Bonchopred_comparatif <- read.csv2("C:/Users/mallah.s/Desktop/Stats et Theses/Brochopred_Arvin/Bonchopred_comparatif.csv", stringsAsFactors=TRUE) BP_C<-Bonchopred_comparatiftest de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.87148, p-value = 0.06823shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.80513, p-value = 0.06533la normalité: acceptée -> t-test sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_IIIA
## F = 1.4236, num df = 11, denom df = 5, p-value = 0.7334
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.216757 5.757118
## sample estimates:
## ratio of variances
## 1.42362test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_IIIA
## t = 0.44148, df = 16, p-value = 0.6648
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -42.25136 64.47803
## sample estimates:
## mean in group Non mean in group Oui
## -27.77667 -38.89000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.8723, p-value = 0.06989shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.63989, p-value = 0.001351la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_IIIB
## F = 11.906, num df = 11, denom df = 5, p-value = 0.01334
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.812776 48.147776
## sample estimates:
## ratio of variances
## 11.90598test de variance: non accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_IIIB)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_IIIB
## t = -0.74976, df = 14.129, p-value = 0.4657
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -53.59641 25.81308
## sample estimates:
## mean in group Non mean in group Oui
## -36.11167 -22.22000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.83977, p-value = 0.02751shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.88181, p-value = 0.2775la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_IV
## F = 0.19086, num df = 11, denom df = 5, p-value = 0.02105
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02905923 0.77182035
## sample estimates:
## ratio of variances
## 0.1908558test de variance: rejetée
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_IV,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_IV
## t = 0.085543, df = 5.9751, p-value = 0.9346
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -76.77455 82.33122
## sample estimates:
## mean in group Non mean in group Oui
## -30.55500 -33.33333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIA == "Non"]
## W = 0.8165, p-value = 0.01452shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIA == "Oui"]
## W = 0.96427, p-value = 0.6368la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_IIIA
## F = 0.88311, num df = 11, denom df = 2, p-value = 0.7142
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02240987 4.64151193
## sample estimates:
## ratio of variances
## 0.8831069test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_IIIA
## W = 20.5, p-value = 0.7534
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIB == "Non"]
## W = 0.87251, p-value = 0.1308shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IIIB == "Oui"]
## W = 0.82682, p-value = 0.101la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_IIIB
## F = 8.1269, num df = 8, denom df = 5, p-value = 0.03366
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.202704 39.149398
## sample estimates:
## ratio of variances
## 8.126875test de variance: non accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_IIIB)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_IIIB
## t = 0.5096, df = 10.645, p-value = 0.6207
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -37.07053 59.29053
## sample estimates:
## mean in group Non mean in group Oui
## 11.11 0.00significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IV == "Non"]
## W = 0.8463, p-value = 0.06783shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_IV == "Oui"]
## W = 0.85134, p-value = 0.1614la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_IV
## F = 0.22269, num df = 8, denom df = 5, p-value = 0.0604
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03295567 1.07274515
## sample estimates:
## ratio of variances
## 0.2226871test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_IV
## t = -0.90407, df = 6.5052, p-value = 0.3982
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -94.79644 42.94422
## sample estimates:
## mean in group Non mean in group Oui
## -3.704444 22.221667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.83906, p-value = 0.009444shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.99839, p-value = 0.9951la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_IIIA
## F = 0.32308, num df = 15, denom df = 3, p-value = 0.1176
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02266786 1.34168179
## sample estimates:
## ratio of variances
## 0.3230785test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_IIIA
## W = 25, p-value = 0.5254
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.88535, p-value = 0.1027shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.78232, p-value = 0.01846la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_IIIB
## F = 3.3167, num df = 11, denom df = 7, p-value = 0.1225
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.704259 12.466224
## sample estimates:
## ratio of variances
## 3.316687test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_IIIB)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_IIIB
## W = 34, p-value = 0.2815
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.92398, p-value = 0.3207shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.84338, p-value = 0.08157la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_IV
## F = 1.033, num df = 11, denom df = 7, p-value = 0.9957
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2193375 3.8825361
## sample estimates:
## ratio of variances
## 1.032964test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_IV
## W = 69, p-value = 0.102
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.94919, p-value = 0.6251shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.78254, p-value = 0.04071la normalité: rejetée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_IIIA
## F = 2.4532, num df = 11, denom df = 5, p-value = 0.3323
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3735237 9.9208811
## sample estimates:
## ratio of variances
## 2.453236test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_IIIA
## W = 47.5, p-value = 0.2989
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.88126, p-value = 0.09098shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.891, p-value = 0.3234la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_IIIB
## F = 3.0823, num df = 11, denom df = 5, p-value = 0.2241
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.469297 12.464643
## sample estimates:
## ratio of variances
## 3.082257test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_IIIB)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_IIIB
## t = -1.2867, df = 15.526, p-value = 0.2171
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -56.47701 13.88035
## sample estimates:
## mean in group Non mean in group Oui
## -31.48333 -10.18500significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.9323, p-value = 0.4052shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.92139, p-value = 0.5154la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_IV
## F = 0.23512, num df = 11, denom df = 5, p-value = 0.04257
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03579941 0.95084118
## sample estimates:
## ratio of variances
## 0.235124test de variance: rejetée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_IV,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_IV
## t = 0.19819, df = 6.2057, p-value = 0.8492
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -57.26856 67.45190
## sample estimates:
## mean in group Non mean in group Oui
## -22.68667 -27.77833significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIA == "Non"]
## W = 0.95517, p-value = 0.7133shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIA == "Oui"]
## W = 0.85464, p-value = 0.2529la normalité: acceptée -> t.test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_IIIA
## F = 0.73709, num df = 11, denom df = 2, p-value = 0.5948
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.01870455 3.87406870
## sample estimates:
## ratio of variances
## 0.737091test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_IIIA
## t = 1.3729, df = 13, p-value = 0.193
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -18.85601 84.60101
## sample estimates:
## mean in group Non mean in group Oui
## 14.3525 -18.5200significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIB == "Non"]
## W = 0.99037, p-value = 0.9966shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IIIB == "Oui"]
## W = 0.96822, p-value = 0.8802la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_IIIB
## F = 1.6435, num df = 8, denom df = 5, p-value = 0.6059
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2432267 7.9173104
## sample estimates:
## ratio of variances
## 1.643524test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_IIIB, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_IIIB
## t = -0.51782, df = 12.546, p-value = 0.6136
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -52.84270 32.46936
## sample estimates:
## mean in group Non mean in group Oui
## 3.703333 13.890000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IV == "Non"]
## W = 0.96249, p-value = 0.824shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_IV == "Oui"]
## W = 0.96353, p-value = 0.8465la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_IV
## F = 0.79349, num df = 8, denom df = 5, p-value = 0.7329
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1174291 3.8224536
## sample estimates:
## ratio of variances
## 0.7934887test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_IV
## t = -0.55361, df = 9.949, p-value = 0.5921
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -58.96478 35.50812
## sample estimates:
## mean in group Non mean in group Oui
## 3.086667 14.815000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.88866, p-value = 0.05304shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.94562, p-value = 0.6889la normalité: accepté meme si une des données est = à 0.05 -> on va opter pour un test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_IIIA
## F = 0.47004, num df = 15, denom df = 3, p-value = 0.2791
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03297932 1.95200393
## sample estimates:
## ratio of variances
## 0.4700448test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_IIIA
## t = -0.76337, df = 18, p-value = 0.4551
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -70.35049 32.85174
## sample estimates:
## mean in group Non mean in group Oui
## -40.97187 -22.22250significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.9239, p-value = 0.3199shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.8021, p-value = 0.03014la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_IIIB
## F = 2.8523, num df = 11, denom df = 7, p-value = 0.1745
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6056531 10.7207811
## sample estimates:
## ratio of variances
## 2.852305test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_IIIB)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_IIIB
## W = 47, p-value = 0.9689
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.9345, p-value = 0.4302shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.86904, p-value = 0.1475la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_IV
## F = 0.63984, num df = 11, denom df = 7, p-value = 0.4876
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1358624 2.4049256
## sample estimates:
## ratio of variances
## 0.6398397test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_IV
## t = 0.79574, df = 12.767, p-value = 0.4407
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -28.66716 62.00216
## sample estimates:
## mean in group Non mean in group Oui
## -30.5550 -47.2225significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.89374, p-value = 0.1317shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.86977, p-value = 0.2253la normalité: acceptée -> t-test sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_IIIA
## F = 2.8164, num df = 11, denom df = 5, p-value = 0.2629
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4288249 11.3896954
## sample estimates:
## ratio of variances
## 2.816444test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_IIIA
## t = -0.69761, df = 16, p-value = 0.4954
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -36.32254 18.33587
## sample estimates:
## mean in group Non mean in group Oui
## 13.37833 22.37167significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.76634, p-value = 0.00398shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.98413, p-value = 0.9701la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_IIIB
## F = 7.8526, num df = 11, denom df = 5, p-value = 0.03396
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.195625 31.756101
## sample estimates:
## ratio of variances
## 7.85265test de variance: non accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_IIIB,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_IIIB
## t = 1.5019, df = 15.154, p-value = 0.1537
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -5.990963 34.659296
## sample estimates:
## mean in group Non mean in group Oui
## 21.15417 6.82000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.95912, p-value = 0.7713shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.734, p-value = 0.01383la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_IV
## F = 0.15503, num df = 11, denom df = 5, p-value = 0.009832
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02360428 0.62693546
## sample estimates:
## ratio of variances
## 0.1550286test de variance: rejetée
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_IV,var.test=FALSE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_IV
## W = 19, p-value = 0.1246
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIA == "Non"]
## W = 0.90326, p-value = 0.1748shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIA == "Oui"]
## W = 0.9872, p-value = 0.7835la normalité: non acceptée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_IIIA
## F = 0.35654, num df = 11, denom df = 2, p-value = 0.2074
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.009047524 1.873914918
## sample estimates:
## ratio of variances
## 0.3565362test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_IIIA
## t = -1.4919, df = 13, p-value = 0.1596
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -60.94289 11.15456
## sample estimates:
## mean in group Non mean in group Oui
## -6.17750 18.71667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIB == "Non"]
## W = 0.97044, p-value = 0.8985shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IIIB == "Oui"]
## W = 0.95146, p-value = 0.7521la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_IIIB
## F = 6.0555, num df = 8, denom df = 5, p-value = 0.0629
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8961633 29.1711535
## sample estimates:
## ratio of variances
## 6.055529test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_IIIB)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_IIIB
## t = -0.0061963, df = 11.341, p-value = 0.9952
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -27.80158 27.64491
## sample estimates:
## mean in group Non mean in group Oui
## -1.230000 -1.151667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IV == "Non"]
## W = 0.92545, p-value = 0.4393shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_IV == "Oui"]
## W = 0.92789, p-value = 0.5639la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_IV
## F = 0.6496, num df = 8, denom df = 5, p-value = 0.559
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09613426 3.12928178
## sample estimates:
## ratio of variances
## 0.6495958test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_IV
## t = 1.1375, df = 9.191, p-value = 0.2841
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -16.38105 49.72994
## sample estimates:
## mean in group Non mean in group Oui
## 5.471111 -11.203333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.90223, p-value = 0.08725shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.95772, p-value = 0.7646la normalité: rejetée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_IIIA
## F = 0.83296, num df = 15, denom df = 3, p-value = 0.6868
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05844213 3.45911450
## sample estimates:
## ratio of variances
## 0.8329588test de variance: accepté
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_IIIA
## t = 0.48529, df = 18, p-value = 0.6333
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -19.85245 31.77870
## sample estimates:
## mean in group Non mean in group Oui
## 18.12313 12.16000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.9327, p-value = 0.4096shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.97024, p-value = 0.8998la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_IIIB
## F = 4.2678, num df = 11, denom df = 7, p-value = 0.06493
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.9062104 16.0410045
## sample estimates:
## ratio of variances
## 4.267771test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_IIIB,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_IIIB
## t = 2.0216, df = 16.825, p-value = 0.05942
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -0.7345335 33.7862002
## sample estimates:
## mean in group Non mean in group Oui
## 23.54083 7.01500significativité: les resultats sont à la limite de la significativité, on peut en deduire ( avec precaution car nombre de patient non suffisant) que l’evolution du score QL30 entre la V2 et la V3 pour les patients inclus au Stade IIIB tend à etre dépendant
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.92879, p-value = 0.3674shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.88648, p-value = 0.217la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_IV
## F = 0.42694, num df = 11, denom df = 7, p-value = 0.1999
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09065611 1.60472122
## sample estimates:
## ratio of variances
## 0.4269422test de variance: acceptée
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_IV
## t = -2.1229, df = 10.986, p-value = 0.05732
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -41.7603477 0.7578477
## sample estimates:
## mean in group Non mean in group Oui
## 8.73000 29.23125significativité: les resultats sont à la limite de la significativité, on peut en deduire ( avec precaution car nombre de patient non suffisant) que l’evolution du score QL30 entre la V2 et la V1 pour les patients inclus au Stade IIIB tend à etre dépendant
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.90942, p-value = 0.2097shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.75142, p-value = 0.02057la normalité: rejetée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_IIIA
## F = 2.5738, num df = 11, denom df = 5, p-value = 0.3068
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3918771 10.4083523
## sample estimates:
## ratio of variances
## 2.573778test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_IIIA
## W = 31.5, p-value = 0.7075
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.82399, p-value = 0.01779shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.86202, p-value = 0.1962la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_IIIB
## F = 0.69522, num df = 11, denom df = 5, p-value = 0.5705
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1058527 2.8114750
## sample estimates:
## ratio of variances
## 0.6952216test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_IIIB)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_IIIB
## W = 51.5, p-value = 0.1594
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.88078, p-value = 0.0897shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.83161, p-value = 0.1109la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_IV
## F = 0.60217, num df = 11, denom df = 5, p-value = 0.4486
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09168531 2.43518435
## sample estimates:
## ratio of variances
## 0.6021725test de variance: rejetée
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_IV
## t = 0.12407, df = 8.1292, p-value = 0.9043
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -25.01698 27.87031
## sample estimates:
## mean in group Non mean in group Oui
## 11.61333 10.18667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIA == "Non"]
## W = 0.92264, p-value = 0.3085shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIA == "Oui"]
## W = 0.96429, p-value = 0.6369la normalité: acceptée -> t.test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_IIIA
## F = 86.405, num df = 11, denom df = 2, p-value = 0.02299
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.192626 454.134790
## sample estimates:
## ratio of variances
## 86.40494test de variance: rejeté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_IIIA
## t = -0.23473, df = 13, p-value = 0.8181
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -37.39628 30.06628
## sample estimates:
## mean in group Non mean in group Oui
## 1.581667 5.246667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIB == "Non"]
## W = 0.9042, p-value = 0.2774shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IIIB == "Oui"]
## W = 0.90074, p-value = 0.3783la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_IIIB
## F = 0.21759, num df = 8, denom df = 5, p-value = 0.05678
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03220132 1.04819019
## sample estimates:
## ratio of variances
## 0.2175898test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_IIIB, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_IIIB
## t = -0.4508, df = 6.4707, p-value = 0.6669
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -41.55956 28.43622
## sample estimates:
## mean in group Non mean in group Oui
## -0.310000 6.251667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IV == "Non"]
## W = 0.882, p-value = 0.1648shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_IV == "Oui"]
## W = 0.96508, p-value = 0.8579la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_IV
## F = 1.958, num df = 8, denom df = 5, p-value = 0.4762
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2897685 9.4323008
## sample estimates:
## ratio of variances
## 1.958016test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_IV
## t = 0.77048, df = 12.869, p-value = 0.4549
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -16.2706 34.2806
## sample estimates:
## mean in group Non mean in group Oui
## 5.916667 -3.088333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.86131, p-value = 0.02007shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.8732, p-value = 0.3104la normalité: rejetée
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_IIIA
## F = 1.9071, num df = 15, denom df = 3, p-value = 0.6557
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1338032 7.9196390
## sample estimates:
## ratio of variances
## 1.907058test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_IIIA
## W = 33, p-value = 0.9623
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.87031, p-value = 0.06594shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.87213, p-value = 0.1581la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_IIIB
## F = 0.28998, num df = 11, denom df = 7, p-value = 0.06607
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06157395 1.08993222
## sample estimates:
## ratio of variances
## 0.2899806test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_IIIB,test.var=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_IIIB
## t = 0.83446, df = 9.7365, p-value = 0.424
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -19.12184 41.88600
## sample estimates:
## mean in group Non mean in group Oui
## 11.72833 0.34625significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.87837, p-value = 0.08355shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.82008, p-value = 0.04675la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_IV
## F = 2.4209, num df = 11, denom df = 7, p-value = 0.2501
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5140428 9.0991703
## sample estimates:
## ratio of variances
## 2.420869test de variance: acceptée
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_IV
## W = 30, p-value = 0.1765
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.75817, p-value = 0.002658shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.7832, p-value = 0.02783la normalité: acceptée -> t-test sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.46028, num df = 10, denom df = 6, p-value = 0.2662
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08428048 1.87433264
## sample estimates:
## ratio of variances
## 0.460283test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_.dyspnee_II
## t = -0.19474, df = 16, p-value = 0.848
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -56.61856 47.09128
## sample estimates:
## mean in group Non mean in group Oui
## -33.33364 -28.57000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.87148, p-value = 0.06823shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.80513, p-value = 0.06533la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_.dyspnee_III
## F = 1.4236, num df = 11, denom df = 5, p-value = 0.7334
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.216757 5.757118
## sample estimates:
## ratio of variances
## 1.42362test de variance: non accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_.dyspnee_III)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_.dyspnee_III
## t = 0.4696, df = 11.909, p-value = 0.6471
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -40.49328 62.71995
## sample estimates:
## mean in group Non mean in group Oui
## -27.77667 -38.89000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.88284, p-value = 0.07793shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.73476, p-value = 0.02138la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 1.4706, num df = 12, denom df = 4, p-value = 0.7621
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1680493 6.0607578
## sample estimates:
## ratio of variances
## 1.470626test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Stade_.dyspnee_IV)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 25.5, p-value = 0.5076
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.82713, p-value = 0.05542shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.93222, p-value = 0.5699la normalité: acceptée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_.dyspnee_II
## F = 0.77886, num df = 7, denom df = 6, p-value = 0.7438
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1367511 3.9866802
## sample estimates:
## ratio of variances
## 0.778862test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_.dyspnee_II
## t = 0.14015, df = 13, p-value = 0.8907
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -51.47424 58.61638
## sample estimates:
## mean in group Non mean in group Oui
## 8.332500 4.761429significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.85694, p-value = 0.04476shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_.dyspnee_III
## F = 7.5471, num df = 11, denom df = 2, p-value = 0.2454
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1915163 39.6666758
## sample estimates:
## ratio of variances
## 7.547091test de variance: non accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_.dyspnee_III)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_.dyspnee_III
## t = -0.29428, df = 10.118, p-value = 0.7745
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -47.54797 36.43797
## sample estimates:
## mean in group Non mean in group Oui
## 5.555 11.110significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.89705, p-value = 0.2033shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.86279, p-value = 0.2384la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_.dyspnee_IV
## F = 0.54164, num df = 9, denom df = 4, p-value = 0.4086
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06082687 2.55551833
## sample estimates:
## ratio of variances
## 0.5416439test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Stade_.dyspnee_IV
## t = 0, df = 6.256, p-value = 1
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -72.83559 72.83559
## sample estimates:
## mean in group Non mean in group Oui
## 6.666 6.666significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.89331, p-value = 0.13shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.85994, p-value = 0.1199la normalité: accepté -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_.dyspnee_II
## F = 2.2008, num df = 11, denom df = 7, p-value = 0.3044
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4673128 8.2719929
## sample estimates:
## ratio of variances
## 2.200795test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_.dyspnee_II
## t = -0.25945, df = 18, p-value = 0.7982
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -50.55241 39.43907
## sample estimates:
## mean in group Non mean in group Oui
## -38.88917 -33.33250significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.91634, p-value = 0.1472shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.8494, p-value = 0.2242la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_.dyspnee_III
## F = 0.85742, num df = 15, denom df = 3, p-value = 0.7107
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.0601587 3.5607165
## sample estimates:
## ratio of variances
## 0.8574247test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_.dyspnee_III)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_.dyspnee_III
## t = 0.22691, df = 4.3837, p-value = 0.8307
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -67.66974 80.17224
## sample estimates:
## mean in group Non mean in group Oui
## -35.41625 -41.66750significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.83028, p-value = 0.02113shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.91939, p-value = 0.4249la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 0.45472, num df = 11, denom df = 7, p-value = 0.2338
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09655393 1.70911968
## sample estimates:
## ratio of variances
## 0.4547178test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 51.5, p-value = 0.8109
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.93874, p-value = 0.5058shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.85391, p-value = 0.1334la normalité:acceptée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.4806, num df = 10, denom df = 6, p-value = 0.2924
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08799985 1.95704864
## sample estimates:
## ratio of variances
## 0.4805957test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_.dyspnee_II
## t = -1.4408, df = 16, p-value = 0.1689
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -66.51755 12.68716
## sample estimates:
## mean in group Non mean in group Oui
## -34.850909 -7.935714significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.9495, p-value = 0.6296shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.90669, p-value = 0.415la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_.dyspnee_III
## F = 2.9009, num df = 11, denom df = 5, p-value = 0.2496
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4416783 11.7310841
## sample estimates:
## ratio of variances
## 2.900863test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_.dyspnee_III,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_.dyspnee_III
## t = 0.45765, df = 15.347, p-value = 0.6536
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -28.71773 44.46107
## sample estimates:
## mean in group Non mean in group Oui
## -21.76000 -29.63167significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.86004, p-value = 0.03857shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.92855, p-value = 0.5865la normalité: non acceptée -> test de Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 0.92372, num df = 12, denom df = 4, p-value = 0.8159
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1055541 3.8068469
## sample estimates:
## ratio of variances
## 0.923721test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 40, p-value = 0.4866
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.89398, p-value = 0.2547shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.90428, p-value = 0.3577la normalité: acceptée -> t.test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_.dyspnee_II
## F = 0.64484, num df = 7, denom df = 6, p-value = 0.5768
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1132203 3.3006898
## sample estimates:
## ratio of variances
## 0.6448427test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_.dyspnee_II
## t = -0.30547, df = 13, p-value = 0.7648
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -50.47438 37.96866
## sample estimates:
## mean in group Non mean in group Oui
## 4.86000 11.11286significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.97532, p-value = 0.9578shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 1, p-value = 1la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_.dyspnee_III
## F = 14.736, num df = 11, denom df = 2, p-value = 0.1304
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3739384 77.4497782
## sample estimates:
## ratio of variances
## 14.73581test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_.dyspnee_III, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_.dyspnee_III
## t = 0.70036, df = 12.654, p-value = 0.4964
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -20.3518 39.7968
## sample estimates:
## mean in group Non mean in group Oui
## 9.7225 0.0000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.92228, p-value = 0.3763shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.85455, p-value = 0.2093la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_.dyspnee_IV
## F = 0.64307, num df = 9, denom df = 4, p-value = 0.5338
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07221726 3.03406275
## sample estimates:
## ratio of variances
## 0.6430717test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Stade_.dyspnee_IV
## t = 0.00012756, df = 6.6789, p-value = 0.9999
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -56.15399 56.15999
## sample estimates:
## mean in group Non mean in group Oui
## 7.779 7.776significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.93909, p-value = 0.4864shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.92215, p-value = 0.4476la normalité: accepté
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_.dyspnee_II
## F = 1.3196, num df = 11, denom df = 7, p-value = 0.7351
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.280201 4.959891
## sample estimates:
## ratio of variances
## 1.319598test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_.dyspnee_II
## t = -0.31894, df = 18, p-value = 0.7534
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -49.16868 36.20784
## sample estimates:
## mean in group Non mean in group Oui
## -39.81417 -33.33375significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.93707, p-value = 0.3146shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.75264, p-value = 0.04088la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_.dyspnee_III
## F = 1.6504, num df = 15, denom df = 3, p-value = 0.7574
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1157927 6.8536229
## sample estimates:
## ratio of variances
## 1.65036test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_.dyspnee_III, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_.dyspnee_III
## W = 20.5, p-value = 0.2935
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.87074, p-value = 0.06678shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.88775, p-value = 0.223la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 0.52338, num df = 11, denom df = 7, p-value = 0.324
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.111133 1.967187
## sample estimates:
## ratio of variances
## 0.5233777test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Stade_.dyspnee_IV
## t = 0.77999, df = 11.821, p-value = 0.4507
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -29.96563 63.29646
## sample estimates:
## mean in group Non mean in group Oui
## -30.55583 -47.22125significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.92886, p-value = 0.3994shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.83341, p-value = 0.08619la normalité: acceptée -> t-test sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.32355, num df = 10, denom df = 6, p-value = 0.1117
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05924476 1.31755770
## sample estimates:
## ratio of variances
## 0.3235548test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_.dyspnee_II
## t = 0.81742, df = 16, p-value = 0.4257
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -16.14829 36.41712
## sample estimates:
## mean in group Non mean in group Oui
## 20.31727 10.18286significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.86898, p-value = 0.06344shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.88102, p-value = 0.2738la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_.dyspnee_III
## F = 3.2534, num df = 11, denom df = 5, p-value = 0.2032
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4953526 13.1566872
## sample estimates:
## ratio of variances
## 3.253386test de variance: non accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_.dyspnee_III,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_.dyspnee_III
## t = 0.064238, df = 15.661, p-value = 0.9496
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -22.33453 23.72786
## sample estimates:
## mean in group Non mean in group Oui
## 16.60833 15.91167significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.84284, p-value = 0.02304shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.89762, p-value = 0.3969la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 1.3236, num df = 12, denom df = 4, p-value = 0.853
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1512517 5.4549464
## sample estimates:
## ratio of variances
## 1.323628test de variance: rejetée
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 23, p-value = 0.3873
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.82239, p-value = 0.04944shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.87655, p-value = 0.2116la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_.dyspnee_II
## F = 0.78588, num df = 7, denom df = 6, p-value = 0.7521
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.137983 4.022593
## sample estimates:
## ratio of variances
## 0.785878test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_.dyspnee_II
## t = 0.24456, df = 13, p-value = 0.8106
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -27.67527 34.74098
## sample estimates:
## mean in group Non mean in group Oui
## 0.450000 -3.082857significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.94657, p-value = 0.5874shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.90167, p-value = 0.3908la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_.dyspnee_III
## F = 1.4225, num df = 11, denom df = 2, p-value = 0.9679
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.0360966 7.4762960
## sample estimates:
## ratio of variances
## 1.422461test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_.dyspnee_III)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_.dyspnee_III
## t = 0.17089, df = 3.5928, p-value = 0.8735
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -44.09261 49.60428
## sample estimates:
## mean in group Non mean in group Oui
## -0.647500 -3.403333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.91897, p-value = 0.3484shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.78064, p-value = 0.05582la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_.dyspnee_IV
## F = 0.7896, num df = 9, denom df = 4, p-value = 0.7027
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08867281 3.72540968
## sample estimates:
## ratio of variances
## 0.7896032test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Stade_.dyspnee_IV
## t = -0.3738, df = 7.2777, p-value = 0.7192
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -43.23398 31.35198
## sample estimates:
## mean in group Non mean in group Oui
## -3.179 2.762significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.91294, p-value = 0.2327shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.91909, p-value = 0.4225la normalité: rejetée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.90097, num df = 11, denom df = 7, p-value = 0.8413
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1913092 3.3864017
## sample estimates:
## ratio of variances
## 0.9009651test de variance: accepté
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_.dyspnee_II
## t = 0.6451, df = 18, p-value = 0.527
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -14.53428 27.41511
## sample estimates:
## mean in group Non mean in group Oui
## 19.50667 13.06625significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.89345, p-value = 0.06318shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.93975, p-value = 0.6528la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_.dyspnee_III
## F = 2.1341, num df = 15, denom df = 3, p-value = 0.5828
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1497324 8.8624714
## sample estimates:
## ratio of variances
## 2.134093test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_.dyspnee_III,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_.dyspnee_III
## t = -0.6792, df = 6.6751, p-value = 0.5199
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -29.88818 16.65193
## sample estimates:
## mean in group Non mean in group Oui
## 15.60688 22.22500significativité: les resultats sont à la limite de la significativité, on peut en deduire ( avec precaution car nombre de patient non suffisant) que l’evolution du score QL30 entre la V2 et la V3 pour les patients inclus au Stade IIIB tend à etre dépendant
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.95938, p-value = 0.775shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.87996, p-value = 0.1882la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 0.67696, num df = 11, denom df = 7, p-value = 0.5403
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1437434 2.5444298
## sample estimates:
## ratio of variances
## 0.6769553test de variance: acceptée
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Stade_.dyspnee_IV
## t = -0.19305, df = 13.052, p-value = 0.8499
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -24.71770 20.66103
## sample estimates:
## mean in group Non mean in group Oui
## 16.11917 18.14750significativité: les resultats sont à la limite de la significativité, on peut en deduire ( avec precaution car nombre de patient non suffisant) que l’evolution du score QL30 entre la V2 et la V1 pour les patients inclus au Stade IIIB tend à etre dépendant
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.88724, p-value = 0.1284shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.90828, p-value = 0.3841la normalité: acceptée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.3164, num df = 10, denom df = 6, p-value = 0.105
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05793548 1.28844031
## sample estimates:
## ratio of variances
## 0.3164044test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_.dyspnee_II
## t = -0.48838, df = 16, p-value = 0.6319
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -26.36220 16.48999
## sample estimates:
## mean in group Non mean in group Oui
## 9.218182 14.154286significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.90942, p-value = 0.2097shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.75142, p-value = 0.02057la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_.dyspnee_III
## F = 2.5738, num df = 11, denom df = 5, p-value = 0.3068
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3918771 10.4083523
## sample estimates:
## ratio of variances
## 2.573778test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_.dyspnee_III)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_.dyspnee_III
## W = 31.5, p-value = 0.7075
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.87006, p-value = 0.05239shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.83105, p-value = 0.1417la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 2.63, num df = 12, denom df = 4, p-value = 0.3629
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3005335 10.8388463
## sample estimates:
## ratio of variances
## 2.630016test de variance: rejetée
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Stade_.dyspnee_IV
## t = 1.7157, df = 12.069, p-value = 0.1117
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -3.908504 32.957120
## sample estimates:
## mean in group Non mean in group Oui
## 15.17231 0.64800significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.84505, p-value = 0.08484shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.95908, p-value = 0.8108la normalité: acceptée -> t.test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_.dyspnee_II
## F = 0.11218, num df = 7, denom df = 6, p-value = 0.01081
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.01969602 0.57419446
## sample estimates:
## ratio of variances
## 0.1121781test de variance: rejeté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_.dyspnee_II, var.equal=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_.dyspnee_II
## t = -0.79541, df = 7.1764, p-value = 0.4519
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -40.74089 20.15481
## sample estimates:
## mean in group Non mean in group Oui
## -2.488750 7.804286significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.92863, p-value = 0.3658shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.99999, p-value = 0.994la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_.dyspnee_III
## F = 810.61, num df = 11, denom df = 2, p-value = 0.002465
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 20.57009 4260.45799
## sample estimates:
## ratio of variances
## 810.6065test de variance: rejetée
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_.dyspnee_III, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_.dyspnee_III
## t = -0.22831, df = 11.107, p-value = 0.8236
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -18.49454 15.01454
## sample estimates:
## mean in group Non mean in group Oui
## 1.966667 3.706667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.93223, p-value = 0.4702shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.94996, p-value = 0.7369la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_.dyspnee_IV
## F = 4.3893, num df = 9, denom df = 4, p-value = 0.1681
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4929211 20.7090855
## sample estimates:
## ratio of variances
## 4.389305test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Stade_.dyspnee_IV
## t = 1.2522, df = 12.998, p-value = 0.2326
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -9.27053 34.83253
## sample estimates:
## mean in group Non mean in group Oui
## 6.575 -6.206significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.84331, p-value = 0.03038shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.91421, p-value = 0.3847la normalité: rejetée
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.075733, num df = 11, denom df = 7, p-value = 0.0002956
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.01608106 0.28465400
## sample estimates:
## ratio of variances
## 0.07573329test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_.dyspnee_II
## W = 63.5, p-value = 0.2466
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.85607, p-value = 0.01676shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.87373, p-value = 0.3125la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_.dyspnee_III
## F = 3.2484, num df = 15, denom df = 3, p-value = 0.3613
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2279132 13.4898927
## sample estimates:
## ratio of variances
## 3.248382test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_.dyspnee_III,test.var=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_.dyspnee_III
## t = -1.0888, df = 8.7031, p-value = 0.3055
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -35.74764 12.59889
## sample estimates:
## mean in group Non mean in group Oui
## 4.860625 16.435000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.93778, p-value = 0.4698shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.91986, p-value = 0.4287la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 25.991, num df = 11, denom df = 7, p-value = 0.0002569
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 5.518815 97.689596
## sample estimates:
## ratio of variances
## 25.99069test de variance: acceptée
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 48, p-value = 1
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.84545, p-value = 0.03227shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.86588, p-value = 0.2103la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Hist_ADK
## F = 0.51074, num df = 11, denom df = 5, p-value = 0.328
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07776378 2.06542511
## sample estimates:
## ratio of variances
## 0.5107384test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Hist_ADK,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Hist_ADK
## W = 23, p-value = 0.226
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.86913, p-value = 0.09765shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.78088, p-value = 0.01781la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Hist_Epiderm
## F = 1.4257, num df = 9, denom df = 7, p-value = 0.6546
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2955852 5.9836094
## sample estimates:
## ratio of variances
## 1.425671test de variance: acceptée
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Hist_Epiderm,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Hist_Epiderm
## t = 0.4633, df = 15.943, p-value = 0.6494
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -38.75277 60.42077
## sample estimates:
## mean in group Non mean in group Oui
## -26.666 -37.500significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_ADK == "Non"]
## W = 0.83021, p-value = 0.0449shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_ADK == "Oui"]
## W = 0.75467, p-value = 0.02212la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Hist_ADK
## F = 0.47437, num df = 8, denom df = 5, p-value = 0.3329
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.0702025 2.2851726
## sample estimates:
## ratio of variances
## 0.4743703test de variance: non accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Hist_ADK, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Hist_ADK
## W = 11, p-value = 0.04686
## alternative hypothesis: true location shift is not equal to 0significativité: Oui D’apres les resultats le delta des dyspnée QLO30 entre la V3 et V2 est fortement relié au type histologique ADK
Pour verifier si le type histologique ADK et les la données initiales de la question dysppnée, à V3 ensuite à V2 sont independants j’ai realisé un X² test qui a montré l’independance de ces resulats , donc c’est la difference entre v3 et V2 qui est statistqument significative
chisq.test(BP_C$symp_QLC30_dyspnée_2_.,BP_C$Hist_ADK)## Warning in chisq.test(BP_C$symp_QLC30_dyspnée_2_., BP_C$Hist_ADK):
## L’approximation du Chi-2 est peut-être incorrecte##
## Pearson's Chi-squared test
##
## data: BP_C$symp_QLC30_dyspnée_2_. and BP_C$Hist_ADK
## X-squared = 3.4857, df = 3, p-value = 0.3226chisq.test(BP_C$symp_QLC30_dyspnée_3_.,BP_C$Hist_ADK)## Warning in chisq.test(BP_C$symp_QLC30_dyspnée_3_., BP_C$Hist_ADK):
## L’approximation du Chi-2 est peut-être incorrecte##
## Pearson's Chi-squared test
##
## data: BP_C$symp_QLC30_dyspnée_3_. and BP_C$Hist_ADK
## X-squared = 2.025, df = 3, p-value = 0.5672test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_Epiderm == "Non"]
## W = 0.90865, p-value = 0.2719shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Hist_Epiderm == "Oui"]
## W = 0.55218, p-value = 0.000131la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Hist_Epiderm
## F = 3.1247, num df = 9, denom df = 4, p-value = 0.2846
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3509039 14.7425209
## sample estimates:
## ratio of variances
## 3.124688test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Hist_Epiderm
## W = 33.5, p-value = 0.2864
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.91557, p-value = 0.3215shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.87802, p-value = 0.1238la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Hist_ADK
## F = 1.7419, num df = 9, denom df = 9, p-value = 0.421
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4326684 7.0129617
## sample estimates:
## ratio of variances
## 1.74192test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Hist_ADK)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Hist_ADK
## t = 0.97622, df = 16.772, p-value = 0.3428
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -23.27023 63.27223
## sample estimates:
## mean in group Non mean in group Oui
## -26.666 -46.667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.83841, p-value = 0.01552shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.95138, p-value = 0.7515la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Hist_Epiderm
## F = 0.54742, num df = 13, denom df = 5, p-value = 0.3534
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08438002 2.06196064
## sample estimates:
## ratio of variances
## 0.5474221test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Hist_Epiderm
## W = 29.5, p-value = 0.3061
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.95702, p-value = 0.7406shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.88446, p-value = 0.2902la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Hist_ADK
## F = 0.54006, num df = 11, denom df = 5, p-value = 0.3664
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08222763 2.18398606
## sample estimates:
## ratio of variances
## 0.5400561test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Hist_ADK
## t = -1.2434, df = 7.8061, p-value = 0.2497
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -76.86243 23.16077
## sample estimates:
## mean in group Non mean in group Oui
## -33.334167 -6.483333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.89059, p-value = 0.1722shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.97458, p-value = 0.9313la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Hist_Epiderm
## F = 1.5802, num df = 9, denom df = 7, p-value = 0.5594
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3276168 6.6320342
## sample estimates:
## ratio of variances
## 1.580167test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Hist_Epiderm
## t = 1.4609, df = 15.999, p-value = 0.1634
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -11.7799 64.0019
## sample estimates:
## mean in group Non mean in group Oui
## -12.779 -38.890significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_ADK == "Non"]
## W = 0.98173, p-value = 0.9726shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_ADK == "Oui"]
## W = 0.9732, p-value = 0.9132la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Hist_ADK
## F = 1.0661, num df = 8, denom df = 5, p-value = 0.9897
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1577698 5.1355900
## sample estimates:
## ratio of variances
## 1.066078test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Hist_ADK, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Hist_ADK
## t = -1.8932, df = 11.122, p-value = 0.08464
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -75.36790 5.61679
## sample estimates:
## mean in group Non mean in group Oui
## -6.172222 28.703333significativité: Non mais à la limoté de la significativité
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_Epiderm == "Non"]
## W = 0.93762, p-value = 0.5268shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Hist_Epiderm == "Oui"]
## W = 0.932, p-value = 0.6101la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Hist_Epiderm
## F = 0.6943, num df = 9, denom df = 4, p-value = 0.5948
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07796984 3.27574578
## sample estimates:
## ratio of variances
## 0.6942966test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Hist_Epiderm
## t = 0.81261, df = 6.8902, p-value = 0.4436
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -35.18744 71.85344
## sample estimates:
## mean in group Non mean in group Oui
## 13.889 -4.444significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.90097, p-value = 0.2245shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.96475, p-value = 0.8383la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Hist_ADK
## F = 1.4855, num df = 9, denom df = 9, p-value = 0.5649
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3689666 5.9804422
## sample estimates:
## ratio of variances
## 1.485457test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Hist_ADK
## t = 0.2789, df = 17.339, p-value = 0.7836
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -36.41221 47.52421
## sample estimates:
## mean in group Non mean in group Oui
## -34.444 -40.000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.93636, p-value = 0.3736shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.91335, p-value = 0.4588la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Hist_Epiderm
## F = 0.37386, num df = 13, denom df = 5, p-value = 0.142
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05762633 1.40819147
## sample estimates:
## ratio of variances
## 0.3738554test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Hist_Epiderm
## t = -0.0099821, df = 6.6648, p-value = 0.9923
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -63.62812 63.09860
## sample estimates:
## mean in group Non mean in group Oui
## -37.30143 -37.03667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.96714, p-value = 0.8787shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.83486, p-value = 0.1181la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Hist_ADK
## F = 0.28329, num df = 11, denom df = 5, p-value = 0.0757
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.04313308 1.14562536
## sample estimates:
## ratio of variances
## 0.2832903test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Hist_ADK
## t = 1.1821, df = 6.4579, p-value = 0.2789
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -18.43956 54.07289
## sample estimates:
## mean in group Non mean in group Oui
## 22.315000 4.498333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.83638, p-value = 0.03992shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.94645, p-value = 0.6755la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Hist_Epiderm
## F = 2.21, num df = 9, denom df = 7, p-value = 0.3084
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4581917 9.2752975
## sample estimates:
## ratio of variances
## 2.209958test de variance: acceptée
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Hist_Epiderm,var.test=FALSE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Hist_Epiderm
## W = 36, p-value = 0.7618
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_ADK == "Non"]
## W = 0.88473, p-value = 0.176shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_ADK == "Oui"]
## W = 0.85574, p-value = 0.175la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Hist_ADK
## F = 0.92976, num df = 8, denom df = 5, p-value = 0.8805
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1375955 4.4788940
## sample estimates:
## ratio of variances
## 0.9297567test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Hist_ADK
## t = 2.4346, df = 10.579, p-value = 0.03394
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## 2.738381 57.082730
## sample estimates:
## mean in group Non mean in group Oui
## 10.76556 -19.14500significativité: Oui significativité de del total QLC entre V3 et V2 en fonction dype histo ADK
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_Epiderm == "Non"]
## W = 0.93885, p-value = 0.5403shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Hist_Epiderm == "Oui"]
## W = 0.91043, p-value = 0.4702la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Hist_Epiderm
## F = 0.86065, num df = 9, denom df = 4, p-value = 0.7779
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09665132 4.06061078
## sample estimates:
## ratio of variances
## 0.8606493test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Hist_Epiderm
## t = -1.4335, df = 7.561, p-value = 0.1917
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -54.83774 13.05774
## sample estimates:
## mean in group Non mean in group Oui
## -8.162 12.728significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.883, p-value = 0.1412shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.86422, p-value = 0.08555la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Hist_ADK
## F = 2.1781, num df = 9, denom df = 9, p-value = 0.2617
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5410134 8.7690849
## sample estimates:
## ratio of variances
## 2.178117test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Hist_ADK
## t = -0.56787, df = 15.825, p-value = 0.5781
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -26.37722 15.23922
## sample estimates:
## mean in group Non mean in group Oui
## 14.146 19.715significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.93973, p-value = 0.4149shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.87633, p-value = 0.2526la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Hist_Epiderm
## F = 0.80302, num df = 13, denom df = 5, p-value = 0.6874
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1237775 3.0247008
## sample estimates:
## ratio of variances
## 0.8030163test de variance: acceptée
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Hist_Epiderm
## t = 0.61889, df = 8.6401, p-value = 0.552
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -18.52854 32.36330
## sample estimates:
## mean in group Non mean in group Oui
## 19.00571 12.08833significativité: non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.69171, p-value = 0.000702shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.95604, p-value = 0.7887la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Hist_ADK
## F = 0.34551, num df = 11, denom df = 5, p-value = 0.1318
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05260719 1.39726000
## sample estimates:
## ratio of variances
## 0.3455145test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Hist_ADK)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Hist_ADK
## W = 44, p-value = 0.4817
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.92396, p-value = 0.3912shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.8563, p-value = 0.1103la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Hist_Epiderm
## F = 25.947, num df = 9, denom df = 7, p-value = 0.0002892
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 5.379706 108.902815
## sample estimates:
## ratio of variances
## 25.94749test de variance: rejetée
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Hist_Epiderm,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Hist_Epiderm
## t = 0.33153, df = 9.8586, p-value = 0.7472
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -16.98111 22.90411
## sample estimates:
## mean in group Non mean in group Oui
## 12.4540 9.4925significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_ADK == "Non"]
## W = 0.8798, p-value = 0.1563shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_ADK == "Oui"]
## W = 0.90638, p-value = 0.413la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Hist_ADK
## F = 2.0504, num df = 8, denom df = 5, p-value = 0.4452
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3034357 9.8771840
## sample estimates:
## ratio of variances
## 2.050367test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Hist_ADK, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Hist_ADK
## t = 1.8933, df = 12.922, p-value = 0.08093
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -2.862682 43.244904
## sample estimates:
## mean in group Non mean in group Oui
## 10.39111 -9.80000significativité: Non, mais à la limote de la significativité
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_Epiderm == "Non"]
## W = 0.95984, p-value = 0.784shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Hist_Epiderm == "Oui"]
## W = 0.68644, p-value = 0.006871la normalité: non acceptée -> test de Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Hist_Epiderm
## F = 0.46186, num df = 9, denom df = 4, p-value = 0.3088
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05186654 2.17906841
## sample estimates:
## ratio of variances
## 0.4618551test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Hist_Epiderm
## W = 15, p-value = 0.2442
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.92035, p-value = 0.3599shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.82948, p-value = 0.03298la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Hist_ADK
## F = 9.1001, num df = 9, denom df = 9, p-value = 0.002983
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.260326 36.636781
## sample estimates:
## ratio of variances
## 9.100058test de variance: rejetée
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Hist_ADK,test.var=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Hist_ADK
## W = 39, p-value = 0.4268
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.8472, p-value = 0.02035shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.7456, p-value = 0.01803la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Hist_Epiderm
## F = 0.76927, num df = 13, denom df = 5, p-value = 0.6454
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1185759 2.8975916
## sample estimates:
## ratio of variances
## 0.7692706test de variance: acceptée
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Hist_Epiderm
## W = 55, p-value = 0.302
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.88746, p-value = 0.2617shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.85537, p-value = 0.05014la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$lignes_ttt_jamais
## F = 2.3167, num df = 6, denom df = 10, p-value = 0.2303
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5689209 12.6523601
## sample estimates:
## ratio of variances
## 2.31672test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$lignes_ttt_jamais)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$lignes_ttt_jamais
## W = 48, p-value = 0.3992
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.86912, p-value = 0.0637shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.92409, p-value = 0.5353la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$lignes_ttt_une
## F = 0.33505, num df = 11, denom df = 5, p-value = 0.1215
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05101391 1.35494192
## sample estimates:
## ratio of variances
## 0.3350501test de variance: acceptée
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$lignes_ttt_une
## t = -1.0568, df = 6.7297, p-value = 0.327
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -99.48696 38.37363
## sample estimates:
## mean in group Non mean in group Oui
## -41.66667 -11.11000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.89318, p-value = 0.2917shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.82602, p-value = 0.05397la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$lignes_ttt_jamais
## F = 4.1214, num df = 6, denom df = 7, p-value = 0.08586
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8051724 23.4730420
## sample estimates:
## ratio of variances
## 4.121353test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$lignes_ttt_jamais,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$lignes_ttt_jamais
## t = 1.2876, df = 8.4901, p-value = 0.2319
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -24.84777 89.13242
## sample estimates:
## mean in group Non mean in group Oui
## 23.80857 -8.33375significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_une == "Non"]
## W = 0.78645, p-value = 0.01429shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$lignes_ttt_une == "Oui"]
## W = 0.91237, p-value = 0.4522la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$lignes_ttt_une
## F = 0.18436, num df = 8, denom df = 5, p-value = 0.03596
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02728347 0.88810852
## sample estimates:
## ratio of variances
## 0.1843591test de variance: non accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$lignes_ttt_une
## t = -1.2572, df = 6.2456, p-value = 0.2537
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -103.01910 32.65021
## sample estimates:
## mean in group Non mean in group Oui
## -7.407778 27.776667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.93001, p-value = 0.4815shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.81365, p-value = 0.01423la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$lignes_ttt_jamais
## F = 1.7514, num df = 8, denom df = 10, p-value = 0.4004
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4543303 7.5224584
## sample estimates:
## ratio of variances
## 1.751394test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$lignes_ttt_jamais,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$lignes_ttt_jamais
## W = 46, p-value = 0.8137
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.80685, p-value = 0.00823shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.89317, p-value = 0.2916la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$lignes_ttt_une
## F = 0.41175, num df = 12, denom df = 6, p-value = 0.1799
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07673036 1.53513847
## sample estimates:
## ratio of variances
## 0.4117538test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$lignes_ttt_une
## W = 54.5, p-value = 0.4861
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.85381, p-value = 0.1331shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.94997, p-value = 0.6436la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$lignes_ttt_jamais
## F = 2.5677, num df = 6, denom df = 10, p-value = 0.1804
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6305636 14.0232476
## sample estimates:
## ratio of variances
## 2.567738test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$lignes_ttt_jamais,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$lignes_ttt_jamais
## t = 0.89338, df = 9.0104, p-value = 0.3949
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -29.28462 67.52280
## sample estimates:
## mean in group Non mean in group Oui
## -12.70000 -31.81909significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.94701, p-value = 0.5938shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.86544, p-value = 0.2086la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$lignes_ttt_une
## F = 0.30492, num df = 11, denom df = 5, p-value = 0.09364
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.04642627 1.23309316
## sample estimates:
## ratio of variances
## 0.3049193test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$lignes_ttt_une,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$lignes_ttt_une
## t = -0.94751, df = 6.5714, p-value = 0.3769
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -80.04935 34.68268
## sample estimates:
## mean in group Non mean in group Oui
## -31.945000 -9.261667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.96263, p-value = 0.841shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.94479, p-value = 0.6587la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$lignes_ttt_jamais
## F = 3.5941, num df = 6, denom df = 7, p-value = 0.1185
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7021691 20.4702059
## sample estimates:
## ratio of variances
## 3.59412test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$lignes_ttt_jamais, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$lignes_ttt_jamais
## t = 0.87517, df = 8.8285, p-value = 0.4047
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -28.90964 65.21714
## sample estimates:
## mean in group Non mean in group Oui
## 17.46000 -0.69375significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_une == "Non"]
## W = 0.9394, p-value = 0.5756shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$lignes_ttt_une == "Oui"]
## W = 0.98693, p-value = 0.9804la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$lignes_ttt_une
## F = 0.36656, num df = 8, denom df = 5, p-value = 0.1997
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05424727 1.76581160
## sample estimates:
## ratio of variances
## 0.3665581test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$lignes_ttt_une
## t = -0.24064, df = 7.4637, p-value = 0.8163
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -59.44376 48.33709
## sample estimates:
## mean in group Non mean in group Oui
## 5.556667 11.110000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.90242, p-value = 0.2664shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.91315, p-value = 0.2656la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$lignes_ttt_jamais
## F = 3.2589, num df = 8, denom df = 10, p-value = 0.08406
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.845393 13.997380
## sample estimates:
## ratio of variances
## 3.258898test de variance: rejetée
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$lignes_ttt_jamais)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$lignes_ttt_jamais
## t = 0.015876, df = 11.92, p-value = 0.9876
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -45.94283 46.61677
## sample estimates:
## mean in group Non mean in group Oui
## -37.03667 -37.37364significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.88613, p-value = 0.08639shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.91548, p-value = 0.4351la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$lignes_ttt_une
## F = 0.32512, num df = 12, denom df = 6, p-value = 0.09242
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06058679 1.21215542
## sample estimates:
## ratio of variances
## 0.3251235test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$lignes_ttt_une
## t = -0.44398, df = 8.1596, p-value = 0.6686
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -67.11610 45.38248
## sample estimates:
## mean in group Non mean in group Oui
## -41.02538 -30.15857significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.81479, p-value = 0.05719shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.95976, p-value = 0.7687la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$lignes_ttt_jamais
## F = 4.315, num df = 6, denom df = 10, p-value = 0.04169
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.05964 23.56557
## sample estimates:
## ratio of variances
## 4.314992test de variance: non accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$lignes_ttt_jamais,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$lignes_ttt_jamais
## t = -0.0068836, df = 7.7985, p-value = 0.9947
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -34.14588 33.94354
## sample estimates:
## mean in group Non mean in group Oui
## 16.31429 16.41545significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.9681, p-value = 0.8899shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.74886, p-value = 0.01942la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$lignes_ttt_une
## F = 0.18619, num df = 11, denom df = 5, p-value = 0.01929
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02834909 0.75295871
## sample estimates:
## ratio of variances
## 0.1861917test de variance: rejetée
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$lignes_ttt_une,var.test=FALSE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$lignes_ttt_une
## W = 23, p-value = 0.2496
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.9266, p-value = 0.5224shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.90327, p-value = 0.3091la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$lignes_ttt_jamais
## F = 2.9661, num df = 6, denom df = 7, p-value = 0.1809
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5794834 16.8935725
## sample estimates:
## ratio of variances
## 2.966142test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$lignes_ttt_jamais, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$lignes_ttt_jamais
## t = -0.48445, df = 9.3636, p-value = 0.6392
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -40.62142 26.22177
## sample estimates:
## mean in group Non mean in group Oui
## -5.038571 2.161250significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_une == "Non"]
## W = 0.93304, p-value = 0.5108shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$lignes_ttt_une == "Oui"]
## W = 0.92067, p-value = 0.5102la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$lignes_ttt_une
## F = 0.25312, num df = 8, denom df = 5, p-value = 0.08421
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03746019 1.21937274
## sample estimates:
## ratio of variances
## 0.253125test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$lignes_ttt_une
## t = 0.58507, df = 6.7105, p-value = 0.5776
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -29.73665 49.06332
## sample estimates:
## mean in group Non mean in group Oui
## 2.666667 -6.996667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.9337, p-value = 0.5174shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.90439, p-value = 0.209la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$lignes_ttt_jamais
## F = 1.5232, num df = 8, denom df = 10, p-value = 0.5236
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3951425 6.5424710
## sample estimates:
## ratio of variances
## 1.523231test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$lignes_ttt_jamais,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$lignes_ttt_jamais
## t = 0.47989, df = 15.358, p-value = 0.6381
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -16.63261 26.32372
## sample estimates:
## mean in group Non mean in group Oui
## 19.59556 14.75000significativité: les resultats sont à la limite de la significativité, on peut en deduire ( avec precaution car nombre de patient non suffisant) que l’evolution du score QL30 entre la V2 et la V3 pour les patients inclus au Stade IIIB tend à etre dépendant
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.88149, p-value = 0.0747shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.94439, p-value = 0.6785la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$lignes_ttt_une
## F = 0.54655, num df = 12, denom df = 6, p-value = 0.3514
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1018488 2.0376823
## sample estimates:
## ratio of variances
## 0.5465458test de variance: acceptée
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$lignes_ttt_une
## t = -1.1137, df = 9.634, p-value = 0.2924
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -36.69888 12.32240
## sample estimates:
## mean in group Non mean in group Oui
## 12.66462 24.85286significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.91059, p-value = 0.4shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.87925, p-value = 0.1018la normalité: acceptée -> test de Student Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$lignes_ttt_jamais
## F = 3.1742, num df = 6, denom df = 10, p-value = 0.1038
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7794957 17.3353830
## sample estimates:
## ratio of variances
## 3.174209test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$lignes_ttt_jamais)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$lignes_ttt_jamais
## t = 0.43141, df = 8.4433, p-value = 0.677
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -21.21965 31.09654
## sample estimates:
## mean in group Non mean in group Oui
## 14.155714 9.217273significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.88621, p-value = 0.1053shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.90394, p-value = 0.3977la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$lignes_ttt_une
## F = 0.25069, num df = 11, denom df = 5, p-value = 0.0522
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03816908 1.01378009
## sample estimates:
## ratio of variances
## 0.2506876test de variance: rejetée
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$lignes_ttt_une
## t = -0.57155, df = 6.2871, p-value = 0.5875
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -38.58048 23.83881
## sample estimates:
## mean in group Non mean in group Oui
## 8.680833 16.051667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.88505, p-value = 0.2498shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.93156, p-value = 0.5304la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$lignes_ttt_jamais
## F = 3.8726, num df = 6, denom df = 7, p-value = 0.0996
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7565762 22.0563260
## sample estimates:
## ratio of variances
## 3.872608test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$lignes_ttt_jamais, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$lignes_ttt_jamais
## t = 0.02839, df = 8.6396, p-value = 0.978
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -29.42769 30.17090
## sample estimates:
## mean in group Non mean in group Oui
## 2.512857 2.141250significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_une == "Non"]
## W = 0.90268, p-value = 0.2679shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$lignes_ttt_une == "Oui"]
## W = 0.92541, p-value = 0.5451la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$lignes_ttt_une
## F = 2.4529, num df = 8, denom df = 5, p-value = 0.3379
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3630001 11.8160731
## sample estimates:
## ratio of variances
## 2.452854test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$lignes_ttt_une
## t = 1.5646, df = 12.999, p-value = 0.1417
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -6.46480 40.41924
## sample estimates:
## mean in group Non mean in group Oui
## 9.105556 -7.871667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.86731, p-value = 0.115shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.92916, p-value = 0.4024la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$lignes_ttt_jamais
## F = 6.0777, num df = 8, denom df = 10, p-value = 0.01024
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.576628 26.104624
## sample estimates:
## ratio of variances
## 6.077731test de variance: non accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_total_LC13_2_vs_1~BP_C$lignes_ttt_jamais,test.var=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$lignes_ttt_jamais
## t = 0.41677, df = 10.152, p-value = 0.6855
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -23.71833 34.65994
## sample estimates:
## mean in group Non mean in group Oui
## 10.184444 4.713636significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.86945, p-value = 0.05142shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.76032, p-value = 0.01624la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$lignes_ttt_une
## F = 0.66693, num df = 12, denom df = 6, p-value = 0.5176
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1242817 2.4864946
## sample estimates:
## ratio of variances
## 0.6669259test de variance: acceptée
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$lignes_ttt_une
## W = 26.5, p-value = 0.1422
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.88632, p-value = 0.05902shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 1, p-value = 0.9998la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$typ_stent_Métallique
## F = 2.495, num df = 14, denom df = 2, p-value = 0.6455
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06328269 12.11753518
## sample estimates:
## ratio of variances
## 2.495015test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$typ_stent_Métallique,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$typ_stent_Métallique
## t = 0.094326, df = 4.3397, p-value = 0.929
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -61.22954 65.67487
## sample estimates:
## mean in group Non mean in group Oui
## -31.11067 -33.33333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.92157, p-value = 0.4817shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.89411, p-value = 0.1564la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$typ_stent_Silicone
## F = 0.43935, num df = 6, denom df = 10, p-value = 0.3263
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.107891 2.399413
## sample estimates:
## ratio of variances
## 0.4393464test de variance: acceptée
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$typ_stent_Silicone,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$typ_stent_Silicone
## t = -0.84861, df = 15.924, p-value = 0.4087
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -65.13613 27.90549
## sample estimates:
## mean in group Non mean in group Oui
## -42.85714 -24.24182significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Métallique == "Non"]
## W = 0.77603, p-value = 0.005066shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.98685, p-value = 0.7805la normalité: non acceptée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$typ_stent_Métallique
## F = 0.21411, num df = 11, denom df = 2, p-value = 0.06802
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.00543322 1.12532366
## sample estimates:
## ratio of variances
## 0.2141072test de variance: non accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$typ_stent_Métallique,var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$typ_stent_Métallique
## W = 13.5, p-value = 0.5299
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Silicone == "Non"]
## W = 0.91237, p-value = 0.4522shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.58021, p-value = 5.605e-05la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$typ_stent_Silicone
## F = 3.1537, num df = 5, denom df = 8, p-value = 0.145
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.654656 21.309812
## sample estimates:
## ratio of variances
## 3.153658test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$typ_stent_Silicone
## W = 29, p-value = 0.8475
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.91634, p-value = 0.1472shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.8494, p-value = 0.2242la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$typ_stent_Métallique
## F = 0.85742, num df = 15, denom df = 3, p-value = 0.7107
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.0601587 3.5607165
## sample estimates:
## ratio of variances
## 0.8574247test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$typ_stent_Métallique)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$typ_stent_Métallique
## t = 0.22691, df = 4.3837, p-value = 0.8307
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -67.66974 80.17224
## sample estimates:
## mean in group Non mean in group Oui
## -35.41625 -41.66750significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.91939, p-value = 0.4249shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.83028, p-value = 0.02113la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$typ_stent_Silicone
## F = 2.1992, num df = 7, denom df = 11, p-value = 0.2338
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5850965 10.3569061
## sample estimates:
## ratio of variances
## 2.199166test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$typ_stent_Silicone
## W = 44.5, p-value = 0.8109
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.92571, p-value = 0.2352shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.85473, p-value = 0.2532la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$typ_stent_Métallique
## F = 0.93314, num df = 14, denom df = 2, p-value = 0.7379
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02366795 4.53200104
## sample estimates:
## ratio of variances
## 0.9331445test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$typ_stent_Métallique)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$typ_stent_Métallique
## t = -0.43396, df = 2.8022, p-value = 0.6955
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -99.13822 76.17688
## sample estimates:
## mean in group Non mean in group Oui
## -26.29733 -14.81667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.80045, p-value = 0.04138shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.94457, p-value = 0.5756la normalité: non acceptée -> test de Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$typ_stent_Silicone
## F = 0.57346, num df = 6, denom df = 10, p-value = 0.5123
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1408265 3.1318726
## sample estimates:
## ratio of variances
## 0.573464test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$typ_stent_Silicone,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$typ_stent_Silicone
## t = -0.58267, df = 15.358, p-value = 0.5686
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -50.01295 28.50515
## sample estimates:
## mean in group Non mean in group Oui
## -30.95571 -20.20182significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Métallique == "Non"]
## W = 0.99095, p-value = 0.9999shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.99324, p-value = 0.8428la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$typ_stent_Métallique
## F = 1.0412, num df = 11, denom df = 2, p-value = 0.8253
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02642258 5.47262087
## sample estimates:
## ratio of variances
## 1.041236test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$typ_stent_Métallique, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$typ_stent_Métallique
## t = 0.018354, df = 3.1381, p-value = 0.9865
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -78.04985 78.97818
## sample estimates:
## mean in group Non mean in group Oui
## 7.870833 7.406667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Silicone == "Non"]
## W = 0.91223, p-value = 0.4512shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.94773, p-value = 0.6652la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$typ_stent_Silicone
## F = 1.1283, num df = 5, denom df = 8, p-value = 0.8354
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2342117 7.6238620
## sample estimates:
## ratio of variances
## 1.128262test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$typ_stent_Silicone,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$typ_stent_Silicone
## t = -0.85371, df = 10.388, p-value = 0.4125
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -63.28165 28.09388
## sample estimates:
## mean in group Non mean in group Oui
## -2.778333 14.815556significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.9491, p-value = 0.4755shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.73729, p-value = 0.02915la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$typ_stent_Métallique
## F = 1.2168, num df = 15, denom df = 3, p-value = 0.9962
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08537203 5.05306095
## sample estimates:
## ratio of variances
## 1.216783test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$typ_stent_Métallique,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$typ_stent_Métallique
## W = 34, p-value = 0.8861
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.95229, p-value = 0.7343shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.91379, p-value = 0.2386la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$typ_stent_Silicone
## F = 1.3397, num df = 7, denom df = 11, p-value = 0.6377
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3564293 6.3092232
## sample estimates:
## ratio of variances
## 1.339689test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$typ_stent_Silicone
## t = 0.87879, df = 13.563, p-value = 0.3948
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -26.14742 62.26242
## sample estimates:
## mean in group Non mean in group Oui
## -26.3875 -44.4450significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.90542, p-value = 0.1152shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.93162, p-value = 0.4947la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$typ_stent_Métallique
## F = 12.433, num df = 14, denom df = 2, p-value = 0.1537
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3153349 60.3811607
## sample estimates:
## ratio of variances
## 12.43255test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$typ_stent_Métallique,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$typ_stent_Métallique
## t = -1.4886, df = 12.909, p-value = 0.1606
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -30.498217 5.624883
## sample estimates:
## mean in group Non mean in group Oui
## 14.30333 26.74000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.96093, p-value = 0.8267shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.92764, p-value = 0.3875la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$typ_stent_Silicone
## F = 0.085891, num df = 6, denom df = 10, p-value = 0.007098
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02109234 0.46907735
## sample estimates:
## ratio of variances
## 0.08589078test de variance: rejetée
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$typ_stent_Silicone,var.test=FALSE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$typ_stent_Silicone
## W = 54, p-value = 0.1791
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Métallique == "Non"]
## W = 0.95561, p-value = 0.7199shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.89813, p-value = 0.3796la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$typ_stent_Métallique
## F = 1.2576, num df = 11, denom df = 2, p-value = 0.9516
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03191188 6.60955986
## sample estimates:
## ratio of variances
## 1.257553test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$typ_stent_Métallique,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$typ_stent_Métallique
## t = -0.17315, df = 3.3942, p-value = 0.8724
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -52.90921 47.10421
## sample estimates:
## mean in group Non mean in group Oui
## -1.779167 1.123333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Silicone == "Non"]
## W = 0.90261, p-value = 0.3896shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.8865, p-value = 0.1836la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$typ_stent_Silicone
## F = 1.3982, num df = 5, denom df = 8, p-value = 0.6404
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2902494 9.4479555
## sample estimates:
## ratio of variances
## 1.398211test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$typ_stent_Silicone,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$typ_stent_Silicone
## t = 0.87549, df = 9.548, p-value = 0.4028
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -20.26060 46.21172
## sample estimates:
## mean in group Non mean in group Oui
## 6.586667 -6.388889significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.91986, p-value = 0.1678shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.94883, p-value = 0.7088la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$typ_stent_Métallique
## F = 0.63548, num df = 15, denom df = 3, p-value = 0.4745
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.04458669 2.63902896
## sample estimates:
## ratio of variances
## 0.6354812test de variance: accepté
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$typ_stent_Métallique,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$typ_stent_Métallique
## t = -0.29298, df = 4.0087, p-value = 0.7841
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -43.63402 35.29777
## sample estimates:
## mean in group Non mean in group Oui
## 16.09688 20.26500significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.97256, p-value = 0.9173shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.86058, p-value = 0.04971la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$typ_stent_Silicone
## F = 1.2365, num df = 7, denom df = 11, p-value = 0.7224
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3289753 5.8232548
## sample estimates:
## ratio of variances
## 1.236499test de variance: acceptée
wilcox.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$typ_stent_Silicone
## W = 51, p-value = 0.8506
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.91009, p-value = 0.1358shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.76792, p-value = 0.04003la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$typ_stent_Métallique
## F = 0.79494, num df = 14, denom df = 2, p-value = 0.6289
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02016249 3.86076611
## sample estimates:
## ratio of variances
## 0.7949364test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$typ_stent_Métallique)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$typ_stent_Métallique
## W = 8, p-value = 0.09668
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.93533, p-value = 0.5971shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.84718, p-value = 0.03921la normalité: non acceptée -> test de Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$typ_stent_Silicone
## F = 1.2284, num df = 6, denom df = 10, p-value = 0.7363
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3016483 6.7084251
## sample estimates:
## ratio of variances
## 1.228351test de variance: acceptée
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$typ_stent_Silicone
## W = 55.5, p-value = 0.1345
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Métallique == "Non"]
## W = 0.8909, p-value = 0.121shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.99122, p-value = 0.8208la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$typ_stent_Métallique
## F = 1.5544, num df = 11, denom df = 2, p-value = 0.9116
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03944472 8.16975432
## sample estimates:
## ratio of variances
## 1.5544test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$typ_stent_Métallique, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$typ_stent_Métallique
## t = 0.099372, df = 3.7534, p-value = 0.9259
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -37.41287 40.11620
## sample estimates:
## mean in group Non mean in group Oui
## 2.585000 1.233333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Silicone == "Non"]
## W = 0.9891, p-value = 0.9869shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.91679, p-value = 0.3663la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$typ_stent_Silicone
## F = 0.23632, num df = 5, denom df = 8, p-value = 0.1288
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.04905703 1.59686335
## sample estimates:
## ratio of variances
## 0.2363213test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$typ_stent_Silicone,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$typ_stent_Silicone
## t = -0.17264, df = 12.22, p-value = 0.8658
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -26.23962 22.37962
## sample estimates:
## mean in group Non mean in group Oui
## 1.156667 3.086667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.86615, p-value = 0.02376shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.87314, p-value = 0.3102la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$typ_stent_Métallique
## F = 0.51499, num df = 15, denom df = 3, p-value = 0.3325
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03613283 2.13865592
## sample estimates:
## ratio of variances
## 0.5149908test de variance: accepté
t.test(BP_C$Del_total_LC13_2_vs_1~BP_C$typ_stent_Métallique,test.var=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$typ_stent_Métallique
## t = -0.74279, df = 3.8096, p-value = 0.5008
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -65.44973 38.25098
## sample estimates:
## mean in group Non mean in group Oui
## 4.455625 18.055000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.86786, p-value = 0.1436shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.83605, p-value = 0.0248la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$typ_stent_Silicone
## F = 0.95401, num df = 7, denom df = 11, p-value = 0.9877
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2538183 4.4928858
## sample estimates:
## ratio of variances
## 0.9540109test de variance: acceptée
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$typ_stent_Silicone
## W = 58.5, p-value = 0.4399
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.93661, p-value = 0.6084shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.85978, p-value = 0.0572la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.35637, num df = 6, denom df = 10, p-value = 0.2191
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08751362 1.94623520
## sample estimates:
## ratio of variances
## 0.3563669test de variance: accepté
t.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = -1.6665, df = 15.982, p-value = 0.1151
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -77.709175 9.308136
## sample estimates:
## mean in group Non mean in group Oui
## -52.38143 -18.18091significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.8519, p-value = 0.2006shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.74528, p-value = 0.003126la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## F = 0.98786, num df = 4, denom df = 9, p-value = 0.9225
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.209378 8.796596
## sample estimates:
## ratio of variances
## 0.9878619test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Site_Trachée_ou.Carène, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## W = 15.5, p-value = 0.2304
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.94179, p-value = 0.6008shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.78532, p-value = 0.006031la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 1.1612, num df = 8, denom df = 10, p-value = 0.8085
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.301216 4.987307
## sample estimates:
## ratio of variances
## 1.161155test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## W = 54, p-value = 0.7534
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.85113, p-value = 0.1259shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.98655, p-value = 0.9917la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.1253, num df = 6, denom df = 10, p-value = 0.01932
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03076951 0.68429008
## sample estimates:
## ratio of variances
## 0.1252975test de variance: rejetée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = -1.7837, df = 13.456, p-value = 0.09704
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -60.989706 5.721395
## sample estimates:
## mean in group Non mean in group Oui
## -41.27143 -13.63727significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.97858, p-value = 0.9269shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.97454, p-value = 0.9294la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## F = 2.1222, num df = 4, denom df = 9, p-value = 0.3204
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4498119 18.8979395
## sample estimates:
## ratio of variances
## 2.122248test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Site_Trachée_ou.Carène, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## t = -1.2261, df = 5.9598, p-value = 0.2664
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -84.966 28.302
## sample estimates:
## mean in group Non mean in group Oui
## -11.110 17.222significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.94971, p-value = 0.6868shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.90566, p-value = 0.2165la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 1.5257, num df = 8, denom df = 10, p-value = 0.522
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3957912 6.5532118
## sample estimates:
## ratio of variances
## 1.525732test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = 0.12591, df = 15.349, p-value = 0.9014
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -41.03097 46.19380
## sample estimates:
## mean in group Non mean in group Oui
## -35.80222 -38.38364significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.94696, p-value = 0.702shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.92111, p-value = 0.328la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.11944, num df = 6, denom df = 10, p-value = 0.01706
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02933156 0.65231118
## sample estimates:
## ratio of variances
## 0.119442test de variance: non accepté
t.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = 1.1521, df = 13.324, p-value = 0.2695
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -10.25053 33.79885
## sample estimates:
## mean in group Non mean in group Oui
## 23.57143 11.79727significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.98018, p-value = 0.9356shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.88132, p-value = 0.1351la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## F = 1.501, num df = 4, denom df = 9, p-value = 0.5616
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3181451 13.3662260
## sample estimates:
## ratio of variances
## 1.501034test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## t = 1.5121, df = 6.7746, p-value = 0.1757
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -13.04474 58.46074
## sample estimates:
## mean in group Non mean in group Oui
## 13.940 -8.768significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.96453, p-value = 0.8444shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.85842, p-value = 0.05493la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.559, num df = 8, denom df = 10, p-value = 0.4215
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1450095 2.4009586
## sample estimates:
## ratio of variances
## 0.5589959test de variance: accepté
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = -0.95654, df = 17.892, p-value = 0.3515
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -28.83689 10.79871
## sample estimates:
## mean in group Non mean in group Oui
## 11.97000 20.98909significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.80572, p-value = 0.04664shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.9096, p-value = 0.2412la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.32196, num df = 6, denom df = 10, p-value = 0.1784
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07906432 1.75832944
## sample estimates:
## ratio of variances
## 0.3219603test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Site_Trachée_ou.Carène)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## W = 47.5, p-value = 0.4407
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.74955, p-value = 0.02948shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.9781, p-value = 0.9542la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## F = 2.4267, num df = 4, denom df = 9, p-value = 0.2479
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5143309 21.6085792
## sample estimates:
## ratio of variances
## 2.426654test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Site_Trachée_ou.Carène, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## W = 46.5, p-value = 0.01005
## alternative hypothesis: true location shift is not equal to 0significativité: Oui Je teste quand meme un test de Student pour voir la difference les donnée du t-test ne sont pas à retranscrire mais justepour verification
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Site_Trachée_ou.Carène, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## t = 2.326, df = 5.7104, p-value = 0.06113
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -1.870411 59.372411
## sample estimates:
## mean in group Non mean in group Oui
## 21.482 -7.269test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.87506, p-value = 0.1392shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.79649, p-value = 0.008458la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 1.164, num df = 8, denom df = 10, p-value = 0.8058
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3019426 4.9993378
## sample estimates:
## ratio of variances
## 1.163956test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Site_Trachée_ou.Carène,test.var=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## W = 36.5, p-value = 0.3417
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.72863, p-value = 0.02386shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.87054, p-value = 0.04269la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Type_intervention
## F = 0.67584, num df = 13, denom df = 3, p-value = 0.5322
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.04724655 2.93798538
## sample estimates:
## ratio of variances
## 0.6758374test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Type_intervention)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Type_intervention
## W = 33, p-value = 0.6211
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Type_intervention == "urgente"]
## W = 0.75, p-value < 2.2e-16shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Type_intervention == "programmée"]
## W = 0.88367, p-value = 0.09768la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Type_intervention
## F = 0.60607, num df = 11, denom df = 2, p-value = 0.4724
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.01537965 3.18541932
## sample estimates:
## ratio of variances
## 0.6060667test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Type_intervention, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Type_intervention
## W = 12.5, p-value = 0.4323
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.80298, p-value = 0.08568shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.91917, p-value = 0.1871la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Type_intervention
## F = 0.80749, num df = 14, denom df = 4, p-value = 0.6789
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09298837 3.14268150
## sample estimates:
## ratio of variances
## 0.8074899test de variance: acceptée
t.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Type_intervention
## t = 0.52348, df = 6.3124, p-value = 0.6185
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -48.24851 74.91385
## sample estimates:
## mean in group programmée mean in group urgente
## -33.33333 -46.66600significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.984, p-value = 0.9251shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.87709, p-value = 0.05284la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Type_intervention
## F = 0.84563, num df = 13, denom df = 3, p-value = 0.7089
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05911636 3.67609870
## sample estimates:
## ratio of variances
## 0.8456287test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Type_intervention
## t = 1.4393, df = 4.5633, p-value = 0.215
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -27.62380 93.50023
## sample estimates:
## mean in group programmée mean in group urgente
## -17.06429 -50.00250significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Type_intervention == "urgente"]
## W = 0.75, p-value < 2.2e-16shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Type_intervention == "programmée"]
## W = 0.97624, p-value = 0.9641la normalité: rejetée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Type_intervention
## F = 2.3994, num df = 11, denom df = 2, p-value = 0.6617
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06088653 12.61076221
## sample estimates:
## ratio of variances
## 2.399358test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Type_intervention, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Type_intervention
## W = 10.5, p-value = 0.3106
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.86153, p-value = 0.2338shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.93743, p-value = 0.3511la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Type_intervention
## F = 1.4127, num df = 14, denom df = 4, p-value = 0.8004
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1626825 5.4980988
## sample estimates:
## ratio of variances
## 1.412698test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Type_intervention
## t = 1.683, df = 8.1386, p-value = 0.1302
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -12.20353 78.87020
## sample estimates:
## mean in group programmée mean in group urgente
## -28.88867 -62.22200significativité:Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.86491, p-value = 0.2782shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.85735, p-value = 0.02799la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Type_intervention
## F = 1.1259, num df = 13, denom df = 3, p-value = 0.946
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07870991 4.89450665
## sample estimates:
## ratio of variances
## 1.125904test de variance: accepté
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Type_intervention
## W = 19, p-value = 0.3817
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Type_intervention == "urgente"]
## W = 0.84631, p-value = 0.2306shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Type_intervention == "programmée"]
## W = 0.96324, p-value = 0.8289la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Type_intervention
## F = 34.019, num df = 11, denom df = 2, p-value = 0.05778
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8632787 178.8014991
## sample estimates:
## ratio of variances
## 34.01927test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Type_intervention)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Type_intervention
## t = 1.8917, df = 12.768, p-value = 0.08143
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -2.437204 36.257204
## sample estimates:
## mean in group programmée mean in group urgente
## 2.183333 -14.726667significativité: non , mais à la limite de la significativité
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.77095, p-value = 0.04599shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.95809, p-value = 0.6592la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Type_intervention
## F = 0.59702, num df = 14, denom df = 4, p-value = 0.4228
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06875129 2.32355320
## sample estimates:
## ratio of variances
## 0.5970206test de variance: accepté
wilcox.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Type_intervention
## W = 27, p-value = 0.3949
## alternative hypothesis: true location shift is not equal to 0significativité: Urgente
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.85382, p-value = 0.2388shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.8686, p-value = 0.04011la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Type_intervention
## F = 1.7725, num df = 13, denom df = 3, p-value = 0.7037
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1239097 7.7052155
## sample estimates:
## ratio of variances
## 1.772464test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Type_intervention, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Type_intervention
## W = 24, p-value = 0.7097
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Type_intervention == "urgente"]
## W = 0.80517, p-value = 0.1269shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Type_intervention == "programmée"]
## W = 0.91851, p-value = 0.2738la normalité: urgente acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Type_intervention
## F = 3.1352, num df = 11, denom df = 2, p-value = 0.5332
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07956058 16.47851495
## sample estimates:
## ratio of variances
## 3.135248test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Type_intervention, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Type_intervention
## t = 1.4191, df = 5.7245, p-value = 0.208
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -11.35069 41.83736
## sample estimates:
## mean in group programmée mean in group urgente
## 5.363333 -9.880000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.8873, p-value = 0.3437shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.89763, p-value = 0.08758la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Type_intervention
## F = 11.954, num df = 14, denom df = 4, p-value = 0.02779
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.376592 46.524003
## sample estimates:
## ratio of variances
## 11.95401test de variance: non accepté
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Type_intervention,test.var=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Type_intervention
## W = 32.5, p-value = 0.6941
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.86119, p-value = 0.1234shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.79137, p-value = 0.01139la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Atelectasie_RT
## F = 3.5993, num df = 7, denom df = 9, p-value = 0.07818
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8575885 17.3603930
## sample estimates:
## ratio of variances
## 3.599339test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Atelectasie_RT)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Atelectasie_RT
## W = 28.5, p-value = 0.3121
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Atelectasie_RT == "Absent"]
## W = 0.80442, p-value = 0.04529shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Atelectasie_RT == "Présent"]
## W = 0.87746, p-value = 0.1781la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Atelectasie_RT
## F = 0.62958, num df = 6, denom df = 7, p-value = 0.5893
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1229991 3.5857698
## sample estimates:
## ratio of variances
## 0.6295827test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Atelectasie_RT, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Atelectasie_RT
## W = 32, p-value = 0.6594
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.77596, p-value = 0.01082shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.93365, p-value = 0.4489la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Atelectasie_RT
## F = 1.0576, num df = 8, denom df = 10, p-value = 0.9152
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.274363 4.542696
## sample estimates:
## ratio of variances
## 1.057639test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Atelectasie_RT
## W = 43.5, p-value = 0.6657
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.94005, p-value = 0.6115shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.94017, p-value = 0.5549la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Atelectasie_RT
## F = 3.7017, num df = 7, denom df = 9, p-value = 0.0722
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8819888 17.8543355
## sample estimates:
## ratio of variances
## 3.701748test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Atelectasie_RT
## t = -0.17333, df = 9.9896, p-value = 0.8659
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -50.03639 42.81439
## sample estimates:
## mean in group Absent mean in group Présent
## -26.390 -22.779significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Atelectasie_RT == "Absent"]
## W = 0.90684, p-value = 0.3744shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Atelectasie_RT == "Présent"]
## W = 0.93224, p-value = 0.5367la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Atelectasie_RT
## F = 0.72216, num df = 6, denom df = 7, p-value = 0.7075
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1410847 4.1130154
## sample estimates:
## ratio of variances
## 0.7221555test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Atelectasie_RT, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Atelectasie_RT
## t = 0.014695, df = 12.995, p-value = 0.9885
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -43.57209 44.16887
## sample estimates:
## mean in group Absent mean in group Présent
## 7.937143 7.638750significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.92224, p-value = 0.4111shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.93462, p-value = 0.4595la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Atelectasie_RT
## F = 0.74253, num df = 8, denom df = 10, p-value = 0.6867
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1926193 3.1892446
## sample estimates:
## ratio of variances
## 0.7425263test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Atelectasie_RT
## t = -0.096684, df = 17.929, p-value = 0.924
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -43.41407 39.59508
## sample estimates:
## mean in group Absent mean in group Présent
## -38.27222 -36.36273significativité:Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.78789, p-value = 0.02121shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.93904, p-value = 0.5424la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Atelectasie_RT
## F = 3.5883, num df = 7, denom df = 9, p-value = 0.07886
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8549516 17.3070127
## sample estimates:
## ratio of variances
## 3.588272test de variance: accepté
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Atelectasie_RT
## W = 49, p-value = 0.4598
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Atelectasie_RT == "Absent"]
## W = 0.91396, p-value = 0.424shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Atelectasie_RT == "Présent"]
## W = 0.86453, p-value = 0.1332la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Atelectasie_RT
## F = 1.2878, num df = 6, denom df = 7, p-value = 0.741
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2515983 7.3347998
## sample estimates:
## ratio of variances
## 1.28783test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Atelectasie_RT)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Atelectasie_RT
## t = -0.91945, df = 12.125, p-value = 0.3758
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -43.80336 17.78407
## sample estimates:
## mean in group Absent mean in group Présent
## -8.137143 4.872500significativité: non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.87518, p-value = 0.1396shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.95989, p-value = 0.7703la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Atelectasie_RT
## F = 1.8775, num df = 8, denom df = 10, p-value = 0.3463
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4870506 8.0642167
## sample estimates:
## ratio of variances
## 1.877527test de variance: accepté
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Atelectasie_RT
## t = 0.67632, df = 14.317, p-value = 0.5096
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -14.83598 28.54325
## sample estimates:
## mean in group Absent mean in group Présent
## 20.70000 13.84636significativité: Absent
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.84885, p-value = 0.09275shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.8953, p-value = 0.1944la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Atelectasie_RT
## F = 1.7765, num df = 7, denom df = 9, p-value = 0.4147
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4232629 8.5682245
## sample estimates:
## ratio of variances
## 1.776454test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Atelectasie_RT, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Atelectasie_RT
## t = 0.44233, df = 12.718, p-value = 0.6657
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -17.67487 26.75037
## sample estimates:
## mean in group Absent mean in group Présent
## 13.65875 9.12100significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Atelectasie_RT == "Absent"]
## W = 0.93505, p-value = 0.5946shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Atelectasie_RT == "Présent"]
## W = 0.89769, p-value = 0.2754la normalité: Absent acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Atelectasie_RT
## F = 0.38482, num df = 6, denom df = 7, p-value = 0.2651
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07518012 2.19171226
## sample estimates:
## ratio of variances
## 0.3848167test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Atelectasie_RT, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Atelectasie_RT
## t = -0.58898, df = 11.839, p-value = 0.5669
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -33.26092 19.12200
## sample estimates:
## mean in group Absent mean in group Présent
## -1.455714 5.613750significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.86623, p-value = 0.112shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.85743, p-value = 0.05333la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Atelectasie_RT
## F = 0.53349, num df = 8, denom df = 10, p-value = 0.385
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1383928 2.2914033
## sample estimates:
## ratio of variances
## 0.5334891test de variance: accepté
t.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Atelectasie_RT,test.var=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Atelectasie_RT
## t = 0.77394, df = 17.821, p-value = 0.4491
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -15.49737 33.55414
## sample estimates:
## mean in group Absent mean in group Présent
## 12.141111 3.112727significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Sexe == "Homme"]
## W = 0.88659, p-value = 0.126shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1[BP_C$Sexe == "Femme"]
## W = 0.85856, p-value = 0.1469la normalité: non acceptée -> test de S
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Sexe
## F = 0.53417, num df = 6, denom df = 10, p-value = 0.4572
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1311766 2.9172672
## sample estimates:
## ratio of variances
## 0.5341685test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_1~BP_C$Sexe,vartest=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1 by BP_C$Sexe
## W = 36.5, p-value = 0.8883
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Sexe == "Homme"]
## W = 0.76266, p-value = 0.007599shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2[BP_C$Sexe == "Femme"]
## W = 0.95138, p-value = 0.7515la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Sexe
## F = 1.5429, num df = 5, denom df = 8, p-value = 0.5572
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3202784 10.4254338
## sample estimates:
## ratio of variances
## 1.542869test de variance: accepté
wilcox.test(BP_C$Del_dyspnée_QLQ30_3_vs_2~BP_C$Sexe, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_2 by BP_C$Sexe
## W = 35, p-value = 0.3362
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Sexe == "Homme"]
## W = 0.93131, p-value = 0.3942shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1[BP_C$Sexe == "Femme"]
## W = 0.77983, p-value = 0.01735la normalité: acceptée -> test de W
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Sexe
## F = 1.1947, num df = 7, denom df = 11, p-value = 0.7598
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3178475 5.6262789
## sample estimates:
## ratio of variances
## 1.194674test de variance: acceptée
wilcox.test(BP_C$Del_dyspnée_QLQ30_2_vs_1~BP_C$Sexe,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1 by BP_C$Sexe
## W = 32.5, p-value = 0.2315
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Sexe == "Homme"]
## W = 0.93796, p-value = 0.4969shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1[BP_C$Sexe == "Femme"]
## W = 0.90685, p-value = 0.3745la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Sexe
## F = 0.53172, num df = 6, denom df = 10, p-value = 0.4538
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1305755 2.9038992
## sample estimates:
## ratio of variances
## 0.5317208test de variance: acceptée
t.test(BP_C$Del_dyspnée_LC13_3_vs_1~BP_C$Sexe,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1 by BP_C$Sexe
## t = 0.050703, df = 15.573, p-value = 0.9602
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -38.31678 40.19029
## sample estimates:
## mean in group Femme mean in group Homme
## -23.81143 -24.74818significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Sexe == "Homme"]
## W = 0.96654, p-value = 0.8636shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2[BP_C$Sexe == "Femme"]
## W = 0.88053, p-value = 0.2715la normalité: rejetée -> test de S
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Sexe
## F = 0.42528, num df = 5, denom df = 8, p-value = 0.3613
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08828318 2.87371991
## sample estimates:
## ratio of variances
## 0.4252844test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_3_vs_2~BP_C$Sexe, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_2 by BP_C$Sexe
## t = 0.78735, df = 12.999, p-value = 0.4452
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -25.82986 55.45319
## sample estimates:
## mean in group Femme mean in group Homme
## 16.665000 1.853333significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Sexe == "Homme"]
## W = 0.93734, p-value = 0.4644shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1[BP_C$Sexe == "Femme"]
## W = 0.8908, p-value = 0.2381la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Sexe
## F = 0.57527, num df = 7, denom df = 11, p-value = 0.4746
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1530515 2.7091947
## sample estimates:
## ratio of variances
## 0.5752653test de variance: accepté
t.test(BP_C$Del_dyspnée_LC13_2_vs_1~BP_C$Sexe,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1 by BP_C$Sexe
## t = -0.50822, df = 17.591, p-value = 0.6176
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -49.97660 30.53327
## sample estimates:
## mean in group Femme mean in group Homme
## -43.05500 -33.33333significativité:Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Sexe == "Homme"]
## W = 0.82919, p-value = 0.02278shapiro.test(BP_C$Del_total_QLC30_3_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1[BP_C$Sexe == "Femme"]
## W = 0.90582, p-value = 0.3677la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Sexe
## F = 0.58264, num df = 6, denom df = 10, p-value = 0.5251
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.143079 3.181966
## sample estimates:
## ratio of variances
## 0.5826365test de variance: accepté
wilcox.test(BP_C$Del_total_QLC30_3_vs_1~BP_C$Sexe,var.test=TRUE)##
## Wilcoxon rank sum exact test
##
## data: BP_C$Del_total_QLC30_3_vs_1 by BP_C$Sexe
## W = 41, p-value = 0.8601
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Sexe == "Homme"]
## W = 0.93595, p-value = 0.54shapiro.test(BP_C$Del_total_QLC30_3_vs_2[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_2[BP_C$Sexe == "Femme"]
## W = 0.75039, p-value = 0.0201la normalité: acceptée -> wilcox test
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Sexe
## F = 0.36035, num df = 5, denom df = 8, p-value = 0.2759
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07480451 2.43497355
## sample estimates:
## ratio of variances
## 0.3603539test de variance: accepté
t.test(BP_C$Del_total_QLC30_3_vs_2~BP_C$Sexe)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_3_vs_2 by BP_C$Sexe
## t = -0.77635, df = 12.938, p-value = 0.4515
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -38.43799 18.12244
## sample estimates:
## mean in group Femme mean in group Homme
## -7.293333 2.864444significativité: non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Sexe == "Homme"]
## W = 0.90426, p-value = 0.18shapiro.test(BP_C$Del_total_QLC30_2_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1[BP_C$Sexe == "Femme"]
## W = 0.9373, p-value = 0.5847la normalité: non acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Sexe
## F = 1.6416, num df = 7, denom df = 11, p-value = 0.4439
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4367521 7.7310330
## sample estimates:
## ratio of variances
## 1.641593test de variance: accepté
t.test(BP_C$Del_total_QLC30_2_vs_1~BP_C$Sexe,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_QLC30_2_vs_1 by BP_C$Sexe
## t = 1.6572, df = 12.525, p-value = 0.1223
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -5.029103 37.619103
## sample estimates:
## mean in group Femme mean in group Homme
## 26.7075 10.4125significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Sexe == "Homme"]
## W = 0.78122, p-value = 0.005328shapiro.test(BP_C$Del_total_LC13_3_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1[BP_C$Sexe == "Femme"]
## W = 0.89287, p-value = 0.2899la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Sexe
## F = 3.1656, num df = 6, denom df = 10, p-value = 0.1046
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7773768 17.2882603
## sample estimates:
## ratio of variances
## 3.165581test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_3_vs_1~BP_C$Sexe, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_3_vs_1 by BP_C$Sexe
## W = 55, p-value = 0.1467
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Sexe == "Homme"]
## W = 0.89517, p-value = 0.2253shapiro.test(BP_C$Del_total_LC13_3_vs_2[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_2[BP_C$Sexe == "Femme"]
## W = 0.98107, p-value = 0.9567la normalité: Homme acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Sexe
## F = 0.41469, num df = 5, denom df = 8, p-value = 0.3472
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08608476 2.80215887
## sample estimates:
## ratio of variances
## 0.414694test de variance: accepté
t.test(BP_C$Del_total_LC13_3_vs_2~BP_C$Sexe, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_total_LC13_3_vs_2 by BP_C$Sexe
## t = -1.0232, df = 13, p-value = 0.3249
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -36.02161 12.86717
## sample estimates:
## mean in group Femme mean in group Homme
## -4.631667 6.945556significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Sexe == "Homme"]
## W = 0.68473, p-value = 0.000603shapiro.test(BP_C$Del_total_LC13_2_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1[BP_C$Sexe == "Femme"]
## W = 0.92896, p-value = 0.5066la normalité: non acceptée -> test de wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Sexe
## F = 1.9443, num df = 7, denom df = 11, p-value = 0.3118
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5172754 9.1563907
## sample estimates:
## ratio of variances
## 1.944251test de variance: accepté
wilcox.test(BP_C$Del_total_LC13_2_vs_1~BP_C$Sexe,test.var=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_total_LC13_2_vs_1 by BP_C$Sexe
## W = 78.5, p-value = 0.02049
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.79888, p-value = 0.009099shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.66578, p-value = 0.002641la normalité: non acceptée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_IIIA
## F = 0.99166, num df = 11, denom df = 5, p-value = 0.9143
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1509874 4.0102623
## sample estimates:
## ratio of variances
## 0.9916578test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_IIIA
## W = 49.5, p-value = 0.1962
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.80604, p-value = 0.01098shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.82162, p-value = 0.09114la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_IIIB
## F = 2.5913, num df = 11, denom df = 5, p-value = 0.3033
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3945478 10.4792864
## sample estimates:
## ratio of variances
## 2.591318test de variance:accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_IIIB,var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_IIIB
## W = 22, p-value = 0.1796
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.80843, p-value = 0.0117shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.86625, p-value = 0.2116la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_IV
## F = 0.54812, num df = 11, denom df = 5, p-value = 0.3771
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08345528 2.21659283
## sample estimates:
## ratio of variances
## 0.5481191test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_IV
## W = 36.5, p-value = 1
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIA == "Non"]
## W = 0.80615, p-value = 0.01101shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIA == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_IIIA
## F = 2.796, num df = 11, denom df = 2, p-value = 0.5857
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07095246 14.69561037
## sample estimates:
## ratio of variances
## 2.796027test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_IIIA
## W = 24, p-value = 0.3677
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIB == "Non"]
## W = 0.72819, p-value = 0.003025shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IIIB == "Oui"]
## W = 0.91545, p-value = 0.4732la normalité: acceptée -> wilcox. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_IIIB
## F = 0.70313, num df = 8, denom df = 5, p-value = 0.6256
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1040574 3.3871892
## sample estimates:
## ratio of variances
## 0.7031338test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_IIIB,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_IIIB
## W = 21.5, p-value = 0.5038
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IV == "Non"]
## W = 0.8463, p-value = 0.06783shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_IV == "Oui"]
## W = 0.76978, p-value = 0.0309la normalité: acceptée -> wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_IV
## F = 0.89075, num df = 8, denom df = 5, p-value = 0.8401
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1318227 4.2909806
## sample estimates:
## ratio of variances
## 0.8907484test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_IV
## W = 26.5, p-value = 1
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.88034, p-value = 0.03928shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.97136, p-value = 0.8499la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_IIIA
## F = 0.40571, num df = 15, denom df = 3, p-value = 0.2046
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02846546 1.68483402
## sample estimates:
## ratio of variances
## 0.40571test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_IIIA
## W = 35.5, p-value = 0.7668
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.89548, p-value = 0.1387shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.81042, p-value = 0.03697la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_IIIB
## F = 5.0179, num df = 11, denom df = 7, p-value = 0.0421
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.065497 18.860562
## sample estimates:
## ratio of variances
## 5.017925test de variance: non accepté
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_IIIB,var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_IIIB
## W = 39, p-value = 0.4926
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.83298, p-value = 0.02277shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.93443, p-value = 0.5573la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_IV
## F = 0.58092, num df = 11, denom df = 7, p-value = 0.404
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1233523 2.1834821
## sample estimates:
## ratio of variances
## 0.5809238test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_IV
## W = 53.5, p-value = 0.6865
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.90457, p-value = 0.1817shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.77516, p-value = 0.03473la normalité: rejetée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_IIIA
## F = 1.4732, num df = 11, denom df = 5, p-value = 0.7026
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2242987 5.9574289
## sample estimates:
## ratio of variances
## 1.473153test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_IIIA
## W = 46.5, p-value = 0.3327
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.89138, p-value = 0.1228shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.96005, p-value = 0.8201la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_IIIB
## F = 0.79894, num df = 11, denom df = 5, p-value = 0.7
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1216448 3.2309156
## sample estimates:
## ratio of variances
## 0.7989409test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_IIIB)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_IIIB
## t = -2.6323, df = 9.1303, p-value = 0.02694
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -82.555320 -6.333014
## sample estimates:
## mean in group Non mean in group Oui
## -27.77750 16.66667significativité: Oui
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.92919, p-value = 0.3717shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.76984, p-value = 0.03094la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_IV
## F = 1.4735, num df = 11, denom df = 5, p-value = 0.7024
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2243469 5.9587101
## sample estimates:
## ratio of variances
## 1.47347test de variance: acceptée
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_IV
## W = 49, p-value = 0.226
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIA == "Non"]
## W = 0.88134, p-value = 0.07436shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIA == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_IIIA
## F = 7.0784, num df = 12, denom df = 2, p-value = 0.2607
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1795888 36.0707050
## sample estimates:
## ratio of variances
## 7.078423test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_IIIA, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_IIIA
## W = 14.5, p-value = 0.5164
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIB == "Non"]
## W = 0.71765, p-value = 0.00228shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_IIIB == "Oui"]
## W = 0.92025, p-value = 0.4714la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_IIIB
## F = 0.3977, num df = 8, denom df = 6, p-value = 0.2274
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07102338 1.84999854
## sample estimates:
## ratio of variances
## 0.3977041test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_IIIB, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_IIIB
## W = 24.5, p-value = 0.4609
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_IV == "Non"]
## W = 0.89684, p-value = 0.2022shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_IV == "Oui"]
## W = 0.66578, p-value = 0.002641la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_IV
## F = 1.4849, num df = 9, denom df = 5, p-value = 0.6917
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2222515 6.6587862
## sample estimates:
## ratio of variances
## 1.484874test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_IV
## W = 42, p-value = 0.1813
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIA=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIA == "Non"]
## W = 0.90945, p-value = 0.114shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIA=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIA == "Oui"]
## W = 0.94466, p-value = 0.683la normalité: accepté
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_IIIA)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_IIIA
## F = 1.9436, num df = 15, denom df = 3, p-value = 0.643
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1363677 8.0714288
## sample estimates:
## ratio of variances
## 1.943609test de variance: accepté
t.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_IIIA, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_IIIA
## t = 1.1269, df = 18, p-value = 0.2746
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -19.80838 65.63963
## sample estimates:
## mean in group Non mean in group Oui
## -10.41688 -33.33250significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIB=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIB == "Non"]
## W = 0.90274, p-value = 0.1721shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIB=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_IIIB == "Oui"]
## W = 0.82602, p-value = 0.05397la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_IIIB)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_IIIB
## F = 1.9574, num df = 11, denom df = 7, p-value = 0.3825
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4156309 7.3571616
## sample estimates:
## ratio of variances
## 1.957401test de variance: accepté
t.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_IIIB,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_IIIB
## t = -0.70055, df = 17.848, p-value = 0.4926
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -44.45061 22.22978
## sample estimates:
## mean in group Non mean in group Oui
## -19.44417 -8.33375significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_IV == "Non"]
## W = 0.86692, p-value = 0.05974shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_IV == "Oui"]
## W = 0.93445, p-value = 0.5574la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_IV)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_IV
## F = 0.41276, num df = 11, denom df = 7, p-value = 0.1832
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08764438 1.55140999
## sample estimates:
## ratio of variances
## 0.4127586test de variance: acceptée
t.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_IV
## t = -0.22238, df = 10.859, p-value = 0.8281
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -45.47138 37.13805
## sample estimates:
## mean in group Non mean in group Oui
## -16.66667 -12.50000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.75846, p-value = 0.002681shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.8959, p-value = 0.3069la normalité: acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.6612, num df = 10, denom df = 6, p-value = 0.5365
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1210695 2.6924918
## sample estimates:
## ratio of variances
## 0.6611997test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_.dyspnee_II
## W = 44.5, p-value = 0.597
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.79888, p-value = 0.009099shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.66578, p-value = 0.002641la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_.dyspnee_III
## F = 0.99166, num df = 11, denom df = 5, p-value = 0.9143
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1509874 4.0102623
## sample estimates:
## ratio of variances
## 0.9916578test de variance: non accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_.dyspnee_III)##
## Welch Two Sample t-test
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_.dyspnee_III
## t = 1.1024, df = 10.063, p-value = 0.2959
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -22.65181 67.09348
## sample estimates:
## mean in group Non mean in group Oui
## -8.333333e-04 -2.222167e+01significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.83019, p-value = 0.01593shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.68403, p-value = 0.00647la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 5.3427, num df = 12, denom df = 4, p-value = 0.1189
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6105135 22.0183853
## sample estimates:
## ratio of variances
## 5.342701test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Stade_.dyspnee_IV)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 13, p-value = 0.04681
## alternative hypothesis: true location shift is not equal to 0significativité: Oui
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.81042, p-value = 0.03697shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.79428, p-value = 0.03594la normalité: non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_.dyspnee_II
## F = 0.31937, num df = 7, denom df = 6, p-value = 0.1611
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05607375 1.63470759
## sample estimates:
## ratio of variances
## 0.3193664test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_.dyspnee_II, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_.dyspnee_II
## W = 21, p-value = 0.3934
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.80615, p-value = 0.01101shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: acceptée -> wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_.dyspnee_III
## F = 2.796, num df = 11, denom df = 2, p-value = 0.5857
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.07095246 14.69561037
## sample estimates:
## ratio of variances
## 2.796027test de variance: non accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_.dyspnee_III)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_.dyspnee_III
## W = 24, p-value = 0.3677
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.76389, p-value = 0.005269shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.88349, p-value = 0.3254la normalité: acceptée -> wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_.dyspnee_IV
## F = 2.1338, num df = 9, denom df = 4, p-value = 0.4842
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2396283 10.0674991
## sample estimates:
## ratio of variances
## 2.133813test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Stade_.dyspnee_IV
## W = 26, p-value = 0.9446
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.82399, p-value = 0.01779shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.77385, p-value = 0.01493la normalité: accepté -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_.dyspnee_II
## F = 1.6352, num df = 11, denom df = 7, p-value = 0.5271
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.347219 6.146191
## sample estimates:
## ratio of variances
## 1.635218test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_.dyspnee_II
## W = 69, p-value = 0.09797
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.89752, p-value = 0.07336shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.83969, p-value = 0.1945la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_.dyspnee_III
## F = 0.3986, num df = 15, denom df = 3, p-value = 0.1966
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02796659 1.65530635
## sample estimates:
## ratio of variances
## 0.3985997test de variance: accepté
t.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_.dyspnee_III)##
## Welch Two Sample t-test
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_.dyspnee_III
## t = 0.068857, df = 3.6205, p-value = 0.9487
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -85.55294 89.72169
## sample estimates:
## mean in group Non mean in group Oui
## -14.58312 -16.66750significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.8441, p-value = 0.03107shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.85994, p-value = 0.1199la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 1.2926, num df = 11, denom df = 7, p-value = 0.7568
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2744756 4.8585451
## sample estimates:
## ratio of variances
## 1.292635test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 30, p-value = 0.1578
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.78484, p-value = 0.005943shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.80655, p-value = 0.04753la normalité:acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.48603, num df = 10, denom df = 6, p-value = 0.2995
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08899542 1.97918930
## sample estimates:
## ratio of variances
## 0.4860328test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_.dyspnee_II
## W = 19.5, p-value = 0.08314
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.85809, p-value = 0.04627shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.63989, p-value = 0.001351la normalité: acceptée -> Wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_.dyspnee_III
## F = 1.3847, num df = 11, denom df = 5, p-value = 0.7588
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2108359 5.5998548
## sample estimates:
## ratio of variances
## 1.384732test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_.dyspnee_III,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_.dyspnee_III
## W = 41, p-value = 0.6629
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.93848, p-value = 0.4376shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.88349, p-value = 0.3254la normalité: non acceptée -> Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 2.9484, num df = 12, denom df = 4, p-value = 0.3072
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3369174 12.1510455
## sample estimates:
## ratio of variances
## 2.948418test de variance: acceptée
t.test(BP_C$Del_toux_3_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Stade_.dyspnee_IV
## t = 1.8315, df = 12.75, p-value = 0.09048
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -5.130751 61.537828
## sample estimates:
## mean in group Non mean in group Oui
## -5.128462 -33.332000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.86653, p-value = 0.1128shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.93222, p-value = 0.5699la normalité: acceptée -> t.test
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_.dyspnee_II
## F = 0.7516, num df = 8, denom df = 6, p-value = 0.6895
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1342237 3.4962248
## sample estimates:
## ratio of variances
## 0.7516022test de variance: accepté
t.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_.dyspnee_II, var.equal=TRUE)##
## Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_.dyspnee_II
## t = 0.043212, df = 14, p-value = 0.9661
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -51.45933 53.57552
## sample estimates:
## mean in group Non mean in group Oui
## -3.703333 -4.761429significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.88134, p-value = 0.07436shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_.dyspnee_III
## F = 7.0784, num df = 12, denom df = 2, p-value = 0.2607
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1795888 36.0707050
## sample estimates:
## ratio of variances
## 7.078423test de variance: accepté
t.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_.dyspnee_III, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_.dyspnee_III
## t = -1.0428, df = 9.6009, p-value = 0.3226
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -59.20089 21.59782
## sample estimates:
## mean in group Non mean in group Oui
## -7.691538 11.110000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.87463, p-value = 0.1131shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.91642, p-value = 0.48la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_.dyspnee_IV
## F = 0.66664, num df = 9, denom df = 5, p-value = 0.562
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.09978091 2.98949544
## sample estimates:
## ratio of variances
## 0.6666417test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Stade_.dyspnee_IV
## W = 35, p-value = 0.6009
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_II=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_II == "Non"]
## W = 0.81079, p-value = 0.01246shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_II=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_II == "Oui"]
## W = 0.90559, p-value = 0.324la normalité: non acceptée
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_.dyspnee_II)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_.dyspnee_II
## F = 0.86665, num df = 11, denom df = 7, p-value = 0.7981
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1840237 3.2574381
## sample estimates:
## ratio of variances
## 0.8666539test de variance: accepté
wilcox.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_.dyspnee_II, var.equal=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_.dyspnee_II
## W = 14, p-value = 0.006848
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_III=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_III == "Non"]
## W = 0.91919, p-value = 0.1637shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_III=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_III == "Oui"]
## W = 0.86337, p-value = 0.2725la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_.dyspnee_III)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_.dyspnee_III
## F = 1.2362, num df = 15, denom df = 3, p-value = 0.9831
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.08673507 5.13373758
## sample estimates:
## ratio of variances
## 1.23621test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
t.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_.dyspnee_III, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_.dyspnee_III
## t = 1.8257, df = 5.0445, p-value = 0.127
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -13.47588 80.14463
## sample estimates:
## mean in group Non mean in group Oui
## -8.333125 -41.667500significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_IV=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_IV == "Non"]
## W = 0.92919, p-value = 0.3717shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_IV=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Stade_.dyspnee_IV == "Oui"]
## W = 0.83521, p-value = 0.06723la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_.dyspnee_IV)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_.dyspnee_IV
## F = 2.045, num df = 11, denom df = 7, p-value = 0.3519
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4342222 7.6862514
## sample estimates:
## ratio of variances
## 2.044957test de variance: acceptée
wilcox.test(BP_C$Del_toux_2_vs_1~BP_C$Stade_.dyspnee_IV,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Stade_.dyspnee_IV
## W = 65.5, p-value = 0.17
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.7125, p-value = 0.001115shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.70126, p-value = 0.006373la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Hist_ADK
## F = 0.34633, num df = 11, denom df = 5, p-value = 0.1326
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05273102 1.40054888
## sample estimates:
## ratio of variances
## 0.3463278test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Hist_ADK,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Hist_ADK
## W = 23.5, p-value = 0.2329
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.74827, p-value = 0.0034shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.56594, p-value = 6.323e-05la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Hist_Epiderm
## F = 12.239, num df = 9, denom df = 7, p-value = 0.003291
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.537472 51.366728
## sample estimates:
## ratio of variances
## 12.23878test de variance: rejetée
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Hist_Epiderm,var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Hist_Epiderm
## W = 51, p-value = 0.322
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_ADK == "Non"]
## W = 0.76018, p-value = 0.007112shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_ADK == "Oui"]
## W = 0.70125, p-value = 0.006372la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Hist_ADK
## F = 1.2301, num df = 8, denom df = 5, p-value = 0.8558
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.182039 5.925579
## sample estimates:
## ratio of variances
## 1.230069test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Hist_ADK, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Hist_ADK
## W = 15.5, p-value = 0.1413
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_Epiderm == "Non"]
## W = 0.62757, p-value = 0.0001181shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Hist_Epiderm == "Oui"]
## W = 0.68403, p-value = 0.00647la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Hist_Epiderm
## F = 2.4081, num df = 9, denom df = 4, p-value = 0.4123
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2704279 11.3614830
## sample estimates:
## ratio of variances
## 2.408074test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Hist_Epiderm
## W = 43, p-value = 0.01502
## alternative hypothesis: true location shift is not equal to 0significativité: Oui
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.82897, p-value = 0.03252shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.92973, p-value = 0.4452la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Hist_ADK
## F = 0.84026, num df = 9, denom df = 9, p-value = 0.7997
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.208708 3.382871
## sample estimates:
## ratio of variances
## 0.8402572test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Hist_ADK)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Hist_ADK
## W = 58, p-value = 0.5531
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.89504, p-value = 0.09557shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.86626, p-value = 0.2117la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Hist_Epiderm
## F = 2.8318, num df = 13, denom df = 5, p-value = 0.2576
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.43649 10.66633
## sample estimates:
## ratio of variances
## 2.831763test de variance: acceptée
t.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Hist_Epiderm
## t = -1.9268, df = 15.64, p-value = 0.07237
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -61.731328 3.002757
## sample estimates:
## mean in group Non mean in group Oui
## -23.80929 5.55500significativité: Non mais à la limite de la significativité
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.93977, p-value = 0.4951shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.91548, p-value = 0.4734la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Hist_ADK
## F = 1.385, num df = 11, denom df = 5, p-value = 0.7586
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2108812 5.6010567
## sample estimates:
## ratio of variances
## 1.385029test de variance: accepté
t.test(BP_C$Del_toux_3_vs_1~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Hist_ADK
## t = 0.75959, df = 11.76, p-value = 0.4625
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -26.03975 53.81642
## sample estimates:
## mean in group Non mean in group Oui
## -8.333333 -22.221667significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.82581, p-value = 0.02979shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.87745, p-value = 0.178la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Hist_Epiderm
## F = 0.68104, num df = 9, denom df = 7, p-value = 0.5788
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1412001 2.8583520
## sample estimates:
## ratio of variances
## 0.6810389test de variance: acceptée
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Hist_Epiderm
## W = 39.5, p-value = 1
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Hist_ADK == "Non"]
## W = 0.80675, p-value = 0.02438shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Hist_ADK == "Oui"]
## W = 0.92394, p-value = 0.5006la normalité:non acceptée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Hist_ADK
## F = 0.81422, num df = 8, denom df = 6, p-value = 0.766
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1454066 3.7875131
## sample estimates:
## ratio of variances
## 0.8142221test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Hist_ADK, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Hist_ADK
## W = 41.5, p-value = 0.2812
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Hist_Epiderm == "Non"]
## W = 0.88918, p-value = 0.1358shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Hist_Epiderm == "Oui"]
## W = 0.7709, p-value = 0.04595la normalité: non acceptée -> t de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Hist_Epiderm
## F = 2.8407, num df = 10, denom df = 4, p-value = 0.3262
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3212084 12.6933487
## sample estimates:
## ratio of variances
## 2.840729test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Hist_Epiderm,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Hist_Epiderm
## W = 15.5, p-value = 0.1626
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Hist_ADK=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Hist_ADK == "Non"]
## W = 0.93308, p-value = 0.4789shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Hist_ADK=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Hist_ADK == "Oui"]
## W = 0.87374, p-value = 0.1105la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Hist_ADK)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Hist_ADK
## F = 1.1485, num df = 9, denom df = 9, p-value = 0.84
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2852678 4.6237995
## sample estimates:
## ratio of variances
## 1.148486test de variance: accepté
t.test(BP_C$Del_toux_2_vs_1~BP_C$Hist_ADK,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Hist_ADK
## t = 1.0183, df = 17.914, p-value = 0.3221
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -17.73133 51.06733
## sample estimates:
## mean in group Non mean in group Oui
## -6.666 -23.334significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Hist_Epiderm=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Hist_Epiderm == "Non"]
## W = 0.92249, p-value = 0.2388shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Hist_Epiderm=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Hist_Epiderm == "Oui"]
## W = 0.82161, p-value = 0.09112la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Hist_Epiderm)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Hist_Epiderm
## F = 2.217, num df = 13, denom df = 5, p-value = 0.3895
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3417307 8.3507362
## sample estimates:
## ratio of variances
## 2.217005test de variance: acceptée
t.test(BP_C$Del_toux_2_vs_1~BP_C$Hist_Epiderm,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Hist_Epiderm
## t = 0.66487, df = 14.114, p-value = 0.5168
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -22.93806 43.57139
## sample estimates:
## mean in group Non mean in group Oui
## -11.90500 -22.22167significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.85111, p-value = 0.1258shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.72426, p-value = 0.0009618la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$lignes_ttt_jamais
## F = 1.9233, num df = 6, denom df = 10, p-value = 0.3444
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4723121 10.5038560
## sample estimates:
## ratio of variances
## 1.923317test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$lignes_ttt_jamais)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$lignes_ttt_jamais
## W = 39, p-value = 1
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.77913, p-value = 0.005478shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.8087, p-value = 0.07028la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$lignes_ttt_une
## F = 0.43389, num df = 11, denom df = 5, p-value = 0.2309
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06606231 1.75463124
## sample estimates:
## ratio of variances
## 0.4338853test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$lignes_ttt_une
## W = 30, p-value = 0.5845
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.79428, p-value = 0.03594shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.81042, p-value = 0.03697la normalité: non acceptée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$lignes_ttt_jamais
## F = 3.1312, num df = 6, denom df = 7, p-value = 0.1611
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6117302 17.8336579
## sample estimates:
## ratio of variances
## 3.1312test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$lignes_ttt_jamais,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$lignes_ttt_jamais
## W = 35, p-value = 0.3934
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_une == "Non"]
## W = 0.81259, p-value = 0.02841shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$lignes_ttt_une == "Oui"]
## W = 0.63989, p-value = 0.001351la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$lignes_ttt_une
## F = 0.41654, num df = 8, denom df = 5, p-value = 0.2598
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06164438 2.00659606
## sample estimates:
## ratio of variances
## 0.4165417test de variance: non accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$lignes_ttt_une,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$lignes_ttt_une
## W = 14, p-value = 0.09463
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.94039, p-value = 0.5861shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.82548, p-value = 0.02036la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$lignes_ttt_jamais
## F = 1.0593, num df = 8, denom df = 10, p-value = 0.9133
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2747983 4.5499026
## sample estimates:
## ratio of variances
## 1.059317test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$lignes_ttt_jamais,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$lignes_ttt_jamais
## W = 33.5, p-value = 0.2179
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.7906, p-value = 0.005283shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.96005, p-value = 0.8192la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$lignes_ttt_une
## F = 0.68458, num df = 12, denom df = 6, p-value = 0.5419
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1275711 2.5523050
## sample estimates:
## ratio of variances
## 0.6845775test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$lignes_ttt_une
## W = 65.5, p-value = 0.1059
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.76046, p-value = 0.0163shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.85808, p-value = 0.05438la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$lignes_ttt_jamais
## F = 1.5803, num df = 6, denom df = 10, p-value = 0.4979
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3880723 8.6304292
## sample estimates:
## ratio of variances
## 1.580282test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$lignes_ttt_jamais,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$lignes_ttt_jamais
## W = 48, p-value = 0.3992
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.90273, p-value = 0.172shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.63989, p-value = 0.001351la normalité: non acceptée -> test de Wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$lignes_ttt_une
## F = 1.4421, num df = 11, denom df = 5, p-value = 0.7217
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2195749 5.8319627
## sample estimates:
## ratio of variances
## 1.442128test de variance: acceptée
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$lignes_ttt_une
## W = 35, p-value = 0.9614
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.89951, p-value = 0.2861shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.89951, p-value = 0.2861la normalité: acceptée -> t. test
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$lignes_ttt_jamais
## F = 1, num df = 7, denom df = 7, p-value = 1
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2002038 4.9949092
## sample estimates:
## ratio of variances
## 1test de variance: accepté
t.test(BP_C$Del_toux_3_vs_2~BP_C$lignes_ttt_jamais, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$lignes_ttt_jamais
## t = 0, df = 14, p-value = 1
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -52.10899 52.10899
## sample estimates:
## mean in group Non mean in group Oui
## -4.16625 -4.16625significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_une == "Non"]
## W = 0.88495, p-value = 0.1487shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$lignes_ttt_une == "Oui"]
## W = 0.82682, p-value = 0.101la normalité: acceptée -> t.test ( test de student)
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$lignes_ttt_une
## F = 7.6683, num df = 9, denom df = 5, p-value = 0.0372
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.147773 34.387962
## sample estimates:
## ratio of variances
## 7.668334test de variance: non accepté
t.test(BP_C$Del_toux_3_vs_2~BP_C$lignes_ttt_une,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$lignes_ttt_une
## t = -0.3273, df = 12.292, p-value = 0.7489
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -50.92472 37.59272
## sample estimates:
## mean in group Non mean in group Oui
## -6.666 0.000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_jamais=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_jamais == "Non"]
## W = 0.81259, p-value = 0.02841shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_jamais=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_jamais == "Oui"]
## W = 0.88139, p-value = 0.1083la normalité: acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$lignes_ttt_jamais)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$lignes_ttt_jamais
## F = 0.23959, num df = 8, denom df = 10, p-value = 0.05431
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.06215187 1.02906384
## sample estimates:
## ratio of variances
## 0.2395887test de variance: acceptée
wilcox.test(BP_C$Del_toux_2_vs_1~BP_C$lignes_ttt_jamais)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$lignes_ttt_jamais
## W = 62, p-value = 0.3402
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_une=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_une == "Non"]
## W = 0.88645, p-value = 0.08725shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_une=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$lignes_ttt_une == "Oui"]
## W = 0.83338, p-value = 0.08614la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$lignes_ttt_une)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$lignes_ttt_une
## F = 2.8052, num df = 12, denom df = 6, p-value = 0.2147
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5227532 10.4586832
## sample estimates:
## ratio of variances
## 2.805221test de variance: acceptée
t.test(BP_C$Del_toux_2_vs_1~BP_C$lignes_ttt_une,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$lignes_ttt_une
## t = -0.55846, df = 17.664, p-value = 0.5835
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -40.16950 23.31675
## sample estimates:
## mean in group Non mean in group Oui
## -17.949231 -9.522857significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.81854, p-value = 0.006423shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$typ_stent_Métallique
## F = 4.6292, num df = 14, denom df = 2, p-value = 0.3833
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1174144 22.4828205
## sample estimates:
## ratio of variances
## 4.62924test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$typ_stent_Métallique,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$typ_stent_Métallique
## W = 9.5, p-value = 0.116
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.7186, p-value = 0.005897shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.85356, p-value = 0.0475la normalité: non acceptée -> test de wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$typ_stent_Silicone
## F = 1.5942, num df = 6, denom df = 10, p-value = 0.4904
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3915004 8.7066663
## sample estimates:
## ratio of variances
## 1.594241test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$typ_stent_Silicone,var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$typ_stent_Silicone
## W = 50, p-value = 0.2903
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Métallique == "Non"]
## W = 0.8077, p-value = 0.01148shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: non acceptée -> Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$typ_stent_Métallique
## F = 0.54537, num df = 11, denom df = 2, p-value = 0.4109
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.01383947 2.86641874
## sample estimates:
## ratio of variances
## 0.5453727test de variance: non accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$typ_stent_Métallique,var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$typ_stent_Métallique
## W = 11.5, p-value = 0.3258
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Silicone == "Non"]
## W = 0.49609, p-value = 2.073e-05shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.85328, p-value = 0.08096la normalité: rejetée -> wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$typ_stent_Silicone
## F = 0.66677, num df = 5, denom df = 8, p-value = 0.6806
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1384116 4.5054571
## sample estimates:
## ratio of variances
## 0.6667667test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$typ_stent_Silicone
## W = 33.5, p-value = 0.4224
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.88139, p-value = 0.04079shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.94466, p-value = 0.683la normalité: non acceptée -> test de wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$typ_stent_Métallique
## F = 2.3941, num df = 15, denom df = 3, p-value = 0.5142
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1679771 9.9423478
## sample estimates:
## ratio of variances
## 2.394129test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$typ_stent_Métallique)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$typ_stent_Métallique
## W = 24.5, p-value = 0.4889
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.81041, p-value = 0.03695shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.91957, p-value = 0.2823la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$typ_stent_Silicone
## F = 1.2115, num df = 7, denom df = 11, p-value = 0.7445
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3223187 5.7054259
## sample estimates:
## ratio of variances
## 1.211479test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$typ_stent_Silicone
## W = 58.5, p-value = 0.4195
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.92351, p-value = 0.2179shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.75, p-value < 2.2e-16la normalité: non acceptée; wilcox Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$typ_stent_Métallique
## F = 1.0502, num df = 14, denom df = 2, p-value = 0.8191
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.0266377 5.1006569
## sample estimates:
## ratio of variances
## 1.050231test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_toux_3_vs_1~BP_C$typ_stent_Métallique)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$typ_stent_Métallique
## W = 22, p-value = 1
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.86933, p-value = 0.1831shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.93809, p-value = 0.4983la normalité: acceptée -> test de STUDENT
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$typ_stent_Silicone
## F = 0.57858, num df = 6, denom df = 10, p-value = 0.5194
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1420828 3.1598118
## sample estimates:
## ratio of variances
## 0.5785798test de variance: acceptée
t.test(BP_C$Del_toux_3_vs_1~BP_C$typ_stent_Silicone,var.test=FALSE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$typ_stent_Silicone
## t = -1.022, df = 15.33, p-value = 0.3226
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -54.69154 19.19621
## sample estimates:
## mean in group Non mean in group Oui
## -23.808571 -6.060909significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Métallique == "Non"]
## W = 0.80615, p-value = 0.01101shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.82744, p-value = 0.1612la normalité: non acceptée -> wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$typ_stent_Métallique
## F = 0.21926, num df = 11, denom df = 3, p-value = 0.05223
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.01525375 1.01517995
## sample estimates:
## ratio of variances
## 0.2192601test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$typ_stent_Métallique)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$typ_stent_Métallique
## W = 33, p-value = 0.2693
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Silicone == "Non"]
## W = 0.84471, p-value = 0.1099shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.83021, p-value = 0.0449la normalité:non acceptée -> wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$typ_stent_Silicone
## F = 2.4091, num df = 6, denom df = 8, p-value = 0.2487
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.517905 13.490256
## sample estimates:
## ratio of variances
## 2.409136test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$typ_stent_Silicone,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$typ_stent_Silicone
## W = 17.5, p-value = 0.1256
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Métallique=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Métallique == "Non"]
## W = 0.87879, p-value = 0.03715shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Métallique=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Métallique == "Oui"]
## W = 0.89493, p-value = 0.4063la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$typ_stent_Métallique)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$typ_stent_Métallique
## F = 0.66315, num df = 15, denom df = 3, p-value = 0.5061
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.04652801 2.75393304
## sample estimates:
## ratio of variances
## 0.6631503test de variance: accepté
wilcox.test(BP_C$Del_toux_2_vs_1~BP_C$typ_stent_Métallique,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$typ_stent_Métallique
## W = 20, p-value = 0.2556
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Silicone=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Silicone == "Non"]
## W = 0.89897, p-value = 0.2828shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Silicone=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$typ_stent_Silicone == "Oui"]
## W = 0.89504, p-value = 0.1369la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$typ_stent_Silicone)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$typ_stent_Silicone
## F = 1.7275, num df = 7, denom df = 11, p-value = 0.4011
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4596203 8.1358270
## sample estimates:
## ratio of variances
## 1.727546test de variance: acceptée
t.test(BP_C$Del_toux_2_vs_1~BP_C$typ_stent_Silicone,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$typ_stent_Silicone
## t = -0.15287, df = 12.281, p-value = 0.881
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -42.25700 36.70284
## sample estimates:
## mean in group Non mean in group Oui
## -16.66625 -13.88917significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.7805, p-value = 0.02613shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.66468, p-value = 0.0001654la normalité: non acceptée -> test de wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.83814, num df = 6, denom df = 10, p-value = 0.8646
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2058222 4.5773268
## sample estimates:
## ratio of variances
## 0.8381351test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## W = 14, p-value = 0.02104
## alternative hypothesis: true location shift is not equal to 0significativité: Oui
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.55218, p-value = 0.000131shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.84557, p-value = 0.05145la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## F = 0.17818, num df = 4, denom df = 9, p-value = 0.112
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.03776576 1.58665243
## sample estimates:
## ratio of variances
## 0.1781818test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Site_Trachée_ou.Carène, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## W = 19, p-value = 0.4447
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.76733, p-value = 0.008605shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.84814, p-value = 0.04035la normalité: non acceptée -> test de wilcoxon sera realisé
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 0.76397, num df = 8, denom df = 10, p-value = 0.7169
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1981824 3.2813546
## sample estimates:
## ratio of variances
## 0.7639715test de variance: accepté
Pour un probleme d’ex-aequos j’ai du utiliser le t-test qui est normalement pas loin du test de wilcoxon
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## W = 34, p-value = 0.2331
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.88772, p-value = 0.263shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.88903, p-value = 0.1352la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 1.2907, num df = 6, denom df = 10, p-value = 0.6867
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.316950 7.048723
## sample estimates:
## ratio of variances
## 1.290662test de variance: rejetée
t.test(BP_C$Del_toux_3_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = -0.11039, df = 11.673, p-value = 0.914
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -44.97587 40.65093
## sample estimates:
## mean in group Non mean in group Oui
## -14.28429 -12.12182significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.91237, p-value = 0.4522shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.84061, p-value = 0.04487la normalité: non acceptée -> Wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## F = 5.5577, num df = 5, denom df = 9, p-value = 0.02625
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.239341 37.131407
## sample estimates:
## ratio of variances
## 5.557717test de variance: rejetée
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Site_Trachée_ou.Carène, var.test=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Site_Trachée_ou.Carène
## W = 18, p-value = 0.1813
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Non"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Non"]
## W = 0.85407, p-value = 0.08261shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Site_Trachée_ou.Carène=="Oui"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Site_Trachée_ou.Carène == "Oui"]
## W = 0.90376, p-value = 0.2053la normalité: non acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Site_Trachée_ou.Carène)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## F = 1.3985, num df = 8, denom df = 10, p-value = 0.6077
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3627791 6.0066223
## sample estimates:
## ratio of variances
## 1.398474test de variance: accepté
t.test(BP_C$Del_toux_2_vs_1~BP_C$Site_Trachée_ou.Carène,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Site_Trachée_ou.Carène
## t = 0.41269, df = 15.779, p-value = 0.6854
## alternative hypothesis: true difference in means between group Non and group Oui is not equal to 0
## 95 percent confidence interval:
## -29.29193 43.43334
## sample estimates:
## mean in group Non mean in group Oui
## -11.11111 -18.18182significativité: Non # Type d’intervention ## Type d’intervention / hémoptysie V3-V1 test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.72863, p-value = 0.02386shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.83252, p-value = 0.01297la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Type_intervention
## F = 4.9458, num df = 13, denom df = 3, p-value = 0.214
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3457495 21.5001305
## sample estimates:
## ratio of variances
## 4.945767test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Type_intervention,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Type_intervention
## W = 15, p-value = 0.1588
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Type_intervention == "urgente"]
## W = 1, p-value = 1shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Type_intervention == "programmée"]
## W = 0.72099, p-value = 0.001353la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Type_intervention
## F = 0.87899, num df = 11, denom df = 2, p-value = 0.7111
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02230535 4.61986317
## sample estimates:
## ratio of variances
## 0.8789879test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Type_intervention, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Type_intervention
## W = 19, p-value = 0.9347
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.88104, p-value = 0.314shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.88147, p-value = 0.04991la normalité: non acceptée -> test de W
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Type_intervention
## F = 2.1772, num df = 14, denom df = 4, p-value = 0.4712
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2507249 8.4736246
## sample estimates:
## ratio of variances
## 2.177238test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Type_intervention,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Type_intervention
## W = 22, p-value = 0.1707
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.86336, p-value = 0.2724shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.92171, p-value = 0.2327la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Type_intervention
## F = 1.5823, num df = 13, denom df = 3, p-value = 0.7853
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1106139 6.8784257
## sample estimates:
## ratio of variances
## 1.582274test de variance: acceptée
t.test(BP_C$Del_toux_3_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Type_intervention
## t = 0.80483, df = 6.0407, p-value = 0.4514
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -31.49944 62.45230
## sample estimates:
## mean in group programmée mean in group urgente
## -9.523571 -25.000000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Type_intervention == "urgente"]
## W = 0.92615, p-value = 0.5719shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Type_intervention == "programmée"]
## W = 0.88597, p-value = 0.1046la normalité: oui -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Type_intervention
## F = 0.36183, num df = 11, denom df = 3, p-value = 0.1844
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.02517242 1.67529519
## sample estimates:
## ratio of variances
## 0.3618329test de variance: accepté
t.test(BP_C$Del_toux_3_vs_2~BP_C$Type_intervention, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Type_intervention
## t = 0.15272, df = 3.7524, p-value = 0.8865
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -98.11528 109.22528
## sample estimates:
## mean in group programmée mean in group urgente
## -2.7775 -8.3325significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Type_intervention=="urgente"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Type_intervention == "urgente"]
## W = 0.82083, p-value = 0.1185shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Type_intervention=="programmée"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Type_intervention == "programmée"]
## W = 0.92706, p-value = 0.2465la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Type_intervention)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Type_intervention
## F = 1.2094, num df = 14, denom df = 4, p-value = 0.9405
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.139272 4.706907
## sample estimates:
## ratio of variances
## 1.209407test de variance: accepté
t.test(BP_C$Del_toux_2_vs_1~BP_C$Type_intervention,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Type_intervention
## t = 1.3843, df = 7.5257, p-value = 0.2059
## alternative hypothesis: true difference in means between group programmée and group urgente is not equal to 0
## 95 percent confidence interval:
## -16.72738 65.61805
## sample estimates:
## mean in group programmée mean in group urgente
## -8.888667 -33.334000significativité:Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.87161, p-value = 0.1563shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.80218, p-value = 0.01541la normalité: non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Atelectasie_RT
## F = 4.6654, num df = 7, denom df = 9, p-value = 0.03619
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.111599 22.502401
## sample estimates:
## ratio of variances
## 4.665434test de variance: rejeté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Atelectasie_RT)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Atelectasie_RT
## W = 19.5, p-value = 0.05925
## alternative hypothesis: true location shift is not equal to 0significativité: Non, mais à la limite de la significativité
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Atelectasie_RT == "Absent"]
## W = 0.78649, p-value = 0.03003shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Atelectasie_RT == "Présent"]
## W = 0.82602, p-value = 0.05397la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Atelectasie_RT
## F = 1.2727, num df = 6, denom df = 7, p-value = 0.7519
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.248650 7.248846
## sample estimates:
## ratio of variances
## 1.272739test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Atelectasie_RT, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Atelectasie_RT
## W = 22.5, p-value = 0.5115
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.88652, p-value = 0.1837shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.8643, p-value = 0.06546la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Atelectasie_RT
## F = 2.8737, num df = 8, denom df = 10, p-value = 0.1209
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7454694 12.3429202
## sample estimates:
## ratio of variances
## 2.873703test de variance: acceptée
t.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Atelectasie_RT
## t = -1.0648, df = 12.4, p-value = 0.3073
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -60.36713 20.63784
## sample estimates:
## mean in group Absent mean in group Présent
## -25.925556 -6.060909significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.89742, p-value = 0.2738shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.91646, p-value = 0.3284la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Atelectasie_RT
## F = 1.0626, num df = 7, denom df = 9, p-value = 0.9102
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2531671 5.1249300
## sample estimates:
## ratio of variances
## 1.062554test de variance: acceptée
t.test(BP_C$Del_toux_3_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Atelectasie_RT
## t = -0.35758, df = 14.927, p-value = 0.7257
## alternative hypothesis: true difference in means between group Absent and group Présent is not equal to 0
## 95 percent confidence interval:
## -46.43511 33.09811
## sample estimates:
## mean in group Absent mean in group Présent
## -16.6675 -9.9990significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Atelectasie_RT == "Absent"]
## W = 0.75833, p-value = 0.01009shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Atelectasie_RT == "Présent"]
## W = 0.91215, p-value = 0.3695la normalité: non acceptée -> test de Wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Atelectasie_RT
## F = 0.52315, num df = 7, denom df = 7, p-value = 0.412
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1047362 2.6130763
## sample estimates:
## ratio of variances
## 0.5231479test de variance: accepté
wilcox.test(BP_C$Del_toux_3_vs_2~BP_C$Atelectasie_RT, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Atelectasie_RT
## W = 25, p-value = 0.4644
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Atelectasie_RT=="Absent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Atelectasie_RT == "Absent"]
## W = 0.8994, p-value = 0.2486shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Atelectasie_RT=="Présent"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Atelectasie_RT == "Présent"]
## W = 0.85358, p-value = 0.04752la normalité: non acceptée -> test de wilcox
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Atelectasie_RT)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Atelectasie_RT
## F = 1.0184, num df = 8, denom df = 10, p-value = 0.9588
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2641882 4.3742289
## sample estimates:
## ratio of variances
## 1.018417test de variance: accepté
wilcox.test(BP_C$Del_toux_2_vs_1~BP_C$Atelectasie_RT,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Atelectasie_RT
## W = 45, p-value = 0.7505
## alternative hypothesis: true location shift is not equal to 0significativité:Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Sexe == "Homme"]
## W = 0.82221, p-value = 0.01844shapiro.test(BP_C$Del_hemoptysie_3_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1[BP_C$Sexe == "Femme"]
## W = 0.75015, p-value = 0.01273la normalité: non acceptée -> test de W
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Sexe
## F = 6.6942, num df = 6, denom df = 10, p-value = 0.009203
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.643898 36.559026
## sample estimates:
## ratio of variances
## 6.694169test de variance: non accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_1~BP_C$Sexe,vartest=FALSE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_1 by BP_C$Sexe
## W = 42, p-value = 0.773
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Sexe == "Homme"]
## W = 0.76018, p-value = 0.007112shapiro.test(BP_C$Del_hemoptysie_3_vs_2[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_2[BP_C$Sexe == "Femme"]
## W = 0.70125, p-value = 0.006372la normalité:non acceptée -> test de wilcoxon
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Sexe
## F = 0.81296, num df = 5, denom df = 8, p-value = 0.8558
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1687599 5.4933294
## sample estimates:
## ratio of variances
## 0.8129628test de variance: accepté
wilcox.test(BP_C$Del_hemoptysie_3_vs_2~BP_C$Sexe, var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_3_vs_2 by BP_C$Sexe
## W = 38.5, p-value = 0.1413
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Sexe == "Homme"]
## W = 0.89441, p-value = 0.1343shapiro.test(BP_C$Del_hemoptysie_2_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1[BP_C$Sexe == "Femme"]
## W = 0.81042, p-value = 0.03697la normalité: acceptée -> test de W
Evaluation de l’égalité des variances
var.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Sexe
## F = 2.3765, num df = 7, denom df = 11, p-value = 0.1926
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6322708 11.1919474
## sample estimates:
## ratio of variances
## 2.376477test de variance: acceptée
wilcox.test(BP_C$Del_hemoptysie_2_vs_1~BP_C$Sexe,var.test=TRUE)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): impossible
## de calculer la p-value exacte avec des ex-aequos##
## Wilcoxon rank sum test with continuity correction
##
## data: BP_C$Del_hemoptysie_2_vs_1 by BP_C$Sexe
## W = 41.5, p-value = 0.6282
## alternative hypothesis: true location shift is not equal to 0significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Sexe == "Homme"]
## W = 0.94809, p-value = 0.6196shapiro.test(BP_C$Del_toux_3_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1[BP_C$Sexe == "Femme"]
## W = 0.8936, p-value = 0.294la normalité: acceptée -> test de student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Sexe
## F = 0.88532, num df = 6, denom df = 10, p-value = 0.9213
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2174106 4.8350428
## sample estimates:
## ratio of variances
## 0.8853243test de variance: acceptée
t.test(BP_C$Del_toux_3_vs_1~BP_C$Sexe,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_1 by BP_C$Sexe
## t = -1.4465, df = 13.531, p-value = 0.1708
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -63.53725 12.45439
## sample estimates:
## mean in group Femme mean in group Homme
## -28.57143 -3.03000significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Sexe == "Homme"]
## W = 0.8463, p-value = 0.06783shapiro.test(BP_C$Del_toux_3_vs_2[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_2[BP_C$Sexe == "Femme"]
## W = 0.86249, p-value = 0.1593la normalité: rejetée -> test de S
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_3_vs_2~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Sexe
## F = 4.203, num df = 6, denom df = 8, p-value = 0.06594
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.9035314 23.5349512
## sample estimates:
## ratio of variances
## 4.202953test de variance: accepté
t.test(BP_C$Del_toux_3_vs_2~BP_C$Sexe, var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_3_vs_2 by BP_C$Sexe
## t = -0.6895, df = 8.2151, p-value = 0.5095
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -77.88398 41.90366
## sample estimates:
## mean in group Femme mean in group Homme
## -14.285714 3.704444significativité: Non
test de Shapiro fait pour la normalité des données
shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Sexe=="Homme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Sexe == "Homme"]
## W = 0.88419, p-value = 0.0992shapiro.test(BP_C$Del_toux_2_vs_1[BP_C$Sexe=="Femme"])##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1[BP_C$Sexe == "Femme"]
## W = 0.85785, p-value = 0.1143la normalité: acceptée -> test de Student
Evaluation de l’égalité des variances
var.test(BP_C$Del_toux_2_vs_1~BP_C$Sexe)##
## F test to compare two variances
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Sexe
## F = 2.5718, num df = 7, denom df = 11, p-value = 0.1564
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.684249 12.112023
## sample estimates:
## ratio of variances
## 2.571844test de variance: accepté
t.test(BP_C$Del_toux_2_vs_1~BP_C$Sexe,var.test=TRUE)##
## Welch Two Sample t-test
##
## data: BP_C$Del_toux_2_vs_1 by BP_C$Sexe
## t = -0.90746, df = 10.644, p-value = 0.3842
## alternative hypothesis: true difference in means between group Femme and group Homme is not equal to 0
## 95 percent confidence interval:
## -57.25603 23.92270
## sample estimates:
## mean in group Femme mean in group Homme
## -25.000000 -8.333333significativité:Non
library(car)## Le chargement a nécessité le package : carDatacondition de validité
shapiro.test(BP_C$Del_toux_3_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_3_vs_1
## W = 0.9207, p-value = 0.133t.test(BP_C$symp_Toux_1_.,BP_C$symp_Toux_3_.,paired=TRUE)##
## Paired t-test
##
## data: BP_C$symp_Toux_1_. and BP_C$symp_Toux_3_.
## t = 1.4414, df = 17, p-value = 0.1676
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -6.011701 31.940590
## sample estimates:
## mean difference
## 12.96444les scores symptome toux entre la V1 et la V3 ne sont pas significativement different
condition de validité
shapiro.test(BP_C$Del_toux_2_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_toux_2_vs_1
## W = 0.90588, p-value = 0.05323t.test(BP_C$symp_Toux_1_.,BP_C$symp_Toux_2_.,paired=TRUE)##
## Paired t-test
##
## data: BP_C$symp_Toux_1_. and BP_C$symp_Toux_2_.
## t = 1.8312, df = 19, p-value = 0.0828
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -2.145064 32.147064
## sample estimates:
## mean difference
## 15.001La p-value du test est <0.05., ainsi les scores symptome toux entre la V1 et la V2 sont significativement different dans le sens d’une diminution
condition de validité
shapiro.test(BP_C$Del_dyspnée_LC13_3_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_3_vs_1
## W = 0.94065, p-value = 0.2973t.test(BP_C$symp_LC13_dyspnée_1_.,BP_C$symp_LC13_dyspnée_3_.,paired=TRUE)##
## Paired t-test
##
## data: BP_C$symp_LC13_dyspnée_1_. and BP_C$symp_LC13_dyspnée_3_.
## t = 2.5966, df = 17, p-value = 0.01881
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 4.571359 44.197529
## sample estimates:
## mean difference
## 24.38444La p-value du test est <0.05., ainsi les scores symptome dyspnée entre la V1 et la V3 sont significativement differents dans le sens d’une diminution (diminution moyenne de 24.38)
condition de validité
shapiro.test(BP_C$Del_dyspnée_QLQ30_3_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_3_vs_1
## W = 0.88517, p-value = 0.03192wilcox.test(BP_C$symp_QLC30_dyspnée_1_.,BP_C$symp_QLC30_dyspnée_3_.,paired=TRUE)## Warning in wilcox.test.default(BP_C$symp_QLC30_dyspnée_1_.,
## BP_C$symp_QLC30_dyspnée_3_., : impossible de calculer la p-value exacte avec des
## ex-aequos## Warning in wilcox.test.default(BP_C$symp_QLC30_dyspnée_1_.,
## BP_C$symp_QLC30_dyspnée_3_., : impossible de calculer une p-value exacte avec
## des zéros##
## Wilcoxon signed rank test with continuity correction
##
## data: BP_C$symp_QLC30_dyspnée_1_. and BP_C$symp_QLC30_dyspnée_3_.
## V = 67.5, p-value = 0.02662
## alternative hypothesis: true location shift is not equal to 0La p-value du test est <0.05., les scores symptome dyspnée entre la V1 et la V3 sont significativement differents
condition de validité
shapiro.test(BP_C$Del_dyspnée_LC13_2_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_LC13_2_vs_1
## W = 0.93543, p-value = 0.1963t.test(BP_C$symp_LC13_dyspnée_1_.,BP_C$symp_LC13_dyspnée_2_.,paired=TRUE)##
## Paired t-test
##
## data: BP_C$symp_LC13_dyspnée_1_. and BP_C$symp_LC13_dyspnée_2_.
## t = 2.6262, df = 18, p-value = 0.01713
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 4.561641 41.054148
## sample estimates:
## mean difference
## 22.80789La p-value du test est <0.05., ainsi les scores symptome dyspnée entre la V1 et la V2 sont significativement differents dans le sens d’une diminution (diminution moyenne de 22.80)
condition de validité
shapiro.test(BP_C$Del_dyspnée_QLQ30_2_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_dyspnée_QLQ30_2_vs_1
## W = 0.89925, p-value = 0.03993wilcox.test(BP_C$symp_QLC30_dyspnée_1_.,BP_C$symp_QLC30_dyspnée_2_.,paired=TRUE)## Warning in wilcox.test.default(BP_C$symp_QLC30_dyspnée_1_.,
## BP_C$symp_QLC30_dyspnée_2_., : impossible de calculer la p-value exacte avec des
## ex-aequos## Warning in wilcox.test.default(BP_C$symp_QLC30_dyspnée_1_.,
## BP_C$symp_QLC30_dyspnée_2_., : impossible de calculer une p-value exacte avec
## des zéros##
## Wilcoxon signed rank test with continuity correction
##
## data: BP_C$symp_QLC30_dyspnée_1_. and BP_C$symp_QLC30_dyspnée_2_.
## V = 84, p-value = 0.007173
## alternative hypothesis: true location shift is not equal to 0La p-value du test est <0.05., les scores symptome dyspnée entre la V1 et la V3 sont significativement differents
condition de validité
shapiro.test(BP_C$Del_hemoptysie_3_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_3_vs_1
## W = 0.80257, p-value = 0.001651wilcox.test(BP_C$symp_Hémoptysie_1_.,BP_C$symp_Hémoptysie_3_.,paired=TRUE)## Warning in wilcox.test.default(BP_C$symp_Hémoptysie_1_.,
## BP_C$symp_Hémoptysie_3_., : impossible de calculer la p-value exacte avec des
## ex-aequos## Warning in wilcox.test.default(BP_C$symp_Hémoptysie_1_.,
## BP_C$symp_Hémoptysie_3_., : impossible de calculer une p-value exacte avec des
## zéros##
## Wilcoxon signed rank test with continuity correction
##
## data: BP_C$symp_Hémoptysie_1_. and BP_C$symp_Hémoptysie_3_.
## V = 28, p-value = 1
## alternative hypothesis: true location shift is not equal to 0les scores symptome toux entre la V1 et la V3 ne sont pas significativement different p-value=1
condition de validité
shapiro.test(BP_C$Del_hemoptysie_2_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_hemoptysie_2_vs_1
## W = 0.86886, p-value = 0.01122wilcox.test(BP_C$symp_Hémoptysie_1_.,BP_C$symp_Hémoptysie_2_.,paired=TRUE)## Warning in wilcox.test.default(BP_C$symp_Hémoptysie_1_.,
## BP_C$symp_Hémoptysie_2_., : impossible de calculer la p-value exacte avec des
## ex-aequos## Warning in wilcox.test.default(BP_C$symp_Hémoptysie_1_.,
## BP_C$symp_Hémoptysie_2_., : impossible de calculer une p-value exacte avec des
## zéros##
## Wilcoxon signed rank test with continuity correction
##
## data: BP_C$symp_Hémoptysie_1_. and BP_C$symp_Hémoptysie_2_.
## V = 55.5, p-value = 0.1942
## alternative hypothesis: true location shift is not equal to 0La p-value du test est >0.05., ainsi les scores symptome hemoptysie entre la V1 et la V2 ne sont pas significativement different
condition de validité
shapiro.test(BP_C$Del_total_LC13_3_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_3_vs_1
## W = 0.89654, p-value = 0.05003t.test(BP_C$Total_EORTC.LC13_1,BP_C$Total_EORTC.LC13_3,paired=TRUE)##
## Paired t-test
##
## data: BP_C$Total_EORTC.LC13_1 and BP_C$Total_EORTC.LC13_3
## t = -1.9689, df = 16, p-value = 0.06653
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -18.3811406 0.6787876
## sample estimates:
## mean difference
## -8.851176La p-value du test est > 0.05., ainsi les scores EORTC_LC 13 entre la V1 et la V3 ne sont pas significativement differents
condition de validité
shapiro.test(BP_C$Del_total_LC13_2_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_LC13_2_vs_1
## W = 0.87499, p-value = 0.01439wilcox.test(BP_C$Total_EORTC.LC13_1,BP_C$Total_EORTC.LC13_2,paired=TRUE)## Warning in wilcox.test.default(BP_C$Total_EORTC.LC13_1,
## BP_C$Total_EORTC.LC13_2, : impossible de calculer la p-value exacte avec des ex-
## aequos##
## Wilcoxon signed rank test with continuity correction
##
## data: BP_C$Total_EORTC.LC13_1 and BP_C$Total_EORTC.LC13_2
## V = 43.5, p-value = 0.07064
## alternative hypothesis: true location shift is not equal to 0La p-value du test est <0.05, ainsi les scores EORTC_LC 13 entre la V1 et la V3 sont significativement differents
condition de validité
shapiro.test(BP_C$Del_total_QLC30_3_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_3_vs_1
## W = 0.88373, p-value = 0.03018wilcox.test(BP_C$Total_EORTC.QLQ30_1,BP_C$Total_EORTC.QLQ30_3,paired=TRUE)##
## Wilcoxon signed rank exact test
##
## data: BP_C$Total_EORTC.QLQ30_1 and BP_C$Total_EORTC.QLQ30_3
## V = 27, p-value = 0.008965
## alternative hypothesis: true location shift is not equal to 0La p-value du test est <0.05, ainsi les scores EORTC_QLQ30 entre la V1 et la V3 sont significativement differents
condition de validité
shapiro.test(BP_C$Del_total_QLC30_2_vs_1)##
## Shapiro-Wilk normality test
##
## data: BP_C$Del_total_QLC30_2_vs_1
## W = 0.92141, p-value = 0.1055t.test(BP_C$Total_EORTC.QLQ30_1,BP_C$Total_EORTC.QLQ30_3,paired=TRUE)##
## Paired t-test
##
## data: BP_C$Total_EORTC.QLQ30_1 and BP_C$Total_EORTC.QLQ30_3
## t = -2.7362, df = 17, p-value = 0.01407
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -29.000558 -3.748331
## sample estimates:
## mean difference
## -16.37444La p-value du test est <0.05, ainsi les scores EORTC_QLQ30 entre la V1 et la V2sont significativement differents dans le sens d’une augmentation