**proc MODEL and RANDOM EFFECTS**Hello SAS-Users,
has anyone experience with the use of the MODEL procedure in SAS and=
estimation of panel data in a simultaneous equation system (estimated with=
SUR) ?
If so, I would be happy to get a look on the program code.
I am struggling with the calculation of the random effects estimator. I=
need the residual sum of squares divided by the number of observations=
multiplied with number of periods per observations -1.
The SSE (sum of squared errors) given in the output of the model procedure=
for each equation, is it corrected by the number of observations times=
number of per...

**random effect model**I am trying to do the random effect model {on linear regression} on
my dataset. I wonder if Matlab help document has something similar to
it.
Thank you for your suggestion
...

**correlation coefficient in a random effects model**Hi all:
I want to compute a correlation coefficient between the dependent
variable and a fixed effects covariate compensating for the effects of
a random factor. In standard OLS regression this is just the
unstandardized regression coefficent times a scale factor (ratio of the
estimated sd's of both variables) does the same computation apply with
a random effects model or is it more esoteric?
Thanks,
Mark
...

**pdiff versus main model effects**Hello Folks. This post may reflect a lack of statistical knowledge more than
it does of SAS but any help would be appreciated. I am testing an effect of
treatment in an unbalanced randomized block design. My overall main model
treatment effects based on the P-values are rather strong (P<0.0001) but
when I use the pdiff statement to determine differences between the 3
treatments none of them come up significant. In fact most of these p-values
are all rather high (i.e. greater than 0.4). Isn't in unusual to find a main
model effect significant and then nothing in the means comparisons? I
...

**NLMIXED: Ordered Probit with Random Effects**I am trying to use NLMIXED to fit an ordered probit model with random time
effects. The data-set has panel structure with 12,000 individuals and
responses over 7 points in time (variable "year"). The observations have 6
(ordered) response levels (1 to 6) for response variable "rja".
To give it a first try I used a single predictor variable, which has 6
levels, i.e.: I put 6 dummies ("k1" to "k6") into the model. In subsequent
models I want to add continuous predictor variables.
Here is the code:
proc nlmixed data=data6;
parms b1=0 b2=0 b3=0 b4=...

**random effect model for binary outcome**Hi All,
what sas procedure do you use for a random effect model if your
outcome is binary?
thanks
...

**Random effects model: does it use REML?**Hello all,
A question regarding how ANOVAN impliments a random effects model. Does
it use an ordinary least squares (OLS) method, or does it use some form
of maximum likelihood (say, restricted maximum likelihood, REML)
method? I am not able to find much documentation about either
references to publications or otherwise regarding how MATLAB implements
random effects in ANOVAN.
Thanks for the help in advance.
-Madhu.
> A question regarding how ANOVAN impliments a random effects model. Does
> it use an ordinary least squares (OLS) method, or does it use some form
> of maximum likeli...

**Random Effects Model for Ordinal Data**I just talked to SAS Tech Support representative and she claimed that
SAS cannot handle an ordinal response in combination with random
effects. I find this hard to believe!
In my situation I have an ordinal OUTCOME with three categories: I, II,
and III. There are two predictors A and B. Each subject is assessed
on all three measures multiple times, so I would like to treat subject
as a random factor. In other words, run ordinal response regressions
within each patient separately and then aggregate the outcomes across
patients.
>From what I've been able to find, proportional odds m...

**matrix and crossed random effect models**Hello,
I am stucked with a mixed ANOVA problem and hope that someone can help
me:
I have got an matrix of persons with a behaviour shown from A to B and
B to A. I know from each person the gender and would like to do a mixed
model ANOVA looking for gender differences between actors and receivers
as well as for interactions
A B C D ...
A x 2 3 2
B 3 x 4 2
C 1 0 x 2
D 3 1 3 x
....
My data sheet now looks like this:
Actor Receiver Sex(actor) Sex(receiver) Behaviour
A B m f 2
A C m f 3
A D ...

**mixed (fixed and random) effect linear model**Which matlab function can perform mixed (fixed and random) effect
linear regression model?
"paul" <paultoti@xxx.com> wrote in message
news:ef2aa48.-1@webx.raydaftYaTP...
> Which matlab function can perform mixed (fixed and random) effect
> linear regression model?
Paul, the anovan function can handle fixed and random effects in n-way
anova. Until recently there hasn't been a way to include continuous
predictors, but in the release just now available, anovan now supports an
option to treat some of the inputs as continuous rather than categorical.
-- Tom
Are...

**Re: pdiff versus main model effects**Opps I am very sorry for any confusion. I think I should have detailed my
code better. I am running proc MIXED (common slope model) as below. (wt_in
is a covariate).
proc mixed data=UofS;
class PEN GT;
model ADGI = GT wt_in /noint solution ddfm=satterth;
random pen;
LSMEANS GT/pdiff;
run;
Paul Kononoff, PhD
University of New Hampshire
Durham, NH
PH: 603-862-1815
Paul Kononoff wrote:
> Opps I am very sorry for any confusion. I think I should have detailed my
> code better. I am running proc MIXED (co...

**Re: correlation coefficient in a random effects model**> I want to compute a correlation coefficient between the dependent
> variable and a fixed effects covariate compensating for the effects of
> a random factor. In standard OLS regression this is just the
> unstandardized regression coefficent times a scale factor (ratio of the
> estimated sd's of both variables) does the same computation apply with
> a random effects model or is it more esoteric?
Mark
Chapter 7 of Snijders and Bosker's book "Multilevel Analysis" discusses
issues with computing an R-square-like statistic in a model with a random
effect (...

**Random effects hurdle model **Hi,
I have a conceptual question about a random effects hurdle model.
Dataset:
/************************/
Person Group Y
1 1 0
1 2 2
1 3 0
2 1 0
2 2 0
2 3 15
..
..
..
/***********************/
Would one need to account for person random effects on the count (e.g.
Poisson) equation, if most people (>95%) only have a non-zero value
for one level of "Group"? It seems to me that since the count (e.g.
Poisson) equation is based solely on non-zero obser...

**Proc glimmix for a crossed random effects model?**Hello everyone.
I am not sure whether "Proc glimmix" works for a crossed random
effects model. Especially I want to fit nonlinear model(ex. IRT
model), so I will specify random effects for both respondents and
items in IRT model.
Is it possible to fit this kind of model with "Proc glimmix"?
...

**Re: random effect model for binary outcome**GLIMMIX or maybe NLMIXED
Peter
-----Original Message-----
>From: Sassy <AugustinaO@GMAIL.COM>
>Sent: Mar 6, 2009 5:13 PM
>To: SAS-L@LISTSERV.UGA.EDU
>Subject: random effect model for binary outcome
>
>Hi All,
>
>what sas procedure do you use for a random effect model if your
>outcome is binary?
>
>thanks
Peter L. Flom, PhD
Statistical Consultant
www DOT peterflomconsulting DOT com
...

**Mixed Models**Hi All;
I'm running a repeated measures analysis within the mixed models module. The
dependent variable is "temperature", repeated variable is "date", and
subject grouping is "thermometer". I have two fixed factors ("treatment" and
"slope") plus their interaction defined. I've added a random factor
("location") which is nested within one of the fixed factors (slope). I have
3 questions:
1. What is the repeated covariance type and which one should I use? I assume
it has to deal with how the measurements over time ar...