scaling issues with vegan's envfit()

If I understand correctly Arnaud, you just need `predict()`:

predict(pca1, newdata = varechem, type = "sp", scaling = 1)[, 1:2]

where you set newdata to be the data frame of new variables you want 
to add. This will add so-called passive species to the plot base on 
the linear combinations of the site scores produced from the 
ordination of the original data. Hence the new data need to be 
measured on the same sites and have the same row names as the data 
used to fit the PCA (as that is how vegan identifies them).

Using the example you gave I get:

scores(pca1, scaling = 1, choices = 1:2, display = "sp")

                PC1        PC2
N         0.4008477  0.5267927
P        -1.5075888 -0.8781843
K        -1.5384951 -0.6850869
Ca       -1.5239497 -0.3582552
Mg       -1.4922949 -0.4141239
S        -1.4083631 -1.2567985
Al        0.4893605 -2.0353607
Fe        0.8599106 -1.6224771
Mn       -1.2181461  0.8183585
Zn       -1.4658174 -0.6861639
Mo       -0.1759892 -0.9685048
Baresoil -0.6925869  1.3373455
Humdepth -0.9968279  1.4290890
pH        0.7037755 -1.3647204
attr(,"const")
[1] 4.236078

predict(pca1, varechem, type = "sp", scaling = 1)[, 1:2]

                PC1        PC2
N         0.4008477  0.5267927
P        -1.5075888 -0.8781843
K        -1.5384951 -0.6850869
Ca       -1.5239497 -0.3582552
Mg       -1.4922949 -0.4141239
S        -1.4083631 -1.2567985
Al        0.4893605 -2.0353607
Fe        0.8599106 -1.6224771
Mn       -1.2181461  0.8183585
Zn       -1.4658174 -0.6861639
Mo       -0.1759892 -0.9685048
Baresoil -0.6925869  1.3373455
Humdepth -0.9968279  1.4290890
pH        0.7037755 -1.3647204

So we are able to recover exactly the correct scores - you just need 
to draw arrows (perhaps using ordiArrow()) from (0,0) to those 
coordinates.

I don't think `evnfit()` is the right approach here, especially trying 
to fake this via `ordiArrowMul()` as that is trying to fill the space 
and in scaling 1 you need to scale variables independently.

HTH

Gavin

On 12 September 2014 12:06, ADuranel <arnaud.duranel.09 at ucl.ac.uk 
<mailto:arnaud.duranel.09 at ucl.ac.uk>> wrote:

    Hello

    I am running a PCA of some environmental variables. I have a
    second set of
    environmental variables that I would like to project passively
    onto the
    ordination plot, but with the same arrow length scaling as that
    used for the
    first dataset. I used envfit() in vegan for that purpose. To check
    that I
    get the same arrow length scaling in plot.envfit() as in plot.cca(), I
    fitted the same dataset:

    require(vegan)
    data(varechem)
    pca1<-rda(varechem, scale=T)

    ef<-envfit(pca1, scale(varechem))
    plot(pca1, type="points")
    sp.sc <http://sp.sc><-scores(pca1, display="species")
    ef.sc <http://ef.sc><-scores(ef, display="vectors")
    plot(ef, arrow.mul=abs(sp.sc <http://sp.sc>[1,1]/ef.sc
    <http://ef.sc>[1,1]), col="red")
    # equivalent to:
    plot(ef, arrow.mul=vegan:::ordiArrowMul(ef.sc
    <http://ef.sc>)/vegan:::ordiArrowMul(sp.sc <http://sp.sc>))

    The last two lines are the only two ways I managed to get the same
    length
    scaling for the plot.cca() "species" and plot.envfit() vectors.
    However if I want to use another scaling (scaling=1 or 3) for the
    PCA plot,
    I can't get similar coordinates for the plot.cca() "species" and
    plot.envfit() vectors:

    ef1<-envfit(pca1, scale(varechem), scaling=1)
    plot(pca1, scaling=1, type="points")
    sp.sc1<-scores(pca1, scaling=1, display="species")
    ef.sc1<-scores(ef1, scaling=1, display="vectors")
    plot(ef1, arrow.mul=abs(sp.sc1[1,1]/ef.sc1[1,1]), col="red") #
    wrong: the
    ratio sp.sc/ef.sc <http://sp.sc/ef.sc> varies according to
    "species" in this case...
    plot(ef1,
    arrow.mul=vegan:::ordiArrowMul(ef.sc1)/vegan:::ordiArrowMul(sp.sc1))
    # the
    arrow length scaling varies according to "species".

    Can somebody tell me what I am doing wrong here?

    I use R version 3.1.1 (2014-07-10) with vegan_2.0-10
    Platform: x86_64-w64-mingw32/x64 (64-bit)

    Many thanks

    Arnaud





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