diff --git a/index.Rmd b/index.Rmd
index 5a21acd..6556370 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -103,7 +103,7 @@ L'AFE vise à explorer la structure sous-jacente d'une base de donnée en identi
Cette notion de facteur latent est au coeur de l'analyse factorielle exploratoire. En effet l'idée centrale de cette méthode est que nos variables mesurées sont influencées par ces facteurs latents, et que ces facteurs vont expliquer la structure des relations des variables entre elles. Chaque facteur latent est associé à un groupe de variables qui partagent une variance commune. En d'autres termes, le facteur latent "capture" l'information partagée par un ensemble de variables.
Supposons que nous avons une base de données constituée de variables mesurant un ensemble de critères observables. Par exemple, lors d'un match de rugby nous avons collecté le nombre de mètres parcourus, de points marqués, du nombre de passes, de plaquages... On pourrait postuler l'existence de facteurs latents qu'on pourrait appeler "performance" et "vision de jeu". Ces deux facteurs latents ne peuvent pas être mesurés directement, mais peuvent être postulés pour tenter d'expliquer la variation commune de nos variables observées/mesurées.
-Notre objectif lorsque l'on réalise une AFE sera d'identifier le nombre et la nature de ces facteurs latents et de comprendre comment ils sont associés et reliés aux variables mesurées. Cette méthode va donc permettre de faire émerger un pattern de relations entre ces variables. La structuration des facteurs obtenus ne sera peut-être pas exactement celle envisagée au départ, mais l'AFE, bien que mentionnée comme exploratoire, nécessite d'avoir des hypothèses concernant l’existence des facteurs latents précisément testés.
+Notre objectif lorsque l'on réalise une AFE sera d'identifier le nombre et la nature de ces facteurs latents et de comprendre comment ils sont associés et reliés aux variables mesurées. Cette méthode va donc permettre de faire émerger un pattern de relations entre ces variables. La structuration des facteurs obtenus ne sera peut-être d'ailleurs pas exactement celle envisagée par les hypothèses de départ, c'est en cela que l'AFE est exploratoire.
Ainsi, si on cherche à explorer et à identifier des dimensions explicatives sous-jacentes, qui influencent nos variables, comme c'est souvent le cas en SHS, l'AFE s'avérera être un bon choix.
Autre avantage important de l'analyse factorielle exploratoire, c'est qu'elle peut être associée à une analyse factorielle confirmatoire afin d'étayer les résultats obtenus. Nous reviendrons plus tard dans cet article sur l'analyse factorielle confirmatoire.
@@ -118,7 +118,7 @@ En réalité, les différences entre ces deux méthodes sont importantes, bien q
ACP et AFE ont comme objectif commun la réduction de l'information, mais elles ne vont pas du tout opérer de la même manière.
- - l'ACP : l'objectif est de réduire la dimensionnalité des données en utilisant les composantes principales. L'ACP va transformer les variables observées en un ensemble de variables non corrélées, que l'on appelle composantes principales. Dans l'ACP nous n'allons pas rechercher des facteurs latents.
+ - l'ACP : l'objectif est de réduire la dimensionnalité des données en utilisant les composantes principales. L'ACP va transformer les variables observées en un ensemble de variables non corrélées, que l'on appelle composantes principales.
La composante principale est une combinaison linéaire des variables originales qui capture le plus possible de variance présente dans les données. Ces composantes principales sont classées par ordre décroissant de l'importance de la variance qu'elles capturent. Tout l'enjeu de l'ACP est de définir des composantes principales qui permettent de réduire la dimensionnalité des données tout en conservant l'information essentielle contenue dans les variables d'origine. Les composantes principales étant des variables non corrélées entre elles, cela simplifie l'interprétation des relations entre les observations. Pour résumer, l'objectif principal de l'ACP est de synthétiser nos données en composantes principales en tentant de conserver le maximum de variance des variables de notre base de données.
- L'AFE : vise à explorer la structure sous-jacente des données en identifiant des facteurs latents qui expliquent les relations entre les variables observées. L'objectif est de réduire la dimensionnalité des données en se centrant sur la variance partagée entre les variables et non sur l'ensemble des données. Dans le cadre de l'AFE, une partie de la variance des données ne sera pas retenue dans le modèle. L'objectif n'étant pas de conserver le maximum de variance de notre jeu de données, mais bien de se centrer sur la variance partagée entre les différentes variables.
@@ -165,7 +165,7 @@ La subjectivité de l'interprétation peut aussi être une limite, notamment sur
En SHS aujourd'hui l'AFE est surtout utilisée en psychologie, et ce depuis de nombreuses années.
La validation des tests psychométriques constitue un exemple classique de l’utilisation de l'AFE et de son utilisation conjointe avec l'Analyse factorielle confirmatoire (AFC). Afin de réaliser des tests standardisés pour évaluer des processus psychologiques, les psychologues ont recours à ce couple d’analyses factorielles, en réalisant une analyse factorielle exploratoire sur un premier échantillon et une analyse factorielle confirmatoire sur un second échantillon (ayant tous 2 les mêmes caractéristiques).
-Les psychologues se sont tournés vers ces techniques de réduction des dimensions car, pour la plupart de leurs études, ils souhaitent se centrer sur la variance partagée entre chaque item, plutôt que de chercher à conserver la variance totale d'un ensemble de variables. Le processus méthodologique alors suivi permet de faire émerger une structure factorielle sans nécessairement la totalité des variables présentes. Cette structure factorielle permet d'observer comment les variables du jeu de données rendent compte des facteurs latents qui les sous-tendent. L'objectif finalement est de savoir *"Est-ce que je mesure bien ce que je pense mesurer ?"*. En d'autres termes, est-ce que les questions posées aux répondants sont adaptées pour accéder à une dimension latente non appréhendable directement.
+Les psychologues se sont tournés vers ces techniques de réduction des dimensions car, pour la plupart de leurs études, ils souhaitent se centrer sur la variance partagée entre chaque item, plutôt que de chercher à conserver la variance totale d'un ensemble de variables. Le processus méthodologique alors suivi permet de faire émerger une structure factorielle sans nécessairement la totalité des variables présentes. Cette structure factorielle permet d'observer comment les variables du jeu de données rendent compte des facteurs latents qui les sous-tendent. L'objectif est de savoir *"Est-ce que je mesure bien ce que je pense mesurer ?"*. En d'autres termes, est-ce que les questions posées aux répondants sont adaptées pour accéder à une dimension latente non appréhendable directement.
<div class="alert alert-danger" role="warning">
diff --git a/index.html b/index.html
index ecbe8d7..5edc070 100644
--- a/index.html
+++ b/index.html
@@ -1831,10 +1831,9 @@ <h1><span class="header-section-number">2</span> L’ AFE pour quoi
nombre et la nature de ces facteurs latents et de comprendre comment ils
sont associés et reliés aux variables mesurées. Cette méthode va donc
permettre de faire émerger un pattern de relations entre ces variables.
-La structuration des facteurs obtenus ne sera peut-être pas exactement
-celle envisagée au départ, mais l’AFE, bien que mentionnée comme
-exploratoire, nécessite d’avoir des hypothèses concernant l’existence
-des facteurs latents précisément testés.</p>
+La structuration des facteurs obtenus ne sera peut-être d’ailleurs pas
+exactement celle envisagée par les hypothèses de départ, c’est en cela
+que l’AFE est exploratoire.</p>
<p>Ainsi, si on cherche à explorer et à identifier des dimensions
explicatives sous-jacentes, qui influencent nos variables, comme c’est
souvent le cas en SHS, l’AFE s’avérera être un bon choix. Autre avantage
@@ -1860,18 +1859,17 @@ <h2><span class="header-section-number">2.1</span> ACP et AFE : quelles
<li>l’ACP : l’objectif est de réduire la dimensionnalité des données en
utilisant les composantes principales. L’ACP va transformer les
variables observées en un ensemble de variables non corrélées, que l’on
-appelle composantes principales. Dans l’ACP nous n’allons pas rechercher
-des facteurs latents. La composante principale est une combinaison
-linéaire des variables originales qui capture le plus possible de
-variance présente dans les données. Ces composantes principales sont
-classées par ordre décroissant de l’importance de la variance qu’elles
-capturent. Tout l’enjeu de l’ACP est de définir des composantes
-principales qui permettent de réduire la dimensionnalité des données
-tout en conservant l’information essentielle contenue dans les variables
-d’origine. Les composantes principales étant des variables non corrélées
-entre elles, cela simplifie l’interprétation des relations entre les
-observations. Pour résumer, l’objectif principal de l’ACP est de
-synthétiser nos données en composantes principales en tentant de
+appelle composantes principales. La composante principale est une
+combinaison linéaire des variables originales qui capture le plus
+possible de variance présente dans les données. Ces composantes
+principales sont classées par ordre décroissant de l’importance de la
+variance qu’elles capturent. Tout l’enjeu de l’ACP est de définir des
+composantes principales qui permettent de réduire la dimensionnalité des
+données tout en conservant l’information essentielle contenue dans les
+variables d’origine. Les composantes principales étant des variables non
+corrélées entre elles, cela simplifie l’interprétation des relations
+entre les observations. Pour résumer, l’objectif principal de l’ACP est
+de synthétiser nos données en composantes principales en tentant de
conserver le maximum de variance des variables de notre base de
données.</li>
<li>L’AFE : vise à explorer la structure sous-jacente des données en
@@ -2012,9 +2010,9 @@ <h2><span class="header-section-number">2.3</span> L’AFE aujourd’hui en
structure factorielle sans nécessairement la totalité des variables
présentes. Cette structure factorielle permet d’observer comment les
variables du jeu de données rendent compte des facteurs latents qui les
-sous-tendent. L’objectif finalement est de savoir <em>“Est-ce que je
-mesure bien ce que je pense mesurer ?”</em>. En d’autres termes, est-ce
-que les questions posées aux répondants sont adaptées pour accéder à une
+sous-tendent. L’objectif est de savoir <em>“Est-ce que je mesure bien ce
+que je pense mesurer ?”</em>. En d’autres termes, est-ce que les
+questions posées aux répondants sont adaptées pour accéder à une
dimension latente non appréhendable directement.</p>
<div class="alert alert-danger" role="warning">
<p>Un point important nous semble à présent à souligner, sur
@@ -2968,7 +2966,7 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
R2</p>
<div class="sourceCode" id="cb51"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" tabindex="-1"></a><span class="co">#exemple pour le modèle à 1 facteur</span></span>
<span id="cb51-2"><a href="#cb51-2" tabindex="-1"></a>res_fact1immo</span></code></pre></div>
-<pre class="bg-warning"><code>lavaan 0.6-19 ended normally after 18 iterations
+<pre class="bg-warning"><code>lavaan 0.6-19 ended normally after 22 iterations
Estimator ML
Optimization method NLMINB
@@ -2978,57 +2976,57 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
Model Test User Model:
Standard Scaled
- Test Statistic 883.766 507.390
+ Test Statistic 833.595 493.336
Degrees of freedom 20 20
P-value (Chi-square) 0.000 0.000
- Scaling correction factor 1.742
+ Scaling correction factor 1.690
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
- Test statistic 2932.472 1394.922
+ Test statistic 2969.556 1383.130
Degrees of freedom 28 28
P-value 0.000 0.000
- Scaling correction factor 2.102
+ Scaling correction factor 2.147
User Model versus Baseline Model:
- Comparative Fit Index (CFI) 0.703 0.643
- Tucker-Lewis Index (TLI) 0.584 0.501
+ Comparative Fit Index (CFI) 0.723 0.651
+ Tucker-Lewis Index (TLI) 0.613 0.511
- Robust Comparative Fit Index (CFI) 0.705
- Robust Tucker-Lewis Index (TLI) 0.586
+ Robust Comparative Fit Index (CFI) 0.725
+ Robust Tucker-Lewis Index (TLI) 0.615
Loglikelihood and Information Criteria:
- Loglikelihood user model (H0) -5768.420 -5768.420
- Scaling correction factor 4.117
+ Loglikelihood user model (H0) -5780.141 -5780.141
+ Scaling correction factor 4.097
for the MLR correction
- Loglikelihood unrestricted model (H1) -5326.537 -5326.537
- Scaling correction factor 2.798
+ Loglikelihood unrestricted model (H1) -5363.343 -5363.343
+ Scaling correction factor 2.759
for the MLR correction
- Akaike (AIC) 11568.839 11568.839
- Bayesian (BIC) 11639.481 11639.481
- Sample-size adjusted Bayesian (SABIC) 11588.684 11588.684
+ Akaike (AIC) 11592.281 11592.281
+ Bayesian (BIC) 11662.923 11662.923
+ Sample-size adjusted Bayesian (SABIC) 11612.126 11612.126
Root Mean Square Error of Approximation:
- RMSEA 0.266 0.200
- 90 Percent confidence interval - lower 0.251 0.188
- 90 Percent confidence interval - upper 0.281 0.211
+ RMSEA 0.258 0.197
+ 90 Percent confidence interval - lower 0.243 0.185
+ 90 Percent confidence interval - upper 0.273 0.208
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
- Robust RMSEA 0.264
- 90 Percent confidence interval - lower 0.244
- 90 Percent confidence interval - upper 0.284
+ Robust RMSEA 0.256
+ 90 Percent confidence interval - lower 0.237
+ 90 Percent confidence interval - upper 0.276
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
- SRMR 0.117 0.117
+ SRMR 0.118 0.118
Parameter Estimates:
@@ -3039,98 +3037,98 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
F1 =~
- prix_med 0.669 0.044 15.289 0.000 0.669 0.699
- perc_log_vac -0.470 0.045 -10.511 0.000 -0.470 -0.467
- perc_maison -0.791 0.042 -19.036 0.000 -0.791 -0.786
- perc_tiny_log 0.834 0.064 13.012 0.000 0.834 0.834
- dens_pop 0.582 0.083 6.973 0.000 0.582 0.708
- med_niveau_vis 0.436 0.056 7.766 0.000 0.436 0.429
- part_gr_nb_mpl -0.649 0.035 -18.620 0.000 -0.649 -0.649
- prt_cdr_prfnt_ 0.811 0.065 12.545 0.000 0.811 0.832
+ prix_med 0.653 0.048 13.614 0.000 0.653 0.666
+ perc_log_vac -0.441 0.042 -10.506 0.000 -0.441 -0.425
+ perc_maison -0.839 0.039 -21.447 0.000 -0.839 -0.809
+ perc_tiny_log 0.898 0.059 15.153 0.000 0.898 0.872
+ dens_pop 0.577 0.073 7.923 0.000 0.577 0.716
+ med_niveau_vis 0.372 0.053 7.076 0.000 0.372 0.383
+ part_gr_nb_mpl -0.638 0.031 -20.491 0.000 -0.638 -0.648
+ prt_cdr_prfnt_ 0.871 0.064 13.651 0.000 0.871 0.842
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
- .prix_med 0.469 0.067 7.028 0.000 0.469 0.512
- .perc_log_vac 0.793 0.054 14.590 0.000 0.793 0.782
- .perc_maison 0.388 0.044 8.729 0.000 0.388 0.382
- .perc_tiny_log 0.305 0.037 8.183 0.000 0.305 0.305
- .dens_pop 0.338 0.091 3.692 0.000 0.338 0.499
- .med_niveau_vis 0.843 0.106 7.951 0.000 0.843 0.816
- .part_gr_nb_mpl 0.580 0.071 8.155 0.000 0.580 0.579
- .prt_cdr_prfnt_ 0.292 0.048 6.128 0.000 0.292 0.307
+ .prix_med 0.535 0.072 7.423 0.000 0.535 0.556
+ .perc_log_vac 0.882 0.058 15.166 0.000 0.882 0.820
+ .perc_maison 0.371 0.045 8.225 0.000 0.371 0.345
+ .perc_tiny_log 0.253 0.032 7.805 0.000 0.253 0.239
+ .dens_pop 0.317 0.090 3.513 0.000 0.317 0.487
+ .med_niveau_vis 0.807 0.104 7.792 0.000 0.807 0.854
+ .part_gr_nb_mpl 0.562 0.064 8.718 0.000 0.562 0.580
+ .prt_cdr_prfnt_ 0.312 0.052 6.024 0.000 0.312 0.291
F1 1.000 1.000 1.000
R-Square:
Estimate
- prix_med 0.488
- perc_log_vac 0.218
- perc_maison 0.618
- perc_tiny_log 0.695
- dens_pop 0.501
- med_niveau_vis 0.184
- part_gr_nb_mpl 0.421
- prt_cdr_prfnt_ 0.693
+ prix_med 0.444
+ perc_log_vac 0.180
+ perc_maison 0.655
+ perc_tiny_log 0.761
+ dens_pop 0.513
+ med_niveau_vis 0.146
+ part_gr_nb_mpl 0.420
+ prt_cdr_prfnt_ 0.709
Modification Indices:
lhs op rhs mi epc
-1 prix_med ~~ perc_log_vac 273.953 -0.441
-2 prix_med ~~ perc_maison 14.535 -0.080
-3 prix_med ~~ perc_tiny_log 103.334 -0.202
-4 prix_med ~~ dens_pop 9.485 -0.057
-5 prix_med ~~ med_niveau_vis 144.128 0.328
-6 prix_med ~~ part_agri_nb_emploi 3.524 -0.044
-7 prix_med ~~ part_cadre_profintellec_nbemploi 14.009 -0.073
-8 perc_log_vac ~~ perc_maison 1.023 -0.026
-9 perc_log_vac ~~ perc_tiny_log 132.269 0.272
-10 perc_log_vac ~~ dens_pop 7.793 0.063
-11 perc_log_vac ~~ med_niveau_vis 70.665 -0.286
-12 perc_log_vac ~~ part_agri_nb_emploi 26.183 0.149
-13 perc_log_vac ~~ part_cadre_profintellec_nbemploi 13.531 0.085
-14 perc_maison ~~ perc_tiny_log 78.053 -0.177
-15 perc_maison ~~ dens_pop 3.274 0.032
-16 perc_maison ~~ med_niveau_vis 23.625 0.126
-17 perc_maison ~~ part_agri_nb_emploi 0.834 0.021
-18 perc_maison ~~ part_cadre_profintellec_nbemploi 73.736 0.167
-19 perc_tiny_log ~~ dens_pop 43.196 0.112
-20 perc_tiny_log ~~ med_niveau_vis 78.601 -0.215
-21 perc_tiny_log ~~ part_agri_nb_emploi 10.296 0.069
-22 perc_tiny_log ~~ part_cadre_profintellec_nbemploi 35.656 0.115
-23 dens_pop ~~ med_niveau_vis 23.339 -0.112
-24 dens_pop ~~ part_agri_nb_emploi 35.890 0.120
-25 dens_pop ~~ part_cadre_profintellec_nbemploi 18.675 0.072
-26 med_niveau_vis ~~ part_agri_nb_emploi 4.857 -0.066
-27 med_niveau_vis ~~ part_cadre_profintellec_nbemploi 11.450 0.080
-28 part_agri_nb_emploi ~~ part_cadre_profintellec_nbemploi 6.468 -0.053
+1 prix_med ~~ perc_log_vac 271.463 -0.483
+2 prix_med ~~ perc_maison 24.625 -0.107
+3 prix_med ~~ perc_tiny_log 99.278 -0.198
+4 prix_med ~~ dens_pop 9.076 -0.056
+5 prix_med ~~ med_niveau_vis 160.976 0.355
+6 prix_med ~~ part_agri_nb_emploi 3.637 -0.046
+7 prix_med ~~ part_cadre_profintellec_nbemploi 7.630 -0.057
+8 perc_log_vac ~~ perc_maison 0.951 0.025
+9 perc_log_vac ~~ perc_tiny_log 125.527 0.264
+10 perc_log_vac ~~ dens_pop 1.729 0.030
+11 perc_log_vac ~~ med_niveau_vis 68.790 -0.289
+12 perc_log_vac ~~ part_agri_nb_emploi 11.514 0.102
+13 perc_log_vac ~~ part_cadre_profintellec_nbemploi 7.956 0.070
+14 perc_maison ~~ perc_tiny_log 47.971 -0.137
+15 perc_maison ~~ dens_pop 7.386 0.046
+16 perc_maison ~~ med_niveau_vis 14.314 0.094
+17 perc_maison ~~ part_agri_nb_emploi 2.300 0.033
+18 perc_maison ~~ part_cadre_profintellec_nbemploi 74.779 0.173
+19 perc_tiny_log ~~ dens_pop 35.827 0.095
+20 perc_tiny_log ~~ med_niveau_vis 73.982 -0.193
+21 perc_tiny_log ~~ part_agri_nb_emploi 9.047 0.061
+22 perc_tiny_log ~~ part_cadre_profintellec_nbemploi 36.278 0.119
+23 dens_pop ~~ med_niveau_vis 14.500 -0.083
+24 dens_pop ~~ part_agri_nb_emploi 23.309 0.091
+25 dens_pop ~~ part_cadre_profintellec_nbemploi 8.466 0.048
+26 med_niveau_vis ~~ part_agri_nb_emploi 3.766 -0.055
+27 med_niveau_vis ~~ part_cadre_profintellec_nbemploi 12.146 0.082
+28 part_agri_nb_emploi ~~ part_cadre_profintellec_nbemploi 5.358 -0.048
sepc.lv sepc.all sepc.nox
-1 -0.441 -0.722 -0.722
-2 -0.080 -0.187 -0.187
-3 -0.202 -0.535 -0.535
-4 -0.057 -0.143 -0.143
-5 0.328 0.522 0.522
-6 -0.044 -0.085 -0.085
-7 -0.073 -0.196 -0.196
-8 -0.026 -0.046 -0.046
-9 0.272 0.553 0.553
-10 0.063 0.122 0.122
-11 -0.286 -0.350 -0.350
-12 0.149 0.220 0.220
-13 0.085 0.177 0.177
-14 -0.177 -0.514 -0.514
-15 0.032 0.089 0.089
-16 0.126 0.221 0.221
-17 0.021 0.044 0.044
-18 0.167 0.498 0.498
-19 0.112 0.348 0.348
-20 -0.215 -0.424 -0.424
-21 0.069 0.164 0.164
-22 0.115 0.384 0.384
-23 -0.112 -0.211 -0.211
-24 0.120 0.272 0.272
-25 0.072 0.228 0.228
-26 -0.066 -0.094 -0.094
-27 0.080 0.161 0.161
-28 -0.053 -0.129 -0.129</code></pre>
+1 -0.483 -0.703 -0.703
+2 -0.107 -0.239 -0.239
+3 -0.198 -0.538 -0.538
+4 -0.056 -0.136 -0.136
+5 0.355 0.540 0.540
+6 -0.046 -0.084 -0.084
+7 -0.057 -0.139 -0.139
+8 0.025 0.044 0.044
+9 0.264 0.559 0.559
+10 0.030 0.057 0.057
+11 -0.289 -0.342 -0.342
+12 0.102 0.144 0.144
+13 0.070 0.133 0.133
+14 -0.137 -0.448 -0.448
+15 0.046 0.135 0.135
+16 0.094 0.172 0.172
+17 0.033 0.073 0.073
+18 0.173 0.508 0.508
+19 0.095 0.336 0.336
+20 -0.193 -0.426 -0.426
+21 0.061 0.161 0.161
+22 0.119 0.426 0.426
+23 -0.083 -0.164 -0.164
+24 0.091 0.217 0.217
+25 0.048 0.152 0.152
+26 -0.055 -0.082 -0.082
+27 0.082 0.164 0.164
+28 -0.048 -0.116 -0.116</code></pre>
<p>On peut également obtenir les saturations standardisées pour les deux
modèles, ce qui sera intéressant dans une perspective de compréhension
de la structure de la CFA, mais aussi de réutilisation dans des analyses
@@ -3143,13 +3141,13 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
$cov
prx_md prc_l_ prc_ms prc_t_ dns_pp
prix_med 0.000
-perc_log_vac -9.025 0.000
-perc_maison -1.544 -0.789 0.000
-perc_tiny_log -14.968 16.192 -3.927 0.000
-dens_pop -3.449 3.301 1.665 3.757 0.000
-med_niveau_vis 7.480 -8.762 3.707 -10.079 -4.269
-part_agri_nb_emploi -1.943 4.427 0.977 4.184 5.418
-part_cadre_profintellec_nbemploi -4.362 4.202 8.777 2.632 1.943
+perc_log_vac -9.951 0.000
+perc_maison -1.753 0.670 0.000
+perc_tiny_log -13.872 15.959 -3.507 0.000
+dens_pop -3.004 1.678 2.739 3.093 0.000
+med_niveau_vis 7.942 -8.427 3.176 -9.462 -3.329
+part_agri_nb_emploi -1.977 3.133 1.520 3.639 4.473
+part_cadre_profintellec_nbemploi -2.577 3.099 8.938 2.531 1.438
md_nv_ prt_g__ prt_c__
prix_med
perc_log_vac
@@ -3157,8 +3155,8 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
perc_tiny_log
dens_pop
med_niveau_vis 0.000
-part_agri_nb_emploi -2.414 0.000
-part_cadre_profintellec_nbemploi 2.037 -2.264 0.000</code></pre>
+part_agri_nb_emploi -2.208 0.000
+part_cadre_profintellec_nbemploi 2.186 -1.997 0.000</code></pre>
<div class="sourceCode" id="cb55"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" tabindex="-1"></a><span class="fu">resid</span>(fact_2immo,<span class="at">type=</span><span class="st">"standardized"</span>)</span></code></pre></div>
<pre class="bg-warning"><code>$type
[1] "standardized"
@@ -3166,13 +3164,13 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
$cov
prx_md prc_l_ md_nv_ prc_ms prc_t_ dns_pp
prix_med 0.000
-perc_log_vac -0.015 0.000
-med_niveau_vis 0.347 -3.549 0.000
-perc_maison -2.183 0.337 0.756 0.000
-perc_tiny_log -8.951 6.103 -2.339 -3.284 0.000
-dens_pop -0.852 1.489 -1.375 4.130 2.025 0.000
-part_agri_nb_emploi -5.097 5.867 -4.886 1.776 4.265 4.618
-part_cadre_profintellec_nbemploi 0.807 0.675 3.096 10.022 0.854 1.925
+perc_log_vac -0.027 0.000
+med_niveau_vis 0.398 -3.108 0.000
+perc_maison -2.292 1.286 0.595 0.000
+perc_tiny_log -8.149 6.064 -2.174 -2.160 0.000
+dens_pop -1.093 0.421 -1.174 3.789 1.987 0.000
+part_agri_nb_emploi -4.066 3.874 -3.616 2.091 4.526 3.932
+part_cadre_profintellec_nbemploi 0.660 0.599 2.824 9.581 1.228 1.319
prt_g__ prt_c__
prix_med
perc_log_vac
@@ -3181,7 +3179,7 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
perc_tiny_log
dens_pop
part_agri_nb_emploi 0.000
-part_cadre_profintellec_nbemploi -3.133 0.000</code></pre>
+part_cadre_profintellec_nbemploi -2.713 0.000</code></pre>
<p><strong>Etape 4 : Représenter les deux modèles :</strong></p>
<p>Voici donc la représentation des équations structurelles,
représentations qui obéit à un certain nombre de codes visuels : les
@@ -3194,12 +3192,12 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
<span id="cb57-2"><a href="#cb57-2" tabindex="-1"></a> <span class="at">sizeMan =</span> <span class="dv">8</span>, <span class="at">sizeInt =</span> <span class="dv">8</span>, <span class="at">sizeLat =</span> <span class="dv">8</span>,</span>
<span id="cb57-3"><a href="#cb57-3" tabindex="-1"></a> <span class="at">edge.label.cex=</span><span class="fl">0.8</span>,</span>
<span id="cb57-4"><a href="#cb57-4" tabindex="-1"></a> <span class="at">fade=</span><span class="cn">FALSE</span>)</span></code></pre></div>
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" 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" width="768" /></p>
<div class="sourceCode" id="cb58"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" tabindex="-1"></a>semPlot<span class="sc">::</span><span class="fu">semPaths</span>(fact_2immo, <span class="st">"std"</span>,</span>
<span id="cb58-2"><a href="#cb58-2" tabindex="-1"></a> <span class="at">sizeMan =</span> <span class="dv">8</span>, <span class="at">sizeInt =</span> <span class="dv">8</span>, <span class="at">sizeLat =</span> <span class="dv">8</span>,</span>
<span id="cb58-3"><a href="#cb58-3" tabindex="-1"></a> <span class="at">edge.label.cex=</span><span class="fl">0.8</span>,</span>
<span id="cb58-4"><a href="#cb58-4" tabindex="-1"></a> <span class="at">fade=</span><span class="cn">FALSE</span>)</span></code></pre></div>
-<p><img 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" 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" width="768" /></p>
<p><strong>Etape 5 : Comparer nos deux modèles</strong></p>
<p>Tout notre enjeu ici va être de comparer les deux modèles et de
choisir celui qui s’ajuste le mieux à nos données. Pour ce faire nous
@@ -3210,48 +3208,48 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
Name | Model | Chi2 | Chi2_df | p (Chi2) | Baseline(28)
-----------------------------------------------------------------
-fact_1immo | lavaan | 883.766 | 20.000 | < .001 | 2932.472
-fact_2immo | lavaan | 380.552 | 19.000 | < .001 | 2932.472
+fact_1immo | lavaan | 833.595 | 20.000 | < .001 | 2969.556
+fact_2immo | lavaan | 309.177 | 19.000 | < .001 | 2969.556
Name | p (Baseline) | GFI | AGFI | NFI | NNFI | CFI | RMSEA
-------------------------------------------------------------------------
-fact_1immo | < .001 | 0.726 | 0.506 | 0.699 | 0.584 | 0.703 | 0.266
-fact_2immo | < .001 | 0.869 | 0.751 | 0.870 | 0.817 | 0.876 | 0.176
+fact_1immo | < .001 | 0.741 | 0.534 | 0.719 | 0.613 | 0.723 | 0.258
+fact_2immo | < .001 | 0.891 | 0.793 | 0.896 | 0.855 | 0.901 | 0.158
Name | RMSEA CI | p (RMSEA) | RMR | SRMR | RFI | PNFI | IFI
-----------------------------------------------------------------------------
-fact_1immo | [0.25, 0.28] | < .001 | 0.114 | 0.117 | 0.578 | 0.499 | 0.703
-fact_2immo | [0.16, 0.19] | < .001 | 0.069 | 0.071 | 0.809 | 0.591 | 0.876
+fact_1immo | [0.24, 0.27] | < .001 | 0.117 | 0.118 | 0.607 | 0.514 | 0.724
+fact_2immo | [0.14, 0.17] | < .001 | 0.060 | 0.060 | 0.847 | 0.608 | 0.902
Name | RNI | Loglikelihood | AIC (weights) | BIC (weights) | BIC_adjusted
-------------------------------------------------------------------------------------
-fact_1immo | 0.703 | -5768.420 | 11568.8 (<.001) | 11639.5 (<.001) | 11588.684
-fact_2immo | 0.876 | -5516.813 | 11067.6 (>.999) | 11142.7 (>.999) | 11088.711</code></pre>
+fact_1immo | 0.723 | -5780.141 | 11592.3 (<.001) | 11662.9 (<.001) | 11612.126
+fact_2immo | 0.901 | -5517.932 | 11069.9 (>.999) | 11144.9 (>.999) | 11090.949</code></pre>
<div class="sourceCode" id="cb61"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" tabindex="-1"></a><span class="co"># Une autre manière de visualiser très utile.</span></span>
<span id="cb61-2"><a href="#cb61-2" tabindex="-1"></a>effectsize<span class="sc">::</span><span class="fu">interpret</span>(fact_1immo)</span></code></pre></div>
<pre class="bg-warning"><code> Name Value Threshold Interpretation
-1 GFI 0.7256575 0.95 poor
-2 AGFI 0.5061835 0.90 poor
-3 NFI 0.6986277 0.90 poor
-4 NNFI 0.5836516 0.90 poor
-5 CFI 0.7026083 0.90 poor
-6 RMSEA 0.2658658 0.05 poor
-7 SRMR 0.1169251 0.08 poor
-8 RFI 0.5780787 0.90 poor
-9 PNFI 0.4990198 0.50 poor
-10 IFI 0.7034251 0.90 poor</code></pre>
+1 GFI 0.7408374 0.95 poor
+2 AGFI 0.5335073 0.90 poor
+3 NFI 0.7192862 0.90 poor
+4 NNFI 0.6127786 0.90 poor
+5 CFI 0.7234133 0.90 poor
+6 RMSEA 0.2580290 0.05 poor
+7 SRMR 0.1175162 0.08 poor
+8 RFI 0.6070007 0.90 poor
+9 PNFI 0.5137759 0.50 satisfactory
+10 IFI 0.7241635 0.90 poor</code></pre>
<div class="sourceCode" id="cb63"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" tabindex="-1"></a>effectsize<span class="sc">::</span><span class="fu">interpret</span>(fact_2immo)</span></code></pre></div>
<pre class="bg-warning"><code> Name Value Threshold Interpretation
-1 GFI 0.86866975 0.95 poor
-2 AGFI 0.75116374 0.90 poor
-3 NFI 0.87022819 0.90 poor
-4 NNFI 0.81655400 0.90 poor
-5 CFI 0.87551879 0.90 poor
-6 RMSEA 0.17647699 0.05 poor
-7 SRMR 0.07102026 0.08 satisfactory
-8 RFI 0.80875733 0.90 poor
-9 PNFI 0.59051199 0.50 satisfactory
-10 IFI 0.87590332 0.90 poor</code></pre>
+1 GFI 0.89053008 0.95 poor
+2 AGFI 0.79258331 0.90 poor
+3 NFI 0.89588430 0.90 poor
+4 NNFI 0.85462461 0.90 poor
+5 CFI 0.90135242 0.90 satisfactory
+6 RMSEA 0.15810090 0.05 poor
+7 SRMR 0.05999792 0.08 satisfactory
+8 RFI 0.84656634 0.90 poor
+9 PNFI 0.60792149 0.50 satisfactory
+10 IFI 0.90165332 0.90 satisfactory</code></pre>
<p>Dans les deux configurations testées, l’analyse factorielle
confirmatoire présente un ajustement des modèles aux jeux de données
“test” plutôt mauvais. Pour le deuxième modèle toutefois, 2 indices sont
@@ -3302,90 +3300,90 @@ <h3><span class="header-section-number">6.1.1</span> L’analyse
peut en trouver une traduction <a href="https://lavaan.ugent.be/tutorial/syntax1.html">ici</a></p>
<div class="sourceCode" id="cb65"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb65-1"><a href="#cb65-1" tabindex="-1"></a><span class="fu">modindices</span>(fact_1immo) <span class="sc">%>%</span> <span class="fu">arrange</span>(<span class="sc">-</span>mi) <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">20</span>)</span></code></pre></div>
<pre class="bg-warning"><code> lhs op rhs mi epc sepc.lv
-1 prix_med ~~ perc_log_vac 273.953 -0.441 -0.441
-2 prix_med ~~ med_niveau_vis 144.128 0.328 0.328
-3 perc_log_vac ~~ perc_tiny_log 132.269 0.272 0.272
-4 prix_med ~~ perc_tiny_log 103.334 -0.202 -0.202
-5 perc_tiny_log ~~ med_niveau_vis 78.601 -0.215 -0.215
-6 perc_maison ~~ perc_tiny_log 78.053 -0.177 -0.177
-7 perc_maison ~~ part_cadre_profintellec_nbemploi 73.736 0.167 0.167
-8 perc_log_vac ~~ med_niveau_vis 70.665 -0.286 -0.286
-9 perc_tiny_log ~~ dens_pop 43.196 0.112 0.112
-10 dens_pop ~~ part_agri_nb_emploi 35.890 0.120 0.120
-11 perc_tiny_log ~~ part_cadre_profintellec_nbemploi 35.656 0.115 0.115
-12 perc_log_vac ~~ part_agri_nb_emploi 26.183 0.149 0.149
-13 perc_maison ~~ med_niveau_vis 23.625 0.126 0.126
-14 dens_pop ~~ med_niveau_vis 23.339 -0.112 -0.112
-15 dens_pop ~~ part_cadre_profintellec_nbemploi 18.675 0.072 0.072
-16 prix_med ~~ perc_maison 14.535 -0.080 -0.080
-17 prix_med ~~ part_cadre_profintellec_nbemploi 14.009 -0.073 -0.073
-18 perc_log_vac ~~ part_cadre_profintellec_nbemploi 13.531 0.085 0.085
-19 med_niveau_vis ~~ part_cadre_profintellec_nbemploi 11.450 0.080 0.080
-20 perc_tiny_log ~~ part_agri_nb_emploi 10.296 0.069 0.069
+1 prix_med ~~ perc_log_vac 271.463 -0.483 -0.483
+2 prix_med ~~ med_niveau_vis 160.976 0.355 0.355
+3 perc_log_vac ~~ perc_tiny_log 125.527 0.264 0.264
+4 prix_med ~~ perc_tiny_log 99.278 -0.198 -0.198
+5 perc_maison ~~ part_cadre_profintellec_nbemploi 74.779 0.173 0.173
+6 perc_tiny_log ~~ med_niveau_vis 73.982 -0.193 -0.193
+7 perc_log_vac ~~ med_niveau_vis 68.790 -0.289 -0.289
+8 perc_maison ~~ perc_tiny_log 47.971 -0.137 -0.137
+9 perc_tiny_log ~~ part_cadre_profintellec_nbemploi 36.278 0.119 0.119
+10 perc_tiny_log ~~ dens_pop 35.827 0.095 0.095
+11 prix_med ~~ perc_maison 24.625 -0.107 -0.107
+12 dens_pop ~~ part_agri_nb_emploi 23.309 0.091 0.091
+13 dens_pop ~~ med_niveau_vis 14.500 -0.083 -0.083
+14 perc_maison ~~ med_niveau_vis 14.314 0.094 0.094
+15 med_niveau_vis ~~ part_cadre_profintellec_nbemploi 12.146 0.082 0.082
+16 perc_log_vac ~~ part_agri_nb_emploi 11.514 0.102 0.102
+17 prix_med ~~ dens_pop 9.076 -0.056 -0.056
+18 perc_tiny_log ~~ part_agri_nb_emploi 9.047 0.061 0.061
+19 dens_pop ~~ part_cadre_profintellec_nbemploi 8.466 0.048 0.048
+20 perc_log_vac ~~ part_cadre_profintellec_nbemploi 7.956 0.070 0.070
sepc.all sepc.nox
-1 -0.722 -0.722
-2 0.522 0.522
-3 0.553 0.553
-4 -0.535 -0.535
-5 -0.424 -0.424
-6 -0.514 -0.514
-7 0.498 0.498
-8 -0.350 -0.350
-9 0.348 0.348
-10 0.272 0.272
-11 0.384 0.384
-12 0.220 0.220
-13 0.221 0.221
-14 -0.211 -0.211
-15 0.228 0.228
-16 -0.187 -0.187
-17 -0.196 -0.196
-18 0.177 0.177
-19 0.161 0.161
-20 0.164 0.164</code></pre>
+1 -0.703 -0.703
+2 0.540 0.540
+3 0.559 0.559
+4 -0.538 -0.538
+5 0.508 0.508
+6 -0.426 -0.426
+7 -0.342 -0.342
+8 -0.448 -0.448
+9 0.426 0.426
+10 0.336 0.336
+11 -0.239 -0.239
+12 0.217 0.217
+13 -0.164 -0.164
+14 0.172 0.172
+15 0.164 0.164
+16 0.144 0.144
+17 -0.136 -0.136
+18 0.161 0.161
+19 0.152 0.152
+20 0.133 0.133</code></pre>
<div class="sourceCode" id="cb67"><pre class="sourceCode r bg-info"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" tabindex="-1"></a><span class="fu">modindices</span>(fact_2immo) <span class="sc">%>%</span> <span class="fu">arrange</span>(<span class="sc">-</span>mi) <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">20</span>)</span></code></pre></div>
<pre class="bg-warning"><code> lhs op rhs mi epc
-1 perc_maison ~~ part_cadre_profintellec_nbemploi 79.264 0.174
-2 F1 =~ perc_tiny_log 70.612 -0.261
-3 prix_med ~~ perc_maison 61.030 -0.113
-4 med_niveau_vis ~~ part_cadre_profintellec_nbemploi 60.098 0.159
-5 F1 =~ perc_maison 52.971 -0.248
-6 perc_log_vac ~~ part_agri_nb_emploi 38.931 0.144
-7 med_niveau_vis ~~ perc_maison 27.174 0.115
-8 perc_maison ~~ perc_tiny_log 27.117 -0.106
-9 dens_pop ~~ part_agri_nb_emploi 26.638 0.104
-10 perc_log_vac ~~ perc_tiny_log 26.106 0.084
-11 part_agri_nb_emploi ~~ part_cadre_profintellec_nbemploi 21.456 -0.099
-12 perc_tiny_log ~~ part_agri_nb_emploi 19.316 0.091
-13 F1 =~ part_agri_nb_emploi 14.790 -0.156
-14 F2 =~ prix_med 14.413 -0.276
-15 perc_log_vac ~~ med_niveau_vis 14.413 -0.116
-16 perc_maison ~~ dens_pop 12.981 0.062
-17 dens_pop ~~ part_cadre_profintellec_nbemploi 12.809 0.059
-18 F2 =~ perc_log_vac 11.607 -0.172
-19 prix_med ~~ med_niveau_vis 11.607 -0.147
-20 prix_med ~~ part_cadre_profintellec_nbemploi 9.576 -0.042
+1 F1 =~ perc_maison 65.495 -0.257
+2 perc_maison ~~ part_cadre_profintellec_nbemploi 61.894 0.157
+3 F1 =~ perc_tiny_log 60.582 -0.219
+4 prix_med ~~ perc_maison 59.199 -0.113
+5 med_niveau_vis ~~ part_cadre_profintellec_nbemploi 51.113 0.144
+6 med_niveau_vis ~~ perc_maison 25.875 0.107
+7 perc_log_vac ~~ perc_tiny_log 24.245 0.081
+8 perc_tiny_log ~~ part_agri_nb_emploi 18.107 0.083
+9 dens_pop ~~ part_agri_nb_emploi 17.246 0.079
+10 perc_log_vac ~~ part_agri_nb_emploi 15.706 0.092
+11 part_agri_nb_emploi ~~ part_cadre_profintellec_nbemploi 14.611 -0.081
+12 perc_maison ~~ perc_tiny_log 12.631 -0.072
+13 perc_maison ~~ dens_pop 11.571 0.056
+14 perc_tiny_log ~~ dens_pop 11.001 0.051
+15 perc_log_vac ~~ med_niveau_vis 10.517 -0.101
+16 F2 =~ prix_med 10.517 -0.243
+17 F1 =~ part_agri_nb_emploi 10.476 -0.120
+18 prix_med ~~ part_cadre_profintellec_nbemploi 8.787 -0.042
+19 prix_med ~~ med_niveau_vis 7.248 -0.124
+20 F2 =~ perc_log_vac 7.248 -0.136
sepc.lv sepc.all sepc.nox
-1 0.174 0.517 0.517
-2 -0.261 -0.261 -0.261
-3 -0.113 -0.693 -0.693
-4 0.159 0.344 0.344
-5 -0.248 -0.246 -0.246
-6 0.144 0.250 0.250
-7 0.115 0.226 0.226
-8 -0.106 -0.376 -0.376
-9 0.104 0.233 0.233
-10 0.084 0.249 0.249
-11 -0.099 -0.227 -0.227
-12 0.091 0.249 0.249
-13 -0.156 -0.156 -0.156
-14 -0.276 -0.288 -0.288
-15 -0.116 -0.190 -0.190
-16 0.062 0.181 0.181
-17 0.059 0.188 0.188
-18 -0.172 -0.171 -0.171
-19 -0.147 -0.653 -0.653
-20 -0.042 -0.283 -0.283</code></pre>
+1 -0.257 -0.248 -0.248
+2 0.157 0.454 0.454
+3 -0.219 -0.213 -0.213
+4 -0.113 -0.566 -0.566
+5 0.144 0.312 0.312
+6 0.107 0.218 0.218
+7 0.081 0.248 0.248
+8 0.083 0.255 0.255
+9 0.079 0.185 0.185
+10 0.092 0.156 0.156
+11 -0.081 -0.185 -0.185
+12 -0.072 -0.281 -0.281
+13 0.056 0.168 0.168
+14 0.051 0.218 0.218
+15 -0.101 -0.162 -0.162
+16 -0.243 -0.248 -0.248
+17 -0.120 -0.122 -0.122
+18 -0.042 -0.224 -0.224
+19 -0.124 -0.465 -0.465
+20 -0.136 -0.131 -0.131</code></pre>
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