isotab: Ordered frequency table for Isopam clusters

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Computes an ordered frequency table based on Isopam clustering results. The upper part of the table lists typical descriptors (usually species) with a significant binding to single clusters (according to customisable thresholds). The lower part of the table is ordered by descending overall frequency.

Usage

1
isotab(ip, level = 1, phi.min = 'auto', p.max = .05)

Arguments

ip

object of class isopam.

level

level in cluster hierarchy starting with 1 = first division.

phi.min

threshold of phi determining which descriptors (species) are listed in the upper part of the table. Applies only to descriptors passing the criterion defined by p.max. If phi.min = 'auto' (the default) isotab suggests a suitable value based on the numbers of clusters, observations, and descriptors.

p.max

threshold of Fisher's p determining which descriptors (species) are listed in the upper part of the table. Applies only to descriptors passing the criterion defined by phi.min.

Details

phi.min is based on the standardized phi value according to Chitrý et al. 2002.

Value

tab

dataframe with ordered frequencies and their significance. The latter is derived from Fisher's exact test (p <= 0.05: *, p <= 0.01: **, p <= 0.001: ***).

n

matrix with cluster sizes.

thresholds

phi.min and p.max used.

typical

dataframe with items (often species) typically found in clusters (according to thresholds).

Author(s)

Sebastian Schmidtlein

References

Chitrý, M., Tichý, L., Holt, J., Botta-Dukát, Z. (2002): Determination of diagnostic species with statistical fidelity measures. Journal of Vegetation Science 13, 79–90.

Schmidtlein, S., Tichý, L., Feilhauer, H., Faude, U. (2010): A brute force approach to vegetation classification. Journal of Vegetation Science (in press).

See Also

isopam

Examples

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   ## load data to the current environment
   data(andechs)
     
   ## call isopam with the standard options
   ip<-isopam(andechs)
    
   ## build table (uppermost hierarchy level)
   isotab(ip)

   ## build table (lower hierarchy level)
   isotab(ip,2)

Example output

Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-3
Loading required package: cluster
[1] Processing level 1

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[1] Level 2: Processing 2 groups
[1] Group 1

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isomap returns only 4 axes with positive eigenvalues
[1] Group 2

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[1] Level 3: Processing 2 groups
[1] Group 1

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[1] Group 2

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[1] Cluster tree with 2 levels created
$tab
           1    2
Car_pan 100*  50*
Hol_lan  71*  10*
Ang_syl 71**  0**
Des_ces 71**  0**
Gal_pal  57*   0*
Car_lep  57*   0*
Car_flc  14*  80*
Sch_fer  0** 80**
Cen_jac  14*  70*
Car_sem  0** 70**
Tof_cal   0*  50*
Cir_tub   0*  50*
Mol_cae  100  100
Pot_ere   86  100
Bri_med   57  100
Gal_bor   57  100
Ran_nem   71   90
Car_hos   57   90
Car_pul   57   90
Ses_var   43   90
Fes_ovi   86   60
All_sua   57   70
Ser_tin   43   70
Car_dav   71   40
Koe_pyr   29   50
Gal_mol   43   40
Suc_pra   14   50
Bra_pin   14   50
Cir_pal   57   20
Bet_off   14   40
Fra_aln   29   30
Cir_riv   14   40
Car_pal   57   10
Phr_aus   43   20
Ran_acr   57   10
San_off   14   30
Pru_gra    0   40
Lin_cat   14   30
Dan_dec    0   40
Leo_his   14   30
Eri_lat   43   10
Ave_pub   29   20
Geu_riv   43   10
Phy_orb    0   30
Ast_bel    0   30
Gen_asc    0   30
Bar_alp    0   30
Gla_pal    0   30
Gal_ver    0   30
Lot_cor   14   20
All_car   14   20
Jun_art   43    0
Car_fla   43    0
Peu_pal   29   10
Ant_odo   29   10
Agr_ten   29   10
Sca_col   29   10
Ant_ram    0   20
Pol_ama    0   20
Pri_far    0   20
Gym_odo    0   20
Lis_ova    0   20
Gym_con    0   20
Hip_com    0   20
Col_aut   14   10
Dac_glo   29    0
Tro_eur   14   10
Tri_pra   14   10
Cre_pal   29    0
Cir_ole   29    0
Luz_cam   14   10
Car_ela   14   10
Par_pal    0   10
Sco_hum    0   10
Gen_clu    0   10
Cal_var    0   10
Tet_mar    0   10
Pol_vul    0   10
Dac_och    0   10
Cre_mol   14    0
Bup_sal    0   10
Cal_epi    0   10
Aqu_atr    0   10
Val_dio    0   10
Pim_maj   14    0
Dac_inc    0   10
Vic_cra   14    0
Car_nig   14    0
Pol_bis   14    0
Lat_pra   14    0
Epi_pal   14    0
Lys_vul   14    0
Aju_rep   14    0
Hyp_per   14    0
Lyc_flo   14    0
Men_aqu   14    0
Que_rob    0   10
Rhi_spe   14    0
Jun_sub   14    0
Pru_vul   14    0
Hyp_tet   14    0
Poa_pra    0   10
Jun_eff   14    0
Eri_ang   14    0
Car_ros   14    0
Val_off    0   10
Gle_hed    0   10
Pla_lan   14    0
Fes_aru   14    0
Pla_maj    0   10

$n
  1  2
n 7 10

$thresholds
phi.min   p.max 
   0.49    0.05 

$typical
Typically found in 1: 
Car_pan, Hol_lan, Ang_syl, Des_ces, Gal_pal, Car_lep

Typically found in 2: 
Car_flc, Sch_fer, Cen_jac, Car_sem, Tof_cal, Cir_tub

$tab
         1.0   2.1    2.2
Car_pan 100*    25     67
Hol_lan  71*     0     17
Ang_syl 71**     0      0
Des_ces 71**     0      0
Gal_pal  57*     0      0
Car_lep  57*     0      0
Sch_fer  0**  100*     67
Cir_riv   14 100**      0
Car_sem  0**    25 100***
Cir_tub   0*     0  83***
Bra_pin   14     0   83**
Phy_orb    0     0    50*
Gla_pal    0     0    50*
Mol_cae  100   100    100
Pot_ere   86   100    100
Bri_med   57   100    100
Gal_bor   57   100    100
Ran_nem   71   100     83
Car_hos   57   100     83
Car_pul   57    75    100
Ses_var   43    75    100
Fes_ovi   86   0**    100
All_sua   57    50     83
Ser_tin   43    75     67
Car_dav   71    25     50
Car_flc  14*    75     83
Cen_jac  14*    75     67
Koe_pyr   29    25     67
Gal_mol   43    25     50
Suc_pra   14    50     50
Cir_pal   57    25     17
Tof_cal   0*    50     50
Bet_off   14    25     50
Fra_aln   29     0     50
Car_pal   57     0     17
Phr_aus   43    25     17
Ran_acr   57     0     17
San_off   14    25     33
Pru_gra    0    25     50
Lin_cat   14    25     33
Dan_dec    0    25     50
Leo_his   14    50     17
Eri_lat   43    25      0
Ave_pub   29     0     33
Geu_riv   43     0     17
Ast_bel    0    25     33
Gen_asc    0    50     17
Bar_alp    0    25     33
Gal_ver    0    25     33
Lot_cor   14     0     33
All_car   14     0     33
Jun_art   43     0      0
Car_fla   43     0      0
Peu_pal   29    25      0
Ant_odo   29     0     17
Agr_ten   29     0     17
Sca_col   29     0     17
Ant_ram    0    25     17
Pol_ama    0    25     17
Pri_far    0    25     17
Gym_odo    0    25     17
Lis_ova    0     0     33
Gym_con    0     0     33
Hip_com    0     0     33
Col_aut   14     0     17
Dac_glo   29     0      0
Tro_eur   14    25      0
Tri_pra   14     0     17
Cre_pal   29     0      0
Cir_ole   29     0      0
Luz_cam   14     0     17
Car_ela   14     0     17
Par_pal    0    25      0
Sco_hum    0    25      0
Gen_clu    0     0     17
Cal_var    0    25      0
Tet_mar    0     0     17
Pol_vul    0     0     17
Dac_och    0    25      0
Cre_mol   14     0      0
Bup_sal    0     0     17
Cal_epi    0     0     17
Aqu_atr    0     0     17
Val_dio    0    25      0
Pim_maj   14     0      0
Dac_inc    0    25      0
Vic_cra   14     0      0
Car_nig   14     0      0
Pol_bis   14     0      0
Lat_pra   14     0      0
Epi_pal   14     0      0
Lys_vul   14     0      0
Aju_rep   14     0      0
Hyp_per   14     0      0
Lyc_flo   14     0      0
Men_aqu   14     0      0
Que_rob    0    25      0
Rhi_spe   14     0      0
Jun_sub   14     0      0
Pru_vul   14     0      0
Hyp_tet   14     0      0
Poa_pra    0     0     17
Jun_eff   14     0      0
Eri_ang   14     0      0
Car_ros   14     0      0
Val_off    0    25      0
Gle_hed    0     0     17
Pla_lan   14     0      0
Fes_aru   14     0      0
Pla_maj    0     0     17

$n
  1.0 2.1 2.2
n   7   4   6

$thresholds
phi.min   p.max 
   0.48    0.05 

$typical
Typically found in 1.0: 
Car_pan, Hol_lan, Ang_syl, Des_ces, Gal_pal, Car_lep

Typically found in 2.1: 
Sch_fer, Cir_riv

Typically found in 2.2: 
Car_sem, Cir_tub, Bra_pin, Phy_orb, Gla_pal

isopam documentation built on May 1, 2019, 8:02 p.m.

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