Re: [R-sig-eco] Factors in partial RDA part 2
Thanks Jari,
My education continues!
cheers
Andy
On 8 February 2017 at 17:32, Jari Oksanen wrote:
> I said earlier that you can use either dummy variables or factors with
> little difference. However, there are some cases where the differences
> becomes important: that is the case when you start playing with individual
> dummy variables like they were real variables. Don't do that! Do not use
> forward.sel with dummy variables: it makes no sense. Do not look at the VIF
> values of single factor levels: that rarely makes sense, and they can never
> suggest removing single levels of factors. It is the whole factor, in our
> out. Never partly in, partly out. If you go to play like that, you really
> should switch to standard R way of defining your factor as a factor. The
> highish VIF values for some levels are probably triggered by correlations
> with some continuous variables in your model.
>
> There should be a generalized VIF in vegan that gives one statistic for
> the whole factor. Contributions are welcome at
> http://github.com/vegandevs/vegan.
>
> cheers, Jari Oksanen
>
> From: R-sig-ecology on behalf of
> Andrew Halford
> Sent: 08 February 2017 10:14
> To: r-sig-ecology@r-project.org
> Subject: [R-sig-eco] Factors in partial RDA part 2
>
> Hi Listers,
>
> Further to my last post I am seeking more insights into how to interpret
> effects of Factors in a RDA analysis.
>
> I think of a factor as a single variable whose influence on observed fish
> distribution patterns I would like to quantify, along with a bunch of other
> numerical variables.
>
> To do the analyses this Factor (called 'geom') is turned into a number of
> dummy variables (seven actually).
>
> The conceptual problem I am having is that when I do a call to vif.cca to
> check on collinearity for example, the output suggests I should remove some
> of the dummy variables making up the levels of my Factor. Doing this would
> leave me with only 3 of the dummy variables out of the original 8 to put
> into the RDA. I then don't see that I am actually testing the Factor 'geom'
> anymore but rather just individual variables representing a couple of the
> different levels of the original Factor. How do I proceed with this?
>
> The same conundrum for me is seen when I run the forward.sel command to
> look at the most efficient number of explanatory variables to have in the
> final model. The process selects only some of the dummy variables to
> include in the model. Again I struggle to see how I am testing or including
> the full effects of the Factor 'geom' if only a few of the dummy variables
> are actually included in the model.
>
> # here is the model run with all the potential explanatory variables
> ('geom' is the FACTOR with 7 levels)
>
> > fish.env <-
> rda(fish.h~coral_cover+macroalgae+turf_algae_sqrt+
> ccc_4thrt+rubble_sqrt+reef_slope_sqrt+rugosity
>
> +exposure+min_d_sqrt+d_range+chl_a_log+popn_density_4throot+fp+protection
> +geom,data=env.factor)
>
> # collinearity assessment - the results favour dropping 3 of the 'geo'
> dummy variables leaving only 2 for the model.
> > vif.cca(fish.env)
>
> coral_cover macroalgae turf_algae_sqrt
> ccc_4thrt rubble_sqrt reef_slope_sqrt
> 2.688656 3.099972 2.849219
> 1.771637 2.411291 2.418953
> rugosity exposure min_d_sqrt
> d_rangechl_a_log popn_density_4throot
> 2.967752 3.433961 2.587696
> 2.643991 3.626107 4.571781
> fp protection geomgeo_bl
> geomgeo_cbrcgeomgeo_isefrgeomgeo_isprc
> 4.059624 3.195210 4.329852
> 12.657270 9.57001512.052385
> geomgeo_lefr geomgeo_oefr
> 7.34781217.090731
>
> # here I have kept all the 'geom' dummy variables to submit to forward
> selection and it only selects 3 of them, hence it doesnt feel that I am
> actually including a Factor 'geom' in the model but rather just a few
> individual dummy variables?
>
>
> > forward.sel(fish.h,env.dummy3,adjR2thresh=R2a.all_fish_env)
>
> Testing variable 1
> Testing variable 2
> Testing variable 3
> Testing variable 4
> Testing variable 5
> Testing variable 6
> Testing variable 7
> Testing variable 8
> Procedure stopped (alpha criteria): pvalue for variable 8 is 0.092000
> (superior to 0.05)
> variables order R2 R2Cum AdjR2CumF pval
> 1exposure 8 0.07086240 0.
Re: [R-sig-eco] Factors in partial RDA part 2
I said earlier that you can use either dummy variables or factors with little
difference. However, there are some cases where the differences becomes
important: that is the case when you start playing with individual dummy
variables like they were real variables. Don't do that! Do not use forward.sel
with dummy variables: it makes no sense. Do not look at the VIF values of
single factor levels: that rarely makes sense, and they can never suggest
removing single levels of factors. It is the whole factor, in our out. Never
partly in, partly out. If you go to play like that, you really should switch to
standard R way of defining your factor as a factor. The highish VIF values for
some levels are probably triggered by correlations with some continuous
variables in your model.
There should be a generalized VIF in vegan that gives one statistic for the
whole factor. Contributions are welcome at http://github.com/vegandevs/vegan.
cheers, Jari Oksanen
From: R-sig-ecology on behalf of Andrew
Halford
Sent: 08 February 2017 10:14
To: r-sig-ecology@r-project.org
Subject: [R-sig-eco] Factors in partial RDA part 2
Hi Listers,
Further to my last post I am seeking more insights into how to interpret
effects of Factors in a RDA analysis.
I think of a factor as a single variable whose influence on observed fish
distribution patterns I would like to quantify, along with a bunch of other
numerical variables.
To do the analyses this Factor (called 'geom') is turned into a number of
dummy variables (seven actually).
The conceptual problem I am having is that when I do a call to vif.cca to
check on collinearity for example, the output suggests I should remove some
of the dummy variables making up the levels of my Factor. Doing this would
leave me with only 3 of the dummy variables out of the original 8 to put
into the RDA. I then don't see that I am actually testing the Factor 'geom'
anymore but rather just individual variables representing a couple of the
different levels of the original Factor. How do I proceed with this?
The same conundrum for me is seen when I run the forward.sel command to
look at the most efficient number of explanatory variables to have in the
final model. The process selects only some of the dummy variables to
include in the model. Again I struggle to see how I am testing or including
the full effects of the Factor 'geom' if only a few of the dummy variables
are actually included in the model.
# here is the model run with all the potential explanatory variables
('geom' is the FACTOR with 7 levels)
> fish.env <-
rda(fish.h~coral_cover+macroalgae+turf_algae_sqrt+ccc_4thrt+rubble_sqrt+reef_slope_sqrt+rugosity
+exposure+min_d_sqrt+d_range+chl_a_log+popn_density_4throot+fp+protection
+geom,data=env.factor)
# collinearity assessment - the results favour dropping 3 of the 'geo'
dummy variables leaving only 2 for the model.
> vif.cca(fish.env)
coral_cover macroalgae turf_algae_sqrt
ccc_4thrt rubble_sqrt reef_slope_sqrt
2.688656 3.099972 2.849219
1.771637 2.411291 2.418953
rugosity exposure min_d_sqrt
d_rangechl_a_log popn_density_4throot
2.967752 3.433961 2.587696
2.643991 3.626107 4.571781
fp protection geomgeo_bl
geomgeo_cbrcgeomgeo_isefrgeomgeo_isprc
4.059624 3.195210 4.329852
12.657270 9.57001512.052385
geomgeo_lefr geomgeo_oefr
7.34781217.090731
# here I have kept all the 'geom' dummy variables to submit to forward
selection and it only selects 3 of them, hence it doesnt feel that I am
actually including a Factor 'geom' in the model but rather just a few
individual dummy variables?
> forward.sel(fish.h,env.dummy3,adjR2thresh=R2a.all_fish_env)
Testing variable 1
Testing variable 2
Testing variable 3
Testing variable 4
Testing variable 5
Testing variable 6
Testing variable 7
Testing variable 8
Procedure stopped (alpha criteria): pvalue for variable 8 is 0.092000
(superior to 0.05)
variables order R2 R2Cum AdjR2CumF pval
1exposure 8 0.07086240 0.0708624 0.04925455 3.279475 0.001
2 fp13 0.04756799 0.1184304 0.07645089 2.266248 0.002
3 geo_isefr16 0.04571706 0.1641474 0.10298750 2.242500 0.003
4 chl_a_log11 0.03812686 0.2022743 0.12250174 1.911778 0.009
5 geo_bl17 0.03423972 0.2365140 0.13863121 1.749016 0.008
6 geo_isprc21 0.03384005 0.2703541 0.15514682 1.762391 0.012
7 reef_slope_sqrt 6 0.03466752 0.3050216 0.17353919 1.845666 0.007
Any advice appreciated
cheers
Andy
--
Andrew Halford Ph.D
Research Scientist (Kimberley Marine Parks)
[R-sig-eco] Factors in partial RDA part 2
Hi Listers,
Further to my last post I am seeking more insights into how to interpret
effects of Factors in a RDA analysis.
I think of a factor as a single variable whose influence on observed fish
distribution patterns I would like to quantify, along with a bunch of other
numerical variables.
To do the analyses this Factor (called 'geom') is turned into a number of
dummy variables (seven actually).
The conceptual problem I am having is that when I do a call to vif.cca to
check on collinearity for example, the output suggests I should remove some
of the dummy variables making up the levels of my Factor. Doing this would
leave me with only 3 of the dummy variables out of the original 8 to put
into the RDA. I then don't see that I am actually testing the Factor 'geom'
anymore but rather just individual variables representing a couple of the
different levels of the original Factor. How do I proceed with this?
The same conundrum for me is seen when I run the forward.sel command to
look at the most efficient number of explanatory variables to have in the
final model. The process selects only some of the dummy variables to
include in the model. Again I struggle to see how I am testing or including
the full effects of the Factor 'geom' if only a few of the dummy variables
are actually included in the model.
# here is the model run with all the potential explanatory variables
('geom' is the FACTOR with 7 levels)
> fish.env <-
rda(fish.h~coral_cover+macroalgae+turf_algae_sqrt+ccc_4thrt+rubble_sqrt+reef_slope_sqrt+rugosity
+exposure+min_d_sqrt+d_range+chl_a_log+popn_density_4throot+fp+protection
+geom,data=env.factor)
# collinearity assessment - the results favour dropping 3 of the 'geo'
dummy variables leaving only 2 for the model.
> vif.cca(fish.env)
coral_cover macroalgae turf_algae_sqrt
ccc_4thrt rubble_sqrt reef_slope_sqrt
2.688656 3.099972 2.849219
1.771637 2.411291 2.418953
rugosity exposure min_d_sqrt
d_rangechl_a_log popn_density_4throot
2.967752 3.433961 2.587696
2.643991 3.626107 4.571781
fp protection geomgeo_bl
geomgeo_cbrcgeomgeo_isefrgeomgeo_isprc
4.059624 3.195210 4.329852
12.657270 9.57001512.052385
geomgeo_lefr geomgeo_oefr
7.34781217.090731
# here I have kept all the 'geom' dummy variables to submit to forward
selection and it only selects 3 of them, hence it doesnt feel that I am
actually including a Factor 'geom' in the model but rather just a few
individual dummy variables?
> forward.sel(fish.h,env.dummy3,adjR2thresh=R2a.all_fish_env)
Testing variable 1
Testing variable 2
Testing variable 3
Testing variable 4
Testing variable 5
Testing variable 6
Testing variable 7
Testing variable 8
Procedure stopped (alpha criteria): pvalue for variable 8 is 0.092000
(superior to 0.05)
variables order R2 R2Cum AdjR2CumF pval
1exposure 8 0.07086240 0.0708624 0.04925455 3.279475 0.001
2 fp13 0.04756799 0.1184304 0.07645089 2.266248 0.002
3 geo_isefr16 0.04571706 0.1641474 0.10298750 2.242500 0.003
4 chl_a_log11 0.03812686 0.2022743 0.12250174 1.911778 0.009
5 geo_bl17 0.03423972 0.2365140 0.13863121 1.749016 0.008
6 geo_isprc21 0.03384005 0.2703541 0.15514682 1.762391 0.012
7 reef_slope_sqrt 6 0.03466752 0.3050216 0.17353919 1.845666 0.007
Any advice appreciated
cheers
Andy
--
Andrew Halford Ph.D
Research Scientist (Kimberley Marine Parks)
Dept. Parks and Wildlife
Western Australia
Ph: +61 8 9219 9795
Mobile: +61 (0) 468 419 473
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