[R] Path Analysis

[R Help]

That's an interesting idea, I got the same impression from your SEM
appendix to "Companion to applied regression" in the paragraph just
before Section 3.

So I could get the same results if I built the following two models:

mod1 = lm(intent~exposure+benefit+norms+childBarrier+parentBarrier+knowBenefit,data=dat)
mod2 = glm(recuse~intent+norms+exposure+childBarrier+parentBarrier,data=dat,family=binomial(link=logit))

And in the second model only the intent should have a significant coefficient?

When I run those models I get a number of significant findings in the
mod2.  Does that mean that I have mis-specified my model?  If so (and
I think I have), can I postulate that there is a link between each
significant coefficient?

Thanks so much for your input,
Sam Stewart


> summary(mod2)

Call:
glm(formula = recuse ~ intent + norms + exposure + childBarrier +
    parentBarrier, family = binomial(link = logit), data = dat)

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-2.2784  -0.9018   0.5899   0.7686   1.9314

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept)   -2.51269    0.50359  -4.990 6.05e-07 ***
intent         0.59574    0.08345   7.139 9.39e-13 ***
norms          0.23822    0.02991   7.964 1.67e-15 ***
exposure       0.12522    0.08613   1.454 0.145981
childBarrier  -0.31296    0.08693  -3.600 0.000318 ***
parentBarrier -0.23400    0.08676  -2.697 0.006995 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1803.0  on 1479  degrees of freedom
Residual deviance: 1567.8  on 1474  degrees of freedom
  (40 observations deleted due to missingness)
AIC: 1579.8

Number of Fisher Scoring iterations: 4

On Mon, May 24, 2010 at 1:17 PM, John Fox <jfox at mcmaster.ca> wrote:
> Dear sstewart,
>
> The model appears to reflect the path diagram, assuming that you intend to
> allow the exogenous variables to be correlated and want the errors to be
> uncorrelated.
>
> This is one way to model the binary variable reuse. An alternative would be
> to fit the equation for intent by least-squares regression (assuming that
> the relationships are linear, etc.), and the equation of reuse by, e.g.,
> logistic regression (again assuming that the model is correctly specified).
> If you're right that the effects of the exogenous variables are entirely
> mediated by intent, then if you put these variables in the equation for
> reuse, their coefficients should be small.
>
> I hope this helps,
>  John
>
> --------------------------------
> John Fox
> Senator William McMaster
>  Professor of Social Statistics
> Department of Sociology
> McMaster University
> Hamilton, Ontario, Canada
> web: socserv.mcmaster.ca/jfox
>
>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On
>> Behalf Of R Help
>> Sent: May-24-10 11:18 AM
>> To: r-help
>> Subject: [R] Path Analysis
>>
>> Hello list,
>>
>> I'm trying to make sure that I'm performing a path analysis correctly
>> using the sem package.  the figure at
>> http://flame.cs.dal.ca/~sstewart/regressDiag.png has a detailing of
>> the model.
>>
>> The challenge I'm having is that reuse is an indicator (0/1) variable.
>>
>> Here's the code I'm using:
>>
>> corr =
>>
> hetcor(dat[,c('intent','exposure','benefit','norms','childBarrier','parentBa
> r
>> rier','knowBenefit','recuse')],use="pairwise.complete.obs")$correlations
>> modMat = matrix(c(
>>   'exposure -> intent', 'gam11',NA,
>>   'benefit -> intent', 'gam12',NA,
>>   'norms -> intent', 'gam13',NA,
>>   'childBarrier -> intent', 'gam14',NA,
>>   'parentBarrier -> intent', 'gam15',NA,
>>   'knowBenefit -> intent', 'gam16',NA,
>>   'intent<->intent','psi11',NA,
>>   'intent->recuse','gam21',NA,
>>   'recuse<->recuse','psi22',NA),
>>   ncol=3,byrow=T)
>> model4 =
>>
> sem(modMat,corr,N=1520,fixed.x=c('exposure','benefit','norms','childBarrier'
> ,
>> 'parentBarrier','knowBenefit'))
>>
>> Is this correctly modeling my diagram?  I'm not sure if a) I'm dealing
>> with the categorical variable correctly, or b) whether fixed.x is
>> accurately modeling the correlations for me.
>>
>> Any help would be appreciated.  I'm also looking into creating a plot
>> function within R (similar to the path.diagram function, but using R
>> plots).  If I get something useful I'll try and post it back
>>
>> ______________________________________________
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>> PLEASE do read the posting guide
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>
>
>