Thanks, I suspected it would be something simple.   Best wishes John

On 09/08/2012 14:42, Viechtbauer Wolfgang (STAT)-2 [via R] wrote:
> There is a simple explanation:
>
> 1) The command:
>
> factor(ftype)
>
> does not actually turn 'ftype' permanently into a factor, since you 
> are not re-assigning it back to the object 'ftype'. You have to use:
>
> ftype <- factor(ftype)
>
> 2) If you want to use the formula interface for specifying moderators, 
> you have to use mods = ~ <formula>, so in other words:
>
> rma(KL, VL, mods = ~ ftype)
>
> after you have made 'ftype' a factor (see 1).
>
> Or you can simply use:
>
> rma(KL, VL, mods = ~ factor(ftype))
>
> which does the conversion of 'ftype' into a factor within the model 
> formula.
>
> Best,
>
> Wolfgang
>
> -- 
> Wolfgang Viechtbauer, Ph.D., Statistician
> Department of Psychiatry and Psychology
> School for Mental Health and Neuroscience
> Faculty of Health, Medicine, and Life Sciences
> Maastricht University, P.O. Box 616 (VIJV1)
> 6200 MD Maastricht, The Netherlands
> +31 (43) 388-4170 | http://www.wvbauer.com
>
> > -----Original Message-----
> > From: [hidden email] 
> </user/SendEmail.jtp?type=node&node=4639763&i=0> [mailto:[hidden 
> email] </user/SendEmail.jtp?type=node&node=4639763&i=1>]
> > On Behalf Of John Hodgson
> > Sent: Thursday, August 09, 2012 12:56
> > To: [hidden email] </user/SendEmail.jtp?type=node&node=4639763&i=2>
> > Subject: [R] Factor moderators in metafor
> >
> > I'm puzzled by the behaviour of factors in rma models, see example and
> > comments below. I'm sure there's a simple explanation but can't see 
> it...
> >
> > Thanks for any input
> >
> > John Hodgson
> >
> >
> > ------------------------------------- code/selected output 
> ---------------
> > --
> >
> >
> > library(metafor)
> >
> > ##    Set up data (from Lenters et al  A Meta-analysis of Asbestos and
> > Lung
> > Cancer...
> > ##    Environmental Health Perspectives • volume 119 | number 11 |
> > November
> > 2011)
> >
> > KL = c(0.02905, 0.06929, -0.1523, 1.6441, 0.1215, 0.3975, 1.0566, 
> 0.1257,
> > 0.2277, 0.06791, 0.08164, 0.2526, 0.07577, 0.03266, 0.1141, 0.1836,
> > 1.8276,
> > 0.4149, 15.4974)
> > SE = c(0.006633, 0.09335, 0.08909, 0.4297, 0.07858, 0.1753, 0.3679,
> > 0.1837,
> > 0.2172, 0.2775, 0.4201, 0.1976, 0.7688, 0.06507, 0.06239, 0.09061, 
> 0.9509,
> > 0.2181, 7.331)
> >
> > VL = SE*SE
> >
> > amph =   c(0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
> > mix =    c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
> > ftype =  c(0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
> >
> >
> > factor(amph)
> > factor(ftype)
> > factor(mix)
> >
> > ##  Fit ftype...
> >
> > > rma(KL,VL,mods=ftype)
> >
> >
> > Mixed-Effects Model (k = 19; tau^2 estimator: REML)
> >
> > tau^2 (estimate of residual amount of heterogeneity): 0.0111 (SE = 
> 0.0095)
> > tau (sqrt of the estimate of residual heterogeneity): 0.1054
> >
> > Test for Residual Heterogeneity:
> > QE(df = 17) = 43.0937, p-val = 0.0005
> >
> > Test of Moderators (coefficient(s) 2):
> > QM(df = 1) = 1.1069, p-val = 0.2928
> >
> > Model Results:
> >
> >          estimate      se    zval    pval    ci.lb   ci.ub
> > intrcpt    0.0811  0.0606  1.3380  0.1809  -0.0377  0.2000
> > mods       0.0473  0.0449  1.0521  0.2928  -0.0408  0.1353
> >
> >
> > Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> >
> > ##   why only one df for the 2-level factor?
> > ##   in other words, why isn't the above model the same as the
> > following...
> >
> >
> > >
> > >
> > >
> > > rma(KL,VL,mods=cbind(amph,mix))
> >
> > Mixed-Effects Model (k = 19; tau^2 estimator: REML)
> >
> > tau^2 (estimate of residual amount of heterogeneity): 0.0030 (SE = 
> 0.0046)
> > tau (sqrt of the estimate of residual heterogeneity): 0.0549
> >
> > Test for Residual Heterogeneity:
> > QE(df = 16) = 37.5762, p-val = 0.0017
> >
> > Test of Moderators (coefficient(s) 2,3):
> > QM(df = 2) = 6.9220, p-val = 0.0314
> >
> > Model Results:
> >
> >          estimate      se    zval    pval    ci.lb   ci.ub
> > intrcpt    0.0380  0.0402  0.9448  0.3447  -0.0408  0.1169
> > amph       0.2879  0.1163  2.4754  0.0133   0.0599  0.5158  *
> > mix        0.0888  0.0625  1.4199  0.1556  -0.0338  0.2114
> >
> > ---
> > Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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