DOCUMENT: tutorial002

DOCUMENT: tutorial002
Illustration of running DIOGENE for processing, a diallel trial according to various ways
Introduction
The documents of the “tutorial series”, as the present one, are only concerned with the very practical problems to which the user is faced before being
able to use the results of data processing (thesis, publication, selection of the best genotypes etc.). Other documents, “notice series”, are designed to
give general informations about the biometrical and genetic models and cautions mandatory to draw conclusions from the experimental results.
These practical problems may be classified into three categories:
(1) Data organization: How to prepare the data files to be processed, both for the measured or graded traits (observations), for the codes which
describe the experimental structure which the user wants to analyze (indicators) and for the alphanumeric labelling of these two categories
that we shall shortly name “trait labels”, “indicator labels” or, more compactly, “labels”;
(2) Processing selection: How to select the program (or sequence of programs) which best fits the user’s aim, whatever the kind of results
would be (data management, estimation of parameters, exploration of data structure etc.)
(3) Importation of results: After data processing, it is necessary to obtain the final results in a form edited as well as possible and which
anybody can use for integrating them in a variety of documents.
For point (1), even if it is not the only way to prepare files for DIOGENE, the data in Excel format will be privileged because this spreadsheet is de
facto the standard tool used by researchers. For point (3) and for the same reasons, Word will also be privileged, even if “paper” edition of the results is
also possible without using it, via; for instance, an A3 network printer. Of course, Excel and Word may be replaced by programs belonging to the same
categories, for instance the equivalent spreadsheets and word-processors which can be found for Linux. The examples given in this documents have
been practically obtained On a Sun Microsystem Enterprise 450 server with Solaris 9 version of Unix (for running DIOGENE) and Windows-XP
(professional edition), with Office-XP, for preparation of Excel data files and importation/edition of results. The standard programs connecting the PC
to the server were Tera Term Pro as (alphanumeric terminal emulator) and WS-FTP95 LE, for file transfer. These programs can be freely downloaded
on internet. Lastly, although DIOGENE now enables 2-D and 3-D graphics (as interfaced with Gnuplot), this aspect will not developed in the basic
“tutorials”, because it requires graphic terminal emulators as X-windows, for instance. The points where the files specialized for these graphics are
created by the programs will be indicated. Note that these files can also be used for realization of graphics using Excel (or an equivalent spreadsheet).
The edited graphics will be alphanumeric. Specialized tutorials will be later devoted to high definition graphics. This tutorial follows another
document, tutorial001 which deals with a more simple situation and that we recommend to read first.
The above screen concerning an Excel table (d4423.xls file) and the three ones below display data from a diallel trial of maritime pine(experiment
d4423) which is managed by the EFPA Department of the French NIAR (National Institute of Agronomical Research). The mating design combines 12
parent trees as mothers and 11 of them as fathers (father # 9 is lacking). Each individual is referenced on a row of the Excel Table by five integer
numbers ie from left to right: mother, father, bloc, plantation row and rank of the tree on the row (“abscissa”). After these five indicators, five
measurements or observations are registered: height growth observed at three different years (1984, 1985, and 1986), circumference at breast height
(1995) and score for Dioryctria splendidella pine beetle attack in 1995. There are 74 blocs, numbered from 1 to 74. We remark that, compared to the
2
file “lebart.xls” presented in tutorial001, three indicators: mother, father and bloc (and not one only) receive a label. On the other hand, these
indicators are a sub-sample as row and abscissa remain without any label. Note that the ranks of the labels are assigned in a “positional” way, series of
10 contiguous ‘*’ being used to fill the places where no label is present. Moreover, there are more data rows than indicators and a varying number
of labels from one indicator to another. What is constant when comparing lebart and d4423 files is that the label sequences is always closed by
semicolons (column I here, where the dummy labels holding for abscissa are concerned).
Transcoding this file into a .prn format is done in the same fashion than for lebart.xls and we obtain a d4423.prn version that we transfer on the Unix
server again using the ASCII mode.
3
Note that to preserve, after data transfer, spaces between character chains representing labels or data we have inserted blank columns in the table.
4
This screen shows the beginning of data transfer.
5
The data transfer has been achieved and we can see above the 41 first rows of the Unix ASCII file. We repeat one time more that it is mandatory to
have no insertion of blanks within the strings, whatever their nature would be (labels or data).
6
Typing “diogene” give access to the main menu and, to make a little variation compared with the tutorial001 example, we shall run ASCBIN, the
transcodage program in interactive mode (see the position of the selection cursor).
7
We select the “file management” branch as shown above.
8
Then, we select “transcodage –copy again”.
9
And, lastly, the ASCBIN program which has become familiar to us.
10
The above screen displays the first parameters which describe the general structure of a d4423’s record. NB. If we enter erroneously a filename nonexisting or not suffixed by “.prn”, the program stops running.
11
Above are entered the last parameters. After entering the last one a transient display appears (not shown here) indicating that transcodage is done.
ASCBIN has detected factor’s labels and therefore chains automatically CREFAM which creates the file of factor’s labels already described in the
tutorial001 document.
12
After leaving the DIOGENE menu manager (typing W key), we can obtain an edition of the sortie standard output file where the combined result of
ASCBIN-CREFAM cooperation. In addition, NORMEX utility was also run (cf. message) to write minimum and maximum values of the indicators in
the parameter file of d4423, d4423.p. CREFAM needs these data to run.
13
On the above screen, we can see the beginning of the correspondence table between row numbers and labels. This table has been generated by
CREFAM. Note that, in spite of lack of # 9 father, the label of # 10 father has been correctly positioned on row 10, a dummy chain “**********”
being inserted at row 9. Any sequence gap l among codes would be dealt in the same fashion, including the case where the minimum value of some
codes would not be “1” (5, 7, 15 etc.).
14
And the LIRE program allows a screen edition of any part of the data file as shown above.
We have therefore exemplified here the general value of the file management system of DIOGENE. It is now useful to deal with the case where the
data file, with only trait labels and the factor’s labels are not available at the same time. This situation is met when one wants to label the modalities of
factors when only the data file and its parameter file are available. To simulate this alternative, we have shared the d4423.xls file in two subfiles:
- d4423bis.xls (data file with trait labels)
- d4423ter.xls (factor’s labels)
15
The beginning of the first of these files is shown above.
16
As the 41 first rows of the second one are visualized in this new screen. The d4423bis.prn file can be processed by ASCBIN exactly as the “complete”
d4423.prn file, with the same parameters. The only difference will be that the d4423bis.fam companion file will of course not generated (as having
not detected semicolons, ASCBIN will not run CREFAM).
17
We have run interactively ASCBIN, simply by typing its name (avoiding to run the menu manager to shorten the operations). The last message recalls
what we have just said concerning its property to run CREFAM in the appropriate situation.
Now, we change the name of the d4423ter.prn file into d4423bis.prn by typing the Unix command:
mv d4423ter.prn d4423bis.prn
In this example, as the first useful row has rank 2, we remove the fist row (blanks) of the file by typing “:dd” after editing the file with vi (and then, of
course, save the corrected file by typing “:w”). If we forgot this blank line removal, this will be detected by the program (warning edition).
18
If we run CREFAM in the same way as ASCBIN, we obtain the above screen which recalls what we have just mentioned. The only necessary parameter
is the name of the data file (d4423bis).
Thus, we have seen that the ways to obtain the sets of compatible 3-tuple files which contain all numeric data and alphanumeric labels are quite
flexible and fulfil the user’s needs in every situation. NB. For reasons which will be explained in a document of the “notice” series, the number of rows
of the “.fam” file is equal to the maximum value encountered among the N indicators. Here, it is given by the maximum value of the abscissa (240). Of
course, after row # 74, only series of “**********” are met.
19
The screen above gives the output of the MINMAX program that we have run directly. This program makes a first scanning of the d4423 file, giving
the minimum and maximum values of both indicators and traits. An additional and useful information given for each trait is the number of “valid”
individuals, that is, for this example, all records with values different of “-5” (not observed) or “-9” (dead at measurement time). We can see that, for
the first height measurement we have the minimum of observed individuals. Thus, we can suspect that these young trees where very small when
transplanted into the trial and were hidden by grass. As this implies a strong competition, it is legitimate, at least in a first step, to discard these
individuals from our computations. Therefore, we shall run the COPY program which, as CRECAR used for the tutorial001 document, will select the
records with no lacking observation. In addition, the sub-file that we shall obtain will be adequate for re-sampling (confidence intervals of estimates).
20
As shown by the above screen, COPIE created a sub-file with 2730 records with “no hole”: e4423. But, it may also be interesting to obtain a file
allowing to compute the frequencies of individuals scored as “-5” or “-9”. For this new purpose, we shall run the SURVIE program.
21
The above screen shows the message of SURVIE which allows frequency analysis the traits according to the three referenced categories. The original
file, d4423, is now changed into d4423back and its parameter file into d4423back.p as a copy of d4423.fam is made as d4423back.fam. If we are not
satisfied with this new name, we can change it, using the COPIE utility which also makes a copy of the “.fam” file according to the new name.
22
Let us first work with the e4423 sub-file to study quantitative data (without any transformation for this example). We wish to combine half-diallel
analysis with incomplete bloc trial, to obtain estimates of genetic parameters (coefficients of genetic prediction and genetic correlations) and perform
univariate/multivariate comparisons of GCA.
At this time (as a researcher is often a very scatter-brained person!), we realize that we have forgotten to create a supplementary indicator that will be
necessary for adjustment of traits to bloc effects: family code which combine mother and father codes. For this purpose, we run the MAJF3 program as
illustrated by the above screen. We chosen the option “output on sortie file”, to be able to visualize any parts of this output using the vi editor.
23
We see on the above screen the end of the output of MAJF3.The NORMEX utility has been automatically run for conversion of the resulting file (the
name of which is still “e4423”, which has the new “family” indicator in first position, to the EAN normalisation, now mandatory for every DIOGENE
data file.
Now an additional problem has to be solved: compatibility between the e4423.fam file and the new version of e4423. The incompatibility is due to the
discrepancy between number of indicators and number of label’s columns. To obtain this result, we shall use the CORFAM utility (see the next two
screens).
24
25
We can see on the above screen that an additional column of dummy “**********” has been created in the e4423.fam file. This ensures its
compatibility with e4423 data file. The “unexpected” value of “239” in place of “240” for the 6 th indicator is due to the fact that e24423 is a sub-file
with the result that one of the represented values for abscissa completely disappeared.
NB. The CORFAM utility may also, of course, be used to correct punctual “real” label values. Moreover, it is also designed to de novo create a .fam
file, avoiding that the user is obliged to use Excel for this purpose. All the parts of DIOGENE software were designed with in mind the aim to afford
to the users the greatest versatility as possible.
26
After typing “diogene”, we choose the “procedure” option (script’s name = exdial) and the following choices illustrated by the screens shown below.
27
Choice of the “biometry-quantitative genetics” branch.
28
Selection of the “MANOVA and following computations” branch.
29
Choice of the “Cross classification” model, with generation of an adjusted data file.
30
Choice of the “Half-diallel MANOVA” which will processed the data file adjusted for bloc effects.
31
Choice of the “Duncan/Newman-Keuls” group of methods to compare GCA effects from half-diallel analysis.
32
Backward jump to access to options for Discriminant Analysis.
33
Choice of the group of options corresponding to Discriminant Analysis.
34
Choice of the appropriate case of Discriminant Analysis with “downstream” options including Cluster Analysis.
35
Acceptation of all programs selected (“n” entered for triprog option).
36
Display by the supervisor of the ordered list of seven programs. First parameters for general processing options.
37
Next choice of general processing parameters.
38
End of choice of general parameters including the simplified definition of studied traits.
39
First parameters of the ENVIR program (adjustment of data for bloc effects). Note that we have chosen the “re-sampling” option in view to obtain the
confidence intervals for estimates of interest.
40
End of the parameters for ENVIR.
Important remark : The “.fam” file can only be used to provide labels form outputs of programs which use this file as starting data file. In the actual
DIOGENE version, the ENVIR program automatically makes a copy of this file, if any, into “vajust.fam”. Therefore, the labels are transferred to the
programs which use adjusted data without need of user intervention.
41
Firsts parameters for DIAL program. Choice of “fixed model” for comparison of effects is independent from an estimate of variances-covariances
according to “random model”. These estimations starting from values adjusted for bloc effects follow a “mixed model” (fixed blocs and random GCA
an SCA). Note that we have chosen the “re-sampling” option and that the amont parameter (“1”)indicates that the selection of samples will be done at
the level of ENVIR program as the estimates for which the confidence intervals will be computed are a part of those corresponding to DIAL program.
42
Next parameters for DIAL program. Note that the resampling system will automatically select the estimates the estimates corresponding to the edition
option (here, “normalised” matrices as different categories of correlations and Coefficients of Genetic Prediction).
43
Parameters corresponding to “downstream” options (including Discrimant Analysis). All the parameters with can be “guessed” by the supervisor are
automatically generated.
44
Last parameters for DIAL. Father is entered as “column” factor and mother as “row” factor. Note that and indicator for family (mother x father
combination) is not mandatory.
45
Warnings which indicate the “edition incompatibilities” for re-sampling. Here, we had erroneously chosen to make an edition both for matrices and
effects. We had to correct and choose the “o” value for the “effsup” option (no edition of GCA and/or SCA estimates).
46
Here, we choose the “Newman and Keuls” algorithm for GCA comparisons and the 5% significance level (DUNCAN program).
47
On the above screen are the parameters for DISCRI program (first part of Discriminant Analysis). The number of dimensions for computations of
Mahalanibis distances were further changed into “3”, as being far the most biologically significant.
48
The only parameter of ILLY program (projections of population centroids on the pairs of selected axes) - presence of “supplemental points - is
“guessed” by the supervisor.
49
On the above screen are given the parameters of MAHAL (computation of Mahalanobis distances).
50
First parameters of PRADET which computes dendrograms, here for the Mahalanobis distances after transformation into normalized similarities.
51
Last parameters of Pradet.
52
On the above screen and on the following ones are given the sequence of parameters for reiteration of the exdial script. Note that this last operation
was done after leaving the menu manager (“w” key). Another possibility is to select “jbstar” in the menu. If we do so, JBSTAR is run interactively,
and we have not to leave the menu-manager.
53
JBSTAR displays a variety of warnings to help the user. In the last entry of the above screen, we have to specify the “structures” (matrices or vectors)
for which we want to get confidence intervals (and associated results as t tests).
54
The above screen allows the choice of the probability associated the confidence intervals (level of significance).
55
We can obtain creation of files of the successive estimates of the parameters in the reiteration process. This is mainly interesting when we use the
“bootstrap” method, because it allows computation of confidence intervals without assumption of normality of distribution for these estimates.
56
The above parameters show that the JBSTAR resampler can also be used for simulating model populations to test some “null hypotheses” concerning
population effects and between-trait associations. We have not used this possibility here. Other explanations can be read on the screen.
57
The above screen asks to the user to indicate what are the first and the last program of the “reiterated sequence”. Here, they are ENVIR and DIAL,
respectively (codes “2” and “9”).
58
The above screen gives the number of reiterated sequences as computed by JBSTAR. Eventually, there is an “offcut”, when the rest of division of the
sample size by the number of reiterations is not zero, but say r (the problem raises only for jackknife). In that case, the “total sample” is computed on
all the available individuals and the reiterations are truncated by leaving at the end of the file the last r individuals not implied in the re-sampling. The
user is free to accept or not a such approximation. He can also “call the chief” to obtain a strategy with no “offcut” or with the smallest possible value.
59
The above warning gives different indications. The most important of them are how to run the reiterated script and in what file the final results will be
stored. There are also some polite remarks. The SUPERPICHOT subroutine included in JBSTAR allows to run computations in a complex sequence of
programs, like here, where only a sub-sequence has to be reiterated. In that case, the reiterated subsequence must concern the first programs of this
sequence (here, the first two).
60
The script now controls running of the different programs. The sub-set of two first programs (ENVIR-DIAL) is run 2731 times (1 time on all data and
2730 times on all data but one, arranged in a circular permutation system). Finally, The Tuckey formula is computed on each individual estimate and
all subsequent statistics as confidence intervals are derived.
61
The last screen informs the user than the re-sampling process is achieved and the user can here obtain the results by editing the exdial.all file. Find
below this file after an edition using Word, following the choices described in the tutorial001 document. Some comments about these results are given
at the end of this tutorial002 document.
62
$*$*$*$*$* 24 heures sur 24, DIOGENE 2004 a votre service ! *$*$*$*$*$*
Biometrie du fichier : e4423
---------------------------------exemple
noms des
5 caracteres etudies :
--------------------------------y
y
y
y
y
1
2
3
4
5
=
=
=
=
=
haut84
haut85
haut86
circ95
attdio
..............................................................................
definition des
5 caracteres etudies :
y 1 = x1
y 2 = x2
y 3 = x3
y 4 = x4
y 5 = x5
..............................................................................
ENVIR : ajustement d'un fichier de donnees a n facteurs :
modele croise non-orthogonal a n facteurs
traitement en parallele de n caracteres (observes ou crees)
Carre moyen pour test F des effets principaux = CM intra cellule
Fichier d'entree = e4423
---------------------------------------------------Option de reechantillonnage =
1
Methode = JACKKNIFE, valeur du cache =
1
---------------------------------------------------Option impression d'effets (EFFSUP) =
0
Nombre d'indicatifs/enregistrement =
6
Mode
0
(0 = quantitatif, 1 = qualitatif + transformation, 2 = qualitatif sans transformation.)
Enregistrements numeros
1
a
2730
Numero du premier individu traite/enregistrement =
5
caracteres observes,
5
1 , dernier =
1 , saut =
etudies
Contraintes
63
1
lim.inf.
indicatif
indicatif
caractere
caractere
4
1
1
1
lim.sup.
1
1
-99999.000
-99999.000
99999
99999
99999.000
99999.000
Constante de correction pour d.l. d'erreur (donnees ajustees) =
Nombre de niveaux du facteur bloc retenus =
0
74
Nombre de niveaux du facteur code en sequenc retenus =
Nombre de cellules bloc*code en sequenc retenues =
115
1047
Carres moyens & tests F sous l'hypothese d'effets fixes
(sous les tests F figurent les seuils de signification en %)
Carres moyens du facteur bloc ajuste (
y 1
haut84
3.3742E+03
tests F (
y 2
haut85
6.3573E+03
y 3
haut86
1.4121E+04
73 d.l.)
y 4
circ95
3.7276E+02
y 5
attdio
1.7354E-01
y 4
circ95
4.736
0.000
y 5
attdio
1.795
0.006
73 et 1683 d.l.)
y 1
haut84
13.801
0.000
y 2
haut85
16.723
0.000
y 3
haut86
18.760
0.000
Carres moyens du facteur code en sequenc ajuste (
y 1
haut84
1.0969E+03
Tests F (
y 2
haut85
2.0122E+03
114 d.l.)
y 3
haut86
5.1097E+03
y 4
circ95
3.7496E+02
y 5
attdio
2.1100E-01
y 3
haut86
6.788
0.000
y 4
circ95
4.764
0.000
y 5
attdio
2.182
0.000
114 et 1683 d.l.)
y 1
haut84
4.487
0.000
y 2
haut85
5.293
0.000
Carres moyens de l'interaction bloc * code en sequenc (
y 1
haut84
4.8914E+02
Tests F (
y 2
haut85
7.7596E+02
y 3
haut86
1.6634E+03
y 4
circ95
9.3732E+01
859 d.l.)
y 5
attdio
1.1727E-01
859 et 1683 d.l.)
64
y 1
haut84
2.001
0.000
y 2
haut85
2.041
0.000
y 3
haut86
2.210
0.000
y 4
circ95
1.191
0.145
y 5
attdio
1.213
0.050
Carres moyens intra-cellule bloc * code en sequenc ( 1683 d.l.)
y 1
haut84
2.4448E+02
y 2
haut85
3.8015E+02
y 3
haut86
7.5272E+02
y 4
circ95
7.8709E+01
y 5
attdio
9.6702E-02
Moyennes (en % si caracteres qualitatifs non transformes)
Moyennes generales
y 1
haut84
54.709
y 2
haut85
77.535
y 3
haut86
134.685
y 4
circ95
39.077
Fichier VAJUST cree : 2730 enregistrements a
individu &
5 caracteres.
6
y 5
attdio
0.126
indicatifs,
1
65
NORMEX : conversion d'un fichier de norme ANTAR a la norme ANTAR etendue
Le fichier vajust a ete converti a la norme ANTAR etendue
-----------------------------------------------------------------------DIAL : MANOVA non orthogonale
modele diallele avec niveau individuel sans effet reciproque
(extension du modele Henderson III adaptee de Garretsen & Keuls 1977-78)
Fichier d'entree = vajust
----------------------------------------------------Nombre moyen d'individus/donnee elementaire =
Option de reechantillonnage =
1.000
3
Pilotage par un programme d'amont (AMONT = 1)
----------------------------------------------------carre moyen pour test F des AGC = CM intra-cellule
composantes individuelles utilisees pour estimation des variances-covariances.
options matrices & effets : MATSUP = 1 , DENDRO = 0 , EFFSUP = 0
option elimination des selfs
option Analyse Dicriminante :
population = Aptitude Generale a la combinaison
facteur etudie = Genotype_parent
Nombre d'indicatifs/enregistrement :
Mode
0
6
(0 = quantitatif, 1 = qualitatifs + transformation, 2 = qualitatif sans transformation)
Coefficient des variances - covariances des effets genetiques additifs dans composante d'AGC =
Coefficient des variances - covariances de dominance dans composante d'ASC =
Enregistrements numeros
1
a
2730
Numero du premier individu traite/enregistrement :
5 caracteres observes ,
5
1 , dernier =
1 , saut =
etudies
contraintes
lim. inf.
indicatif
3
1
2.5000E-01
lim. sup.
99999
66
1
2.5000E-01
indicatif
caractere
caractere
2
1
1
1
-99999.000
-99999.000
99999
99999.000
99999.000
Constante de correction pour d.l. d'erreur (donnees ajustees) =
Nombre de niveaux de l'aptitude generale (AGC)
:
73
12
Nombre de cellules retenues (reciproques confondues) :
63
Carres moyens & tests F sous l'hypothese d'effets fixes
Carres moyens de l'AGC du genotype Genotype_parent (
y 1
haut84
4.3732E+03
Tests F (
11
y 2
haut85
7.1542E+03
y 1
haut84
12.727
0.000%
et
2479
y 3
haut86
1.5705E+04
y 4
circ95
1.0888E+03
y 3
haut86
13.690
0.000%
y 4
circ95
12.501
0.000%
Carres moyens de l'aptitude specifique, ASC (
tests F (
y 2
haut85
8.7347E+02
51 et
y 1
haut84
1.452
2.062%
2479
y 3
haut86
2.4344E+03
y 5
attdio
7.4363E-01
y 5
attdio
7.177
0.000%
51
degres de liberte)
y 4
circ95
1.0608E+02
y 5
attdio
1.5961E-01
y 4
circ95
1.218
13.972%
y 5
attdio
1.540
0.868%
d.l.)
y 2
haut85
1.596
0.490%
y 3
haut86
2.122
0.001%
Carres moyens intra-cellule (
y 1
haut84
3.4361E+02
degres de liberte)
degres de liberte)
y 2
haut85
13.072
0.000%
y 1
haut84
4.9877E+02
11
y 2
haut85
5.4730E+02
2479
degres de liberte)
y 3
haut86
1.1473E+03
y 4
circ95
8.7092E+01
y 5
attdio
1.0361E-01
Esperance des 3 sommes de carres et coproduits (modele aleatoire) :
ve = variance intra, var(ASC) = variance d'Aptitude specifique, var(AGC) = variance d'Aptitude generale
S1 =
S2 =
S3 =
11 ve +
51 ve +
2479 ve
508.517 var(ASC) +
2057.875 var(ASC)
4321.907 var(AGC)
67
Correlations des effets d'Aptitude Generale a la Combinaison
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.950
0.908
0.630
-0.140
y 2
haut85
1.000
0.969
0.612
-0.050
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.487
0.030
1.000
0.535
1.000
correlations des effets d'Aptitude Specifiques a la Combinaison
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.988
0.692
-0.156
0.527
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.810
0.123
0.621
1.000
0.812
0.457
1.000
0.324
1.000
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.870
0.591
0.147
1.000
0.696
0.157
1.000
0.165
1.000
Correlations intra-cellule
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.928
0.763
0.500
0.139
Decomposition des variances-covariances selon le plan de croisements
Correlations des effets genetiques additifs
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.950
0.908
0.630
-0.140
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.487
0.030
1.000
0.535
1.000
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.810
0.123
0.621
1.000
0.812
0.457
1.000
0.324
1.000
1.000
0.969
0.612
-0.050
Correlations des effets de dominance
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.988
0.692
-0.156
0.527
Correlations des effets genetiques totaux
y
1
y
2
y
3
y
4
y
5
68
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
haut84
1.000
0.960
0.807
0.456
0.110
haut85
haut86
circ95
attdio
1.000
0.893
0.486
0.222
1.000
0.545
0.239
1.000
0.442
1.000
Pourcentage d'additivite dans la variance genetique
y 1
haut84
71.828
y 2
haut85
66.245
y 3
haut86
51.074
y 4
circ95
84.124
y 5
attdio
51.364
Correlations des effets d'environnement
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.925
0.763
0.509
0.136
1.000
0.870
0.605
0.134
1.000
0.715
0.144
1.000
0.146
1.000
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.873
0.587
0.145
1.000
0.683
0.157
1.000
0.180
1.000
Correlations phenotypiques
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.930
0.768
0.502
0.132
Matrices des Coefficients de prediction genetique (heritabilites sur la diagonale)
Coefficients de prediction genetique au sens strict
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
0.107
0.102
0.097
0.068
-0.011
y 2
haut85
0.108
0.104
0.066
-0.004
y 3
haut86
y 4
circ95
y 5
attdio
0.107
0.052
0.002
0.108
0.041
0.054
Coefficients de prediction genetique au sens large
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
0.149
0.149
0.142
0.063
0.014
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
0.163
0.165
0.070
0.029
0.209
0.089
0.036
0.128
0.051
0.106
69
Estimation des Aptitudes Generales et Specifiques a la Combinaison
Correlations entre interactivites (
y
y
y
y
y
1:
2:
3:
4:
5:
haut84
haut85
haut86
circ95
attdio
y 1
haut84
1.000
0.958
0.862
0.705
0.766
10
d.l.)
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.883
0.681
0.820
1.000
0.559
0.732
1.000
0.766
1.000
70
DUNCAN
Comparaison de moyennes par la methode de Newman & Keuls
Classement & comparaison de modalites de facteurs aux seuils 1%, 5% ou 10%
seuil de signification adopte =
5 %
(difference non significative entre modalites reliees par meme trait)
Effets ajustes issus de DIAL : genotype = Genotype_parent
Valeurs correspondant aux 12 Aptitudes Generales
Moyenne harmonique des effectifs/effet =
414.686
caractere numero :
rang
1
2
3
4
5
6
7
8
9
10
11
12
modalite
libelle
estimation effet
2
9
12
8
5
10
4
6
7
1
11
3
pere_2
mere_9
pere_12
pere_8
pere_5
pere_10
pere_4
pere_6
pere_7
pere_1
pere_11
pere_3
6.227
4.666
3.398
1.541
1.199
0.540
0.350
-0.431
-1.126
-3.304
-5.211
-5.857
caractere numero :
rang
1
2
3
4
5
6
7
8
9
10
11
12
libelle
estimation effet
2
9
12
4
8
10
5
7
6
1
11
3
pere_2
mere_9
pere_12
pere_4
pere_8
pere_10
pere_5
pere_7
pere_6
pere_1
pere_11
pere_3
8.517
7.248
5.114
1.626
1.100
0.009
-1.425
-1.576
-2.193
-2.846
-5.094
-7.247
1
modalite
2
1.028 |
1.147 |
0.789 ||
0.806 ||
0.866 ||
0.876 ||
0.802 ||
0.912
||
0.929
||
0.861
||
1.017
|
0.816
|
pere_2
1.149
erreur standard
1.298 |
1.448 |
0.996 |
1.012 |
1.017 |
1.105 |
1.093 ||
1.173 ||
1.151 ||
1.086 ||
1.284
||
1.030
|
3 (haut86), erreur standard moyenne/modalite :
libelle
0.910
erreur standard
2 (haut85), erreur standard moyenne/modalite :
modalite
caractere numero :
rang
1 (haut84), erreur standard moyenne/modalite :
estimation effet
erreur standard
12.481
1.879 |
1.663
71
2
3
4
5
6
7
8
9
10
11
12
12
9
4
8
10
6
11
7
5
1
3
pere_12
mere_9
pere_4
pere_8
pere_10
pere_6
pere_11
pere_7
pere_5
pere_1
pere_3
caractere numero :
rang
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1.442 ||
2.097 ||
1.466
||
1.473
||
1.600
||
1.667
|
1.859
|
1.698
|
1.583
|
1.572
|
1.491
4 (circ95), erreur standard moyenne/modalite :
modalite
libelle
estimation effet
9
6
2
10
1
4
12
8
7
5
11
3
mere_9
pere_6
pere_2
pere_10
pere_1
pere_4
pere_12
pere_8
pere_7
pere_5
pere_11
pere_3
2.802
2.025
1.960
1.610
0.940
-0.079
-0.253
-0.293
-1.017
-1.035
-2.441
-2.590
caractere numero :
rang
8.958
5.454
2.522
2.327
0.429
-2.308
-2.814
-2.988
-3.957
-4.276
-12.252
0.458
erreur standard
0.578
0.459
0.518
0.441
0.433
0.404
0.397
0.406
0.468
0.436
0.512
0.411
|
||
||
||
||
||
||
||
||
||
|
|
5 (attdio), erreur standard moyenne/modalite :
modalite
libelle
estimation effet
6
2
11
1
9
4
3
10
12
7
5
8
pere_6
pere_2
pere_11
pere_1
mere_9
pere_4
pere_3
pere_10
pere_12
pere_7
pere_5
pere_8
0.096
0.040
0.039
0.018
0.017
0.002
-0.005
-0.006
-0.019
-0.022
-0.049
-0.061
0.016
erreur standard
0.016
0.018
0.018
0.015
0.020
0.014
0.014
0.015
0.014
0.016
0.015
0.014
|
|
||
||
|||
|||
|||
|||
|||
||
|
Valeurs correspondant aux 12 Ecovalences du facteur Genotype_parent
Moyenne harmonique des effectifs/effet =
414.686
caractere numero :
rang
1
2
3
4
5
6
1 (haut84)
modalite
libelle
Ecovalence
9
11
10
5
8
6
mere_9
pere_11
pere_10
pere_5
pere_8
pere_6
15.588
12.958
11.497
10.101
8.842
8.618
Ecovalence cumulee
15.588
28.546
40.043
50.144
58.987
67.605
72
7
8
9
10
11
12
7
2
3
1
12
4
pere_7
pere_2
pere_3
pere_1
pere_12
pere_4
caractere numero :
rang
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
libelle
Ecovalence
9
10
11
7
6
5
8
2
1
3
4
12
mere_9
pere_10
pere_11
pere_7
pere_6
pere_5
pere_8
pere_2
pere_1
pere_3
pere_4
pere_12
15.411
13.960
12.124
8.799
8.426
8.386
8.149
7.640
5.074
4.816
3.630
3.584
rang
1
2
3
4
5
6
7
8
9
10
11
12
libelle
Ecovalence
11
10
9
7
6
5
2
8
12
3
4
1
pere_11
pere_10
mere_9
pere_7
pere_6
pere_5
pere_2
pere_8
pere_12
pere_3
pere_4
pere_1
13.797
12.434
11.275
8.991
8.977
8.748
7.387
7.288
7.193
4.972
4.536
4.402
15.411
29.371
41.495
50.294
58.721
67.107
75.256
82.896
87.970
92.786
96.416
100.000
Ecovalence cumulee
13.797
26.231
37.506
46.497
55.474
64.222
71.609
78.897
86.090
91.062
95.598
100.000
4 (circ95)
modalite
libelle
Ecovalence
9
11
7
6
2
4
10
12
5
3
8
1
mere_9
pere_11
pere_7
pere_6
pere_2
pere_4
pere_10
pere_12
pere_5
pere_3
pere_8
pere_1
23.931
11.711
10.905
10.565
7.291
6.529
6.022
5.768
5.692
4.402
3.748
3.435
caractere numero :
Ecovalence cumulee
3 (haut86)
modalite
caractere numero :
76.074
82.602
88.661
93.176
96.724
100.000
2 (haut85)
modalite
caractere numero :
rang
8.469
6.527
6.060
4.515
3.548
3.276
Ecovalence cumulee
23.931
35.642
46.548
57.113
64.404
70.933
76.955
82.723
88.415
92.817
96.565
100.000
5 (attdio)
73
rang
1
2
3
4
5
6
7
8
9
10
11
12
modalite
libelle
Ecovalence
9
11
6
2
10
7
1
8
5
4
12
3
mere_9
pere_11
pere_6
pere_2
pere_10
pere_7
pere_1
pere_8
pere_5
pere_4
pere_12
pere_3
13.878
12.748
10.659
9.757
9.594
8.345
8.032
6.852
5.714
5.525
4.593
4.303
Ecovalence cumulee
13.878
26.626
37.285
47.042
56.636
64.981
73.013
79.865
85.579
91.104
95.697
100.000
74
NORMEX : conversion d'un fichier de norme ANTAR a la norme ANTAR etendue
Le fichier moyfac a ete converti a la norme ANTAR etendue
-----------------------------------------------------------------------DISCRI : analyse factorielle discriminante
Nombre de dimensions (axes canoniques a contribution relative >0.0001) =
5
Valeurs propres (par ordre decroissant)
lambda 1
1.0239E-01
lambda 2
6.0456E-02
lambda 3
4.5386E-02
lambda 4
2.8708E-02
lambda 5
7.7359E-03
Moyenne des valeurs propres (indice de differenciation sur toutes les dimensions) :
Pourcentages de discrimination
lambda 1
41.848
lambda 2
24.708
lambda 3
18.549
lambda 4
11.733
lambda 5
3.162
lambda 4
96.838
lambda 5
100.000
Pourcentages de discrimination cumules
lambda 1
41.848
lambda 2
66.556
lambda 3
85.106
Vecteurs propres (dans l'ordre des valeurs propres)
VE
1
VE
2
VE
3
VE
4
VE
5
Y 1
Y 2
Y 3
Y 4
Y 5
haut84
haut85
haut86
circ95
attdio
-1.7966E-02 -3.0329E-03 5.7219E-02 -1.6869E-01 -9.8384E-01
Y 1
Y 2
Y 3
Y 4
Y 5
haut84
haut85
haut86
circ95
attdio
1.1478E-01 -4.1283E-02 3.8065E-03 8.3440E-02 -9.8901E-01
Y 1
Y 2
Y 3
Y 4
Y 5
haut84
haut85
haut86
circ95
attdio
-6.9120E-02 5.7581E-02 3.7774E-04 5.3289E-03 9.9593E-01
Y 1
Y 2
Y 3
Y 4
Y 5
haut84
haut85
haut86
circ95
attdio
-4.2558E-02 7.1019E-02 -2.7019E-02 4.1554E-03 -9.9619E-01
Y 1
Y 2
Y 3
Y 4
Y 5
haut84
haut85
haut86
circ95
attdio
1.6687E-02 1.3344E-02 -1.3061E-02 -3.0334E-02 9.9923E-01
Correlations entre axes canoniques (AC) &caracteres (Y)
(1) : Correlations entre termes d'erreur ((r)intra-population )
75
4.8936E-02
AC
AC
AC
AC
AC
Y 1
haut84
0.192
0.924
0.077
0.052
0.318
1
2
3
4
5
Y 2
haut85
0.253
0.847
0.389
0.185
0.183
Y 3
haut86
0.367
0.775
0.462
-0.162
-0.158
Y 4
circ95
-0.374
0.722
0.423
-0.109
-0.384
Y 5
attdio
-0.254
-0.009
0.557
-0.549
0.569
(2) : Correlations entre modalites du facteur discrimine
AC
AC
AC
1
2
3
Y 1
haut84
0.258
0.956
0.069
AC
AC
4
5
0.037
0.118
Y 2
haut85
0.336
0.864
0.344
0.130
0.067
Y 3
haut86
0.477
0.773
0.400
-0.112
-0.056
Y 4
circ95
-0.508
0.754
0.383
Y 5
attdio
-0.455
-0.013
0.665
-0.078
-0.143
-0.521
0.280
Y 4
circ95
-0.383
0.724
0.421
-0.107
-0.375
Y 5
attdio
-0.263
-0.009
0.561
-0.548
0.562
(3) : Correlations phenotypiques (totales)
AC
AC
AC
AC
AC
Y 1
haut84
0.196
0.925
0.076
0.051
0.311
1
2
3
4
5
Y 2
haut85
0.258
0.848
0.386
0.183
0.179
Y 3
haut86
0.374
0.775
0.459
-0.160
-0.154
tests Chi 2 de l'egalite des moyennes de population
(test de Wilks : cf Saporta 1990, p 423-424)
Test global sur tous les axes canoniques =
584.229 avec
55 d.l., seuil de signification =
tests partiels sur chacun des axes canoniques:
Numero de l'axe
1
2
3
4
5
test Chi 2
240.734
144.958
109.611
69.895
19.030
nbre de d.l.
15
13
11
9
7
probabilite
0.000%
0.000%
0.000%
0.000%
0.822%
76
0.000%
ILLY :
Calcul de fonctions discriminantes de populations (centroides)
avec reperage de ces populations.
Graphique par coordonnees sur les axes canoniques pris deux a deux
des points representatifs des diverses populations etudiees.
centroides (moyennes des variables canoniques par population)
population
1 , libelle
Axe 1
Axe 2
2.0136E-01 4.2339E-01
population
2 , libelle
Axe 1
Axe 2
7.6070E-01 1.1753E+00
population
3 , libelle
Axe 1
Axe 2
4.2142E-01 9.0622E-03
population
4 , libelle
Axe 1
Axe 2
6.9846E-01 6.1451E-01
population
5 , libelle
Axe 1
Axe 2
5.3250E-01 7.8332E-01
population
6 , libelle
Axe 1
Axe 2
0.0000E+00 7.4645E-01
population
7 , libelle
Axe 1
Axe 2
6.0141E-01 5.0189E-01
population
8 , libelle
Axe 1
Axe 2
7.6528E-01 8.1633E-01
population
9 , libelle
Axe 1
Axe 2
2.7113E-01 1.1149E+00
population
10 , libelle
Axe 1
Axe 2
3.0325E-01 8.4399E-01
population
11 , libelle
Axe 1
Axe 2
8.7573E-01 0.0000E+00
population
12 , libelle
Axe 1
Axe 2
1.0510E+00 8.5060E-01
= pere_1, nombre =
Axe 3
3.0559E-01
= pere_2, nombre =
Axe 3
3.3487E-01
= pere_3, nombre =
Axe 3
1.8477E-01
= pere_4, nombre =
Axe 3
2.9209E-01
= pere_5, nombre =
Axe 3
0.0000E+00
= pere_6, nombre =
Axe 3
2.2947E-01
= pere_7, nombre =
Axe 3
1.7860E-01
= pere_8, nombre =
Axe 3
1.1581E-01
= mere_9, nombre =
Axe 3
3.4856E-01
= pere_10, nombre =
Axe 3
1.8603E-01
= pere_11, nombre =
Axe 3
3.1143E-01
= pere_12, nombre =
Axe 3
2.6331E-01
464
325
516
534
458
413
398
529
261
448
332
552
Erreurs standard des centroides pour graphique :
(construction des ellipses de confiance)
population
Axe 1
0.060
population
Axe 1
1 , libelle = pere_12, nombre =
Axe 2
Axe 3
0.084
0.029
2 , libelle = pere_12, nombre =
Axe 2
Axe 3
464
325
77
0.072
0.101
population
3 , libelle
Axe 1
Axe 2
0.057
0.080
population
4 , libelle
Axe 1
Axe 2
0.056
0.079
population
5 , libelle
Axe 1
Axe 2
0.061
0.085
population
6 , libelle
Axe 1
Axe 2
0.064
0.089
population
7 , libelle
Axe 1
Axe 2
0.065
0.091
population
8 , libelle
Axe 1
Axe 2
0.057
0.079
population
9 , libelle
Axe 1
Axe 2
0.081
0.112
population
10 , libelle
Axe 1
Axe 2
0.061
0.086
population
11 , libelle
Axe 1
Axe 2
0.071
0.100
population
12 , libelle
Axe 1
Axe 2
0.055
0.077
0.035
= pere_12,
Axe 3
0.028
= pere_12,
Axe 3
0.027
= pere_12,
Axe 3
0.029
= pere_12,
Axe 3
0.031
= pere_12,
Axe 3
0.032
= pere_12,
Axe 3
0.027
= pere_12,
Axe 3
0.039
= pere_12,
Axe 3
0.030
= pere_12,
Axe 3
0.035
= pere_12,
Axe 3
0.027
nombre =
516
nombre =
534
nombre =
458
nombre =
413
nombre =
398
nombre =
529
nombre =
261
nombre =
448
nombre =
332
nombre =
552
-----------------------------------------------------Graphe des centroides par coordonnees : 52 interlignes*100 colonnes
(coordonnees positives ou nulles, car exprimees en deviation au minimum)
(* = point simple , # = point double ou multiple)
si plus de 10 points sur une ligne,les indicatifs concernent les 10 premiers.
Les autres indicatifs seront alors reportes a la page suivante.
78
Axe 1
populations dans l'ordre des points
^
| 1.0510E+00
+
pere_12*
12
|
|
|
|
|
|
|
|*pere_11
11
|
|
|
|
|
|
pere_8*
pere_2*
8
2
|
|
|
pere_4*
4
|
|
|
|
|
*pere_7
7
|
|
m
pere_5*
5
|
|
|
|
|
|*pere_3
3
|
|
|
|
|
pere_10*
10
|
|
mere_9*
9
|
|
|
|
*pere_1
1
|
|
|
|
|
|
|
|
+
pere_6*
6
| 0.0000E+00
|+------------------------------------------------------m-------------------------------------------+-->Axe 2
0.0000E+00
1.1753E+00
79
Axe 1
populations dans l'ordre des points
^
| 1.0510E+00
+
pere_12*
12
|
|
|
|
|
|
|
|
pere_11*
11
|
|
|
|
|
|
*pere_8
pere_2*
8
2
|
|
|
pere_4*
4
|
|
|
|
|
pere_7*
7
|
|
m*pere_5
5
|
|
|
|
|
|
pere_3*
3
|
|
|
|
|
pere_10*
10
|
|
mere_9*
9
|
|
|
|
pere_1*
1
|
|
|
|
|
|
|
|
+
pere_6*
6
| 0.0000E+00
|+----------------------------------------------------------------m---------------------------------+-->Axe 3
0.0000E+00
3.4856E-01
80
Axe 2
populations dans l'ordre des points
^
| 1.1753E+00
+
pere_2*
2
|
|
mere_9*
9
|
|
|
|
|
|
|
|
|
|
|
|
pere_10*
*pere_12
10
12
|
*pere_8
8
|
|*pere_5
5
|
pere_6*
6
|
|
|
m
|
|
pere_4*
4
|
|
|
|
|
pere_7*
7
|
|
|
|
pere_1*
1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
+
pere_3*
pere_11*
3
11
| 0.0000E+00
|+----------------------------------------------------------------m---------------------------------+-->Axe 3
0.0000E+00
3.4856E-01
81
Fichier illy007.gnu cree en vue des graphiques (
12 enregistrements)
82
MAHAL :
Classement & comparaison de centroides issus
d'Analyse Discriminante : seuils 1%, 5% ou 10%
comparaison par la methode de Newman & Keuls
Matrice des distances generalisees de Mahalanobis entre populations
Seuil de signification choisi pour tests = 5 %
Moyenne harmonique des effectifs/population :
Axe discriminant :
rang
1
2
3
4
5
6
7
8
9
10
11
12
rang
1
2
3
4
5
6
7
8
9
10
11
12
libelle
12
11
8
2
4
7
5
3
10
9
1
6
pere_12
pere_11
pere_8
pere_2
pere_4
pere_7
pere_5
pere_3
pere_10
mere_9
pere_1
pere_6
libelle
2
9
12
10
8
5
6
4
7
1
3
11
pere_2
mere_9
pere_12
pere_10
pere_8
pere_5
pere_6
pere_4
pere_7
pere_1
pere_3
pere_11
1
modalite
9
estimation effet
1.051
0.876
0.765
0.761
0.698
0.601
0.532
0.421
0.303
0.271
0.201
0.000
0.064
erreur standard
0.055 |
0.071 ||
0.057 ||
0.072 ||
0.056 ||
0.065
||
0.061
||
0.057
||
0.061
|
0.081
|
0.060
|
0.064
2, erreur standard d'un centroide :
modalite
Axe discriminant :
rang
1, erreur standard d'un centroide :
modalite
Axe discriminant :
414.686
estimation effet
1.175
1.115
0.851
0.844
0.816
0.783
0.746
0.615
0.502
0.423
0.009
0.000
0.089
erreur standard
0.101
0.112
0.077
0.086
0.079
0.085
0.089
0.079
0.091
0.084
0.080
0.100
|
||
||
||
||
||
|
||
||
|
|
|
3, erreur standard d'un centroide :
libelle
mere_9
estimation effet
0.349
0.031
erreur standard
0.039 |
83
2
3
4
5
6
7
8
9
10
11
12
2
11
1
4
12
6
10
3
7
8
5
pere_2
pere_11
pere_1
pere_4
pere_12
pere_6
pere_10
pere_3
pere_7
pere_8
pere_5
0.335
0.311
0.306
0.292
0.263
0.229
0.186
0.185
0.179
0.116
0.000
0.035
0.035
0.029
0.027
0.027
0.031
0.030
0.028
0.032
0.027
0.029
|
||
||
||
||
|||
||
||
||
|
Matrice des distances generalisees entre populations
L'espace de reference est celui des
3
ligne 1 = distance , ligne 2 = test F avec
premieres variables canoniques
p
1:pere_1
test F
probabilite (%)
p
2:pere_2
test F
probabilite (%)
p
3:pere_3
test F
probabilite (%)
p
4:pere_4
test F
probabilite (%)
p
5:pere_5
test F
probabilite (%)
p
6:pere_6
test F
probabilite (%)
p
7:pere_7
test F
probabilite (%)
p
8:pere_8
test F
probabilite (%)
p
9:mere_9
test F
probabilite (%)
p
10:pere_10
test F
probabilite (%)
p
11:pere_11
test F
probabilite (%)
p
12:pere_12
test F
probabilite (%)
p
1
pere_1
0.0000E+00
0.000
100.000
3.5886E-01
22.844
0.000
1.1744E-01
9.556
0.001
1.5761E-01
13.033
0.000
3.3863E-01
25.996
0.000
7.0226E-02
5.111
0.174
1.3695E-01
9.772
0.000
3.2530E-01
26.781
0.000
1.5280E-01
8.501
0.002
9.5782E-02
7.271
0.010
3.2335E-01
20.842
0.000
4.8658E-01
40.855
0.000
p
11
p
2
pere_2
0.0000E+00
0.000
100.000
5.3789E-01
35.724
0.000
1.0245E-01
6.894
0.017
3.5900E-01
22.731
0.000
4.2579E-01
25.793
0.000
2.1410E-01
12.758
0.000
1.5964E-01
10.704
0.000
1.4324E-01
6.906
0.017
2.1266E-01
13.341
0.000
4.2894E-01
23.463
0.000
9.4691E-02
6.452
0.030
p
12
3
& 2477
d.l., ligne 3 = seuil de signification %
p
3
pere_3
p
4
pere_4
p
5
pere_5
p
6
pere_6
p
7
pere_7
p
8
pere_8
0.0000E+00
0.000
100.000
1.8567E-01
16.228
0.000
2.7516E-01
22.236
0.000
2.7521E-01
21.027
0.000
9.3045E-02
6.963
0.016
2.7985E-01
24.347
0.000
4.5226E-01
26.109
0.000
2.2008E-01
17.578
0.000
1.6229E-01
10.920
0.000
4.6493E-01
41.298
0.000
0.0000E+00
0.000
100.000
2.3913E-01
19.636
0.000
3.0347E-01
23.539
0.000
4.1752E-02
3.171
2.313
9.3024E-02
8.234
0.003
1.9201E-01
11.212
0.000
1.3656E-01
11.081
0.000
1.3426E-01
9.154
0.001
9.2459E-02
8.358
0.003
0.0000E+00
0.000
100.000
3.0021E-01
21.714
0.000
1.0696E-01
7.586
0.007
6.6033E-02
5.399
0.119
3.7880E-01
20.976
0.000
1.1906E-01
8.981
0.001
4.9957E-01
32.026
0.000
3.3431E-01
27.872
0.000
0.0000E+00
0.000
100.000
2.3845E-01
16.097
0.000
3.8006E-01
29.359
0.000
1.2030E-01
6.408
0.032
6.1982E-02
4.436
0.427
6.3944E-01
39.198
0.000
6.5900E-01
51.853
0.000
0.0000E+00
0.000
100.000
5.5814E-02
4.222
0.567
2.5117E-01
13.186
0.000
8.8246E-02
6.195
0.042
1.6531E-01
9.966
0.000
1.7441E-01
13.434
0.000
0.0000E+00
0.000
100.000
3.0740E-01
17.894
0.000
1.3878E-01
11.212
0.000
3.0577E-01
20.774
0.000
1.0321E-01
9.286
0.001
84
p
9
mere_9
p
10
pere_10
0.0000E+00
0.000
100.000
8.9227E-02 0.0000E+00
4.901
0.000
0.230
100.000
5.9718E-01 4.4963E-01
29.064
28.557
0.000
0.000
3.9890E-01 3.4545E-01
23.544
28.453
0.000
0.000
p
11:pere_11
test F
probabilite (%)
p
12:pere_12
test F
probabilite (%)
pere_11
pere_12
0.0000E+00
0.000
100.000
2.4381E-01 0.0000E+00
16.835
0.000
0.000
100.000
Distance moyenne entre population = 2.5002E-01
Indice de differenciation = 3.8198E-02
NORMEX : conversion d'un fichier de norme ANTAR a la norme ANTAR etendue
Le fichier dista a ete converti a la norme ANTAR etendue
-----------------------------------------------------------------------Fichier DISTA - distances - maintenu et recopie sous le nom de mahal007.gnu
85
PRADET : analyse des constellations
sur matrice de similarite issue de DAG, MAHAL, DAUGEY ou EUCLID
Matrice de similarite issue de MAHAL
Option MATSUP =
0 , GRSUP =
0 , ZOOM =
3
Algorithme d'agglomeration =
0
(0 = lien simple , 1 = lien moyen , 2 = lien complet)
Nombre total de groupes formes :
13
Dendrogramme (similarite au .01 plus proche si <0.995 et a 0.99 sinon)
---------------------------------------------------------------------1.000
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+
p
1 :
p
10 :
p
6 :
p
4 :
p
7 :
p
8 :
p
5 :
p
9 :
p
12 :
p
3 :
p
2 :
p
11 :
echelle :
pere_1*====>+----------------*
|
pere_10*====>+-----------*
*
|
|
pere_6*====>+-----------*----*---------*
|
pere_4*====>+*
*
|
|
pere_7*====>+*-------*
*
|
|
pere_8*====>+--------*-----*
*
|
|
pere_5*====>+--------------*-----------*
|
mere_9*====>+--------------------------*-*
|
pere_12*====>+----------------------------*
|
pere_3*====>+----------------------------*-*
|
pere_2*====>+------------------------------*----------------*
|
pere_11*====>+-----------------------------------------------*------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+
1.000
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
86
Programme JBMAT : E.S. et intervalles de confiance d'elements de matrices
Methode utilisee pour calcul des E.S. = JACKKNIFE
Seuil choisi pour les intervalles de confiance =
Coefficient des E.S. calcule =
95.000%
1.9600
Pour les intervalles de confiance, ligne 1 = limite superieure, ligne 2 = limite inferieure
nombre de degres de liberte pour les E.S. =
2729
Parametres et tests de la matrice numero
1
Correlations des effets d'Aptitude Generale a la Combinaison
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
1.000
0.000
0.000
100.000
0.950
0.016
60.692
0.000
0.908
0.039
23.032
0.000
0.630
0.098
6.419
0.000
-0.140
0.200
0.700
50.899
87
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.000
0.000
100.000
0.969
0.020
49.519
0.000
0.612
0.095
6.414
0.000
-0.050
0.196
0.257
79.296
1.000
0.000
0.000
100.000
0.487
0.106
4.603
0.001
0.030
0.195
0.155
87.156
1.000
0.000
0.000
100.000
0.535
0.160
3.343
0.100
1.000
0.000
0.000
100.000
Intervalles de confiance de la matrice
1
Correlations des effets d'Aptitude Generale a la Combinaison
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y 1
haut84
-5.000
-5.000
0.980
0.919
0.985
0.831
0.822
0.437
y 2
haut85
y 3
haut86
y 4
circ95
-5.000
-5.000
1.000
0.931
0.800
0.425
-5.000
-5.000
0.694
0.280
-5.000
-5.000
87
y 5
attdio
87
y
5 :
attdio
0.252
-0.531
0.333
-0.434
0.412
-0.352
0.848
0.221
-5.000
-5.000
Parametres et tests de la matrice numero
2
Correlations des effets d'Aptitude Specifique a la Combinaison
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
1.000
0.000
0.000
100.000
0.988
0.050
19.803
0.000
0.692
0.204
3.395
0.085
-0.156
0.991
0.158
86.950
0.527
0.485
1.086
27.721
88
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.000
0.000
100.000
0.810
0.112
7.202
0.000
0.123
0.718
0.172
85.820
0.621
0.432
1.435
14.716
1.000
0.000
0.000
100.000
0.812
0.331
2.453
1.368
0.457
0.354
1.292
19.327
1.000
0.000
0.000
100.000
0.324
0.739
0.439
66.511
1.000
0.000
0.000
100.000
Intervalles de confiance de la matrice
2
Correlations des effets d'Aptitude Specifique a la Combinaison
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
y 1
haut84
-5.000
-5.000
1.000
0.890
1.000
0.293
1.000
-1.000
1.000
-0.424
88
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
-5.000
-5.000
1.000
0.589
1.000
-1.000
1.000
-0.227
-5.000
-5.000
1.000
0.163
1.000
-0.237
-5.000
-5.000
1.000
-1.000
-5.000
-5.000
Parametres et tests de la matrice numero
3
Correlations des effets genetiques additifs
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
y 1
haut84
1.000
0.000
0.000
100.000
0.950
0.016
60.692
y 2
haut85
y 3
haut86
88
y 4
circ95
y 5
attdio
1.000
0.000
0.000
88
Signif. (%) :
3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y
0.000
0.908
0.039
23.032
0.000
0.630
0.098
6.419
0.000
-0.140
0.200
0.700
50.899
100.000
0.969
0.020
49.519
0.000
0.612
0.095
6.414
0.000
-0.050
0.196
0.257
79.296
1.000
0.000
0.000
100.000
0.487
0.106
4.603
0.001
0.030
0.195
0.155
87.156
1.000
0.000
0.000
100.000
0.535
0.160
3.343
0.100
1.000
0.000
0.000
100.000
Intervalles de confiance de la matrice
3
Correlations des effets genetiques additifs
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
y 1
haut84
-5.000
-5.000
0.980
0.919
0.985
y 2
haut85
y 3
haut86
-5.000
-5.000
1.000
-5.000
0.831
0.822
0.437
0.252
-0.531
0.931
0.800
0.425
0.333
-0.434
-5.000
0.694
0.280
0.412
-0.352
Parametres et tests de la matrice numero
Correlations des effets de dominance
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
89
y 1
haut84
1.000
0.000
0.000
100.000
0.988
0.050
19.803
0.000
0.692
0.204
3.395
0.085
-0.156
0.991
0.158
86.950
0.527
0.485
1.086
27.721
y 4
circ95
y 5
attdio
-5.000
-5.000
0.848
0.221
-5.000
-5.000
4
89
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.000
0.000
100.000
0.810
0.112
7.202
0.000
0.123
0.718
0.172
85.820
0.621
0.432
1.435
14.716
1.000
0.000
0.000
100.000
0.812
0.331
2.453
1.368
0.457
0.354
1.292
19.327
1.000
0.000
0.000
100.000
0.324
0.739
0.439
66.511
1.000
0.000
0.000
100.000
89
Intervalles de confiance de la matrice
Correlations des effets de dominance
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
y 1
haut84
-5.000
-5.000
1.000
0.890
1.000
0.293
1.000
-1.000
1.000
-0.424
4
90
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
-5.000
-5.000
1.000
0.589
1.000
-1.000
1.000
-0.227
-5.000
-5.000
1.000
0.163
1.000
-0.237
-5.000
-5.000
1.000
-1.000
-5.000
-5.000
Parametres et tests de la matrice numero
Correlations des effets genetiques totaux
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
1.000
0.000
0.000
100.000
0.960
0.018
54.091
0.000
0.807
0.065
12.486
0.000
0.456
0.146
3.121
0.200
0.110
0.204
0.539
59.663
5
90
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.000
0.000
100.000
0.893
0.036
24.737
0.000
0.486
0.132
3.672
0.034
0.222
0.193
1.152
24.807
1.000
0.000
0.000
100.000
0.545
0.110
4.957
0.000
0.239
0.183
1.302
18.981
1.000
0.000
0.000
100.000
0.442
0.208
2.124
3.184
1.000
0.000
0.000
100.000
Intervalles de confiance de la matrice
5
Correlations des effets genetiques totaux
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y 1
haut84
-5.000
-5.000
0.994
0.925
0.934
0.680
0.743
0.170
90
y 2
haut85
y 3
haut86
y 4
circ95
-5.000
-5.000
0.964
0.822
0.745
0.227
-5.000
-5.000
0.761
0.330
-5.000
-5.000
y 5
attdio
90
y
5 :
attdio
0.510
-0.290
0.600
-0.156
0.598
-0.121
Parametres et tests de la matrice numero
Correlations des effets d'environnement
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
1.000
0.000
0.000
100.000
0.925
0.006
161.913
0.000
0.763
0.016
48.250
0.000
0.509
0.025
20.007
0.000
0.136
0.032
4.183
0.006
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
6
91
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.000
0.000
100.000
0.870
0.010
91.075
0.000
0.605
0.022
26.981
0.000
0.134
0.033
4.119
0.007
1.000
0.000
0.000
100.000
0.715
0.019
38.440
0.000
0.144
0.034
4.208
0.005
1.000
0.000
0.000
100.000
0.146
0.031
4.671
0.001
1.000
0.000
0.000
100.000
6
91
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
-5.000
-5.000
0.889
0.852
0.649
0.561
0.198
0.070
-5.000
-5.000
0.752
0.679
0.211
0.077
-5.000
-5.000
0.207
0.085
-5.000
-5.000
Parametres et tests de la matrice numero
Correlations phenotypiques
y 1
haut84
1.000
0.000
0.000
100.000
0.930
0.004
-5.000
-5.000
y 2
haut85
Intervalles de confiance de la matrice
Correlations des effets d'environnement
y 1
haut84
-5.000
-5.000
0.936
0.914
0.794
0.732
0.559
0.459
0.199
0.072
0.849
0.034
y 2
haut85
7
y 3
haut86
91
y 4
circ95
y 5
attdio
1.000
0.000
91
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
251.673
0.000
0.768
0.009
90.085
0.000
0.502
0.015
33.530
0.000
0.132
0.021
6.225
0.000
0.000
100.000
0.873
0.005
175.034
0.000
0.587
0.013
44.177
0.000
0.145
0.021
6.930
0.000
Intervalles de confiance de la matrice
Correlations phenotypiques
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
y 1
haut84
-5.000
-5.000
0.937
0.923
0.785
0.752
0.531
0.472
0.174
0.091
1.000
0.000
0.000
100.000
0.683
0.011
59.832
0.000
0.157
0.020
7.669
0.000
1.000
0.000
0.000
100.000
0.180
0.020
8.876
0.000
1.000
0.000
0.000
100.000
7
92
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
-5.000
-5.000
0.883
0.863
0.613
0.561
0.187
0.104
-5.000
-5.000
0.706
0.661
0.197
0.117
-5.000
-5.000
0.220
0.140
-5.000
-5.000
Parametres et tests de la matrice numero
8
Coefficients de prediction genetique au sens strict
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
0.107
0.021
5.121
0.000
0.102
0.021
4.941
0.000
0.097
0.019
5.043
0.000
0.068
0.018
3.858
0.018
-0.011
0.015
0.722
52.271
92
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
0.108
0.022
4.996
0.000
0.104
0.020
5.095
0.000
0.066
0.018
3.638
0.038
-0.004
0.015
0.261
79.039
0.107
0.022
4.946
0.000
0.052
0.018
2.878
0.415
0.002
0.015
0.154
87.237
0.108
0.022
4.958
0.000
0.041
0.015
2.670
0.754
0.054
0.018
3.037
0.258
92
Intervalles de confiance de la matrice
8
Coefficients de prediction genetique au sens strict
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
y 1
haut84
0.148
0.066
0.143
0.062
0.135
0.059
0.102
0.033
0.018
-0.040
93
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
0.150
0.066
0.144
0.064
0.102
0.030
0.025
-0.033
0.149
0.065
0.088
0.017
0.032
-0.027
0.150
0.065
0.071
0.011
0.089
0.019
Parametres et tests de la matrice numero
9
Coefficients de prediction genetique au sens large
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
0.149
0.039
3.820
0.021
0.149
0.039
3.866
0.018
0.142
0.036
3.940
0.014
0.063
0.029
2.181
2.763
0.014
0.027
0.520
60.978
93
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
0.163
0.041
4.028
0.010
0.165
0.038
4.315
0.004
0.070
0.030
2.328
1.899
0.029
0.027
1.073
28.348
0.209
0.041
5.090
0.000
0.089
0.033
2.699
0.695
0.036
0.029
1.225
21.827
0.128
0.035
3.655
0.036
0.051
0.027
1.904
5.387
0.106
0.038
2.767
0.572
Intervalles de confiance de la matrice
9
Coefficients de prediction genetique au sens large
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y 1
haut84
0.225
0.072
0.225
0.074
0.213
0.072
0.120
0.006
93
y 2
haut85
y 3
haut86
y 4
circ95
0.243
0.084
0.240
0.090
0.129
0.011
0.290
0.129
0.154
0.024
0.197
0.059
y 5
attdio
93
y
5 :
attdio
0.066
-0.038
0.082
-0.024
0.092
-0.021
Parametres et tests de la matrice numero
Correlations entre interactivites
y
1 :
haut84
E. standard :
Test t :
Signif. (%) :
y 2 :
haut85
E. standard :
Test t :
Signif. (%) :
y 3 :
haut86
E. standard :
Test t :
Signif. (%) :
y 4 :
circ95
E. standard :
Test t :
Signif. (%) :
y 5 :
attdio
E. standard :
Test t :
Signif. (%) :
y 1
haut84
1.000
0.000
0.000
100.000
0.958
0.064
14.857
0.000
0.862
0.253
3.401
0.083
0.705
0.391
1.805
6.764
0.766
0.412
1.859
5.979
y
1 :
haut84
y
2 :
haut85
y
3 :
haut86
y
4 :
circ95
y
5 :
attdio
0.181
0.031
10
94
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
1.000
0.000
0.000
100.000
0.883
-5.000
0.000
100.000
0.681
0.331
2.059
3.734
0.820
0.315
2.605
0.903
1.000
0.000
0.000
100.000
0.559
0.389
1.438
14.646
0.732
0.372
1.968
4.646
1.000
0.000
0.000
100.000
0.766
0.460
1.666
9.161
1.000
0.000
0.000
100.000
Intervalles de confiance de la matrice
Correlations entre interactivites
y 1
haut84
-5.000
-5.000
1.000
0.832
1.000
0.365
1.000
-0.061
1.000
-0.042
0.104
-0.001
10
94
y 2
haut85
y 3
haut86
y 4
circ95
y 5
attdio
-5.000
-5.000
-5.000
-5.000
1.000
0.033
1.000
0.203
-5.000
-5.000
1.000
-0.203
1.000
0.003
-5.000
-5.000
1.000
-0.135
-5.000
-5.000
Compteur de series initialise a 0 sur INDEX
94
Comments about results and concluding remarks
Processing of the survival rate (d4423 file) will be done in the tutorial003 document if my institute, INRA, will decide allowing me to carry on this
teaching task. If these favourable conditions will be met, a new choice of parameters will illustrate how the factor’s labels are used in the MANOVA
outputs.
Pages 71-74 show the outputs of DUNCAN where alphanumeric labels are left-justified. The GCA (General combining abilities) concern each parent
as mother and father. The choice have been (arbitrarily) to label for parents registered according to DIAL’s parameters as “column” factor. As it
appened that we declared father as “column”, the labels are “père_xx”. But, we remark that “père_9” is replaced by “mère_9”. It is not a mistake!
DIAL and REDIAL outputs are arranged in such a fashion, that a “sex” which is absent is replaced by the corresponding modality of the other sex: here,
“father 9” is absent and was replaced as label by “mother 9”. These labels are left-justified as the corresponding ones of MAHAL (pages 83-84). The
same is true for the graphs of ILLY (pages 79-81) where this format saves place between the labels and the “*” giving the position of GCA
corresponding to the 12 parents. This still holds for the dendrogram of PRADET (page 89). For the Mahalanobis distances, the choice was “mixed”,
living the labels “centered” for columns (like for trait’s labels) and left-justified for rows.
Another remark concerns the outputs specific of reiterations, due a specialized program, JBMAT, directly managed by JBSTAR. In “format 3” output,
we have all the results for characterising the estimated parameters. The first matrix tests for the “null hypothesis” that each parameter may be
considered as having a “0” true value. The second one holds for confidence interval at the selected level of confidence. As the confidence interval
concern the true values and not their estimates, the computed ranges are truncated at the values corresponding to their definition. That means: [-1; +1]
for a correlation coefficient or a CPG between two different traits or [0; 1] for an heritability (CPG of a trait by itself).
Lastly, you can see that DIOGENE automatically generates “.gnu” files, for instance, page 82 (illy007.gnu). Generally, the rule for generating the
names is: [program name]xxx.gnu, where “xxx” is a sequence number. The sequence number may be reinitialized to “0” by using the RESET utility
(but, be careful, all these files would be destroyed in the current directory). The “.gnu” files are EAN files (therefore, their edition is easy with utilities
like LIRE, LECTURE etc...), which contain all data for obtaining graphics using Gnuplot or Excel, with, of course, a better quality than those edited in
alphanumeric mode! To transform these files in a usable format for these two programs, use the TOTEMG converter (only one parameter: the name of
the file to be converted). The explorer integrated with DIOGENE can also be used to run Gnuplot. The converted files will be named: [program
name].xxx.dat. Therefore, their correspondence to “.gnu” files is obvious.
If this teaching work may be continued (according to the decision of INRA), a specialised tutorial will be devoted to “advanced” uses of re-sampling,
as for generating model populations or estimating confidence intervals when normality of distribution of parameter estimates cannot be assumed. On
the other hand, again if the undertaken work has to continue, “notices” will be devoted to theoretical aspects completely discarded by the tutorials. Up
to now, only the “Papadakis++” notice is ready. The following ones would have the same kinds of aim and presentation.
Montpellier April 7/2004
Ph. Baradat
95