[R-sig-ME] [R-sig-eco] LRT tests in lmer

Chris Mcowen cm744 at st-andrews.ac.uk
Wed Aug 11 18:20:08 CEST 2010 

Sorry about the formatting, 

i was not going to use P values for model selection, rather the DIC value

 Iterations = 12991
 Thinning interval  = 3001
 Sample size  = 1000 

 DIC: 3171.501 

 G-structure:  ~order

      post.mean  l-95% CI u-95% CI eff.samp
order      7720 4.023e-13  0.09208     1000

               ~fam:fam

        post.mean  l-95% CI u-95% CI eff.samp
fam:fam   4092456 2.376e-12  0.02938     1000

 R-structure:  ~units

      post.mean l-95% CI u-95% CI eff.samp
units         1        1        1        0

 Location effects: IUCN ~ STO + BS + PD + FR + END + WO + RG + SEA + ALT + BIO + SE + FS 

                         			post.mean   l-95% CI   u-95% 		CI eff.samp pMCMC   
(Intercept)              		39.065870  -3.510793   2.407406   1000.0 		0.776   
STOStorage organ        	 -0.004916  -0.299409   0.230731    757.2		 0.946   
BSUnisexual flower       	 0.211852  -0.131660   0.548879    708.0 		0.212   
BSUnisexual plant         	0.370895   0.003567   0.817429    770.3 		0.070 . 
PDBiotic                  		0.381261   0.054626   0.724368    774.4 		0.040 * 
PDMammalia               		26.364377  -2.139720   1.397539   1000		.0 0.724   
FRNon_fleshy_fruit       	-0.208198  -0.536699   0.083012    964.2 		0.202   
ENDNon_endospermous   0.503829   0.200868   0.822120    591.7 		0.004 **
WOWoody                  		-0.203632  -0.565069   0.139240    857.5 		0.272   
RGTwo+                   		-0.052508  -0.250675   0.163811    831.8 		0.588   
SEAHapaxanthic           	-1.344993  -4.504625   1.848373    890.4 		0.406   
SEAHapaxanthic          	  0.223060  -1.590483   2.012970    785.9 		0.800   
SEAPerennial             		-0.097971  -0.460607   0.304681    849.9 		0.580   
SEAPleonanthic       	       -0.069756  -0.813837   0.704066    969.4 		0.872   
ALTHigh                 		 -0.129331  -0.483238   0.200436   1000.0 		0.472   
ALTLow                  		 -0.171467  -0.514753   0.121200    842.9 		0.316   
ALTMid                   		 0.068307  -0.227978   0.379701    814.9 		0.660   
BIOBoreal                 		1.785916  -1.222387   4.769563    860.2 		0.254   
BIOMediterranean-type     2.105530  -0.888236   4.786029    817.9 		0.156   
BIOSubantarctic           	2.214561  -0.888921   5.239470    841.3 		0.190   
BIOSubarctic            		  2.441894  -0.667793   5.677992    849.5 		0.142   
BIOSubtropical/Tropical     2.336425  -0.660675   4.899198    928.3 		0.124   
BIOTemperate             		 2.315834  -0.761101   4.826330    809.2 		0.132   
SEFew-Several           		146.220538  -0.620787   3.933475   1000.0 		0.172   
SENumerous              	  	0.206148  -0.117869   0.572987    734.9 		0.236   
SESeveral                 		0.626675  -0.236956   1.456895    881.7 		0.134   
SESingle                		  0.399690   0.030041   0.779923    709.8 		0.032 * 
FSZygomorphic            	 0.032334  -0.215194   0.265597    355.7 		0.814   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

 Cutpoints: 
                     post.mean l-95% CI u-95% CI eff.samp
cutpoint.traitIUCN.1    0.6593   0.5211    0.793    48.46
cutpoint.traitIUCN.2    2.4694   2.2952    2.663    41.37
cutpoint.traitIUCN.3    3.6258   3.4220    3.827    38.02
cutpoint.traitIUCN.4    4.1156   3.9166    4.341    52.46
On 11 Aug 2010, at 17:15, Jarrod Hadfield wrote:

Hi,

Could you give summary(model) with the new version (2.05) - it will be easier to see what is going on?

Jarrod
On 11 Aug 2010, at 17:08, Chris Mcowen wrote:

> Hi Jarrord,
> 
> I have tried using MCMCglmm, however the posterior distributions of the majority of the fixed factors straddle 0, which i have read is a problem, likely with the priors.
> 
> HPDintervals - https://files.me.com/chrismcowen/wqq1lu
> 
> prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=0), G2=list(V=1, nu=0)))
> 
> So i am unsure how to interpret the results, as to ascertain the importance of each factor.
> 
> Unfortunately i don't know enough about baysian statistics or R to alter my model so the interpretations become clearer.
> 
> An example
> 
>                             			lower      		upper
> (Intercept)             			-3.510792767 	2.40740650
> STOStorage organ        	-0.299408836 	0.23073133
> BSUnisexual flower      	-0.131660436 	0.54887912
> BSUnisexual plant       	 0.003566637 	0.81742862
> PDBiotic                			 0.054625970 	0.72436838
> PDMammalia              		-2.139720264 	1.39753939
> 
> 
> 
> On 11 Aug 2010, at 16:37, Jarrod Hadfield wrote:
> 
> Hi Chris,
> 
> It is hard to say as it will depend on the fixed effects. In addition its not clear whether such a situation is diagnostic of a problem.  Imagine you just have an intercept which is estimated to be exactly zero. The residuals on the data scale will be either 0.5 or -0.5, but this does not imply the model is wrong.
> 
> Cheers,
> 
> Jarrod
> 
> On 11 Aug 2010, at 15:41, Chris Mcowen wrote:
> 
>> Thats great thanks,
>> 
>> But will this work where you have a binary response variable or will the residuals clump around 1 and 0?
>> 
>> Chris
>> On 11 Aug 2010, at 15:31, Ben Bolker wrote:
>> 
>> On 10-08-11 10:21 AM, Chris Mcowen wrote:
>>> Dear Ben/Rob.
>>> 
>>> 
>>>> As far as I can tell, the standard advice is simply to look at the predictions of the model, compare them with the data, and try to spot any systematic patterns in the residuals.
>>>> 
>>> 
>>> I have plotted the residuals of my model - https://files.me.com/chrismcowen/v586vx
>>> 
>>> I have been made aware that  that lmer uses the random effects in its  prediction ( Jarrord Hadfield). And this is reflected in the residual plot with the the long lines of equal residuals all belonging  to the same family - i.e 200 - 600 is the orchid family and 650-100 is the grass family.
>>> 
>>> So is there a work around with a glmm?
>>> 
>>> 
>>> 
>>> Thanks
>>> 
>>> Chris
>>> 
>>> 
>> 
>> If you want to do population-level predictions from a GLMM (i.e. setting all random effects to zero), the basic recipe is to (1) construct a model (design) matrix for the desired sets of predictor variables (if you want to the predict the observed data rather than some other set, you can just extract the model matrix from the fitted object); (2) multiply it by the vector of fixed effect coefficients; (3) transform it back to the scale of the observations with the inverse link function.  There's an example on p. 6 of http://glmm.wdfiles.com/local--files/examples/Owls.pdf ...
>> 
>> _______________________________________________
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> 
> 
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