##chargement des packages----
library(questionr)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tableone)
library(labelled)
library(gtsummary)
## #BlackLivesMatter
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
library(readxl)
library(effects)
## Le chargement a nécessité le package : carData
## lattice theme set by effectsTheme()
## See ?effectsTheme for details.
library(survival)
library(survminer)
## Le chargement a nécessité le package : ggpubr
##
## Attachement du package : 'survminer'
##
## L'objet suivant est masqué depuis 'package:survival':
##
## myeloma
library(ggplot2)
library(dplyr)
library(knitr)
library(cowplot)
##
## Attachement du package : 'cowplot'
##
## L'objet suivant est masqué depuis 'package:ggpubr':
##
## get_legend
##
## L'objet suivant est masqué depuis 'package:lubridate':
##
## stamp
##chargement des données
stab_rheo <- read_excel("Y:/fp/Eq 14 CRCT/Thèse Pauline Claraz/rehologie/stab_rheo.xlsx")
##sous-series
gelta<-filter(stab_rheo, c(type== "gelta"))
gelf<-filter(stab_rheo, c(type== "gelf"))
gelita<-filter(stab_rheo, c(type== "gelita"))
gelif<-filter(stab_rheo, c(type== "gelif"))
xanta<-filter(stab_rheo, c(type== "xanta"))
xanf<-filter(stab_rheo, c(type== "xanf"))
xanita<-filter(stab_rheo, c(type== "xanita"))
xanif<-filter(stab_rheo, c(type== "xanif"))
inta<-filter(stab_rheo, c(type== "inta"))
inf<-filter(stab_rheo, c(type== "inf"))
inita<-filter(stab_rheo, c(type== "inita"))
inif<-filter(stab_rheo, c(type== "inif"))
obta<-filter(stab_rheo, c(type== "obta"))
obf<-filter(stab_rheo, c(type== "obf"))
obita<-filter(stab_rheo, c(type== "obita"))
obif<-filter(stab_rheo, c(type== "obif"))
##données à J0
visco_j0<-filter(stab_rheo, c(jour==0))
viscotaj0<-filter(visco_j0, c(temperature=="rt"))
##donnees par vecteur à J0
gellane<-filter(viscotaj0, c(vecteur=="gellane"))
xanthane<-filter(viscotaj0, c(vecteur=="xanthane"))
inorpha<-filter(viscotaj0, c(vecteur=="inorpha"))
orablend<-filter(viscotaj0, c(vecteur=="orablend"))
##Données à J30
viscoj30<-filter(stab_rheo, c(jour==30))
###données à J30 selon vecteur
gellanej30<-filter(viscoj30, c(vecteur=="gellane"))
xanthanej30<-filter(viscoj30, c(vecteur=="xanthane"))
inorphaj30<-filter(viscoj30, c(vecteur=="inorpha"))
orablendj30<-filter(viscoj30, c(vecteur=="orablend"))
##anova visco au cours du temps
mod1<-lm(visco~jour, data=gelta)
summary(mod1)
##
## Call:
## lm(formula = visco ~ jour, data = gelta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9270 -1.4608 -0.7607 0.6740 4.9703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.1343 1.0046 80.766 2.07e-15 ***
## jour -0.1162 0.0179 -6.495 6.94e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.08 on 10 degrees of freedom
## Multiple R-squared: 0.8084, Adjusted R-squared: 0.7892
## F-statistic: 42.18 on 1 and 10 DF, p-value: 6.943e-05
mod2<-lm(visco~jour, data=gelf)
summary(mod2)
##
## Call:
## lm(formula = visco ~ jour, data = gelf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.194 -5.563 -0.628 5.912 16.678
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 87.13967 4.22348 20.632 1.58e-09 ***
## jour 0.10804 0.07525 1.436 0.182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.743 on 10 degrees of freedom
## Multiple R-squared: 0.1709, Adjusted R-squared: 0.088
## F-statistic: 2.061 on 1 and 10 DF, p-value: 0.1816
mod3<-lm(visco~jour, data=gelita)
summary(mod3)
##
## Call:
## lm(formula = visco ~ jour, data = gelita)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0700 -1.3567 -0.2467 2.0050 7.4767
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.9600 2.2616 37.125 4.79e-12 ***
## jour -0.1328 0.0403 -3.295 0.00808 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.682 on 10 degrees of freedom
## Multiple R-squared: 0.5206, Adjusted R-squared: 0.4726
## F-statistic: 10.86 on 1 and 10 DF, p-value: 0.00808
mod4<-lm(visco~jour, data=gelif)
summary(mod4)
##
## Call:
## lm(formula = visco ~ jour, data = gelif)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.111 -4.258 -0.426 4.216 9.538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 87.65267 3.03235 28.906 5.72e-11 ***
## jour 0.18698 0.05403 3.461 0.00612 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.278 on 10 degrees of freedom
## Multiple R-squared: 0.545, Adjusted R-squared: 0.4995
## F-statistic: 11.98 on 1 and 10 DF, p-value: 0.006115
mod5<-lm(visco~jour, data=xanta)
summary(mod5)
##
## Call:
## lm(formula = visco ~ jour, data = xanta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.2117 -2.4167 0.6333 5.2296 8.1683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.1717 2.9521 26.142 1.54e-10 ***
## jour -0.1277 0.0526 -2.427 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.111 on 10 degrees of freedom
## Multiple R-squared: 0.3707, Adjusted R-squared: 0.3078
## F-statistic: 5.891 on 1 and 10 DF, p-value: 0.03562
mod6<-lm(visco~jour, data=xanf)
summary(mod6)
##
## Call:
## lm(formula = visco ~ jour, data = xanf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2947 -0.2380 0.1210 0.4464 2.1490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 74.61400 0.57814 129.059 <2e-16 ***
## jour 0.02902 0.01030 2.817 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.197 on 10 degrees of freedom
## Multiple R-squared: 0.4425, Adjusted R-squared: 0.3868
## F-statistic: 7.938 on 1 and 10 DF, p-value: 0.01824
mod7<-lm(visco~jour, data=xanita)
summary(mod7)
##
## Call:
## lm(formula = visco ~ jour, data = xanita)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.0025 -0.9462 0.5150 1.9275 2.8325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.27000 1.35458 59.996 4.02e-14 ***
## jour -0.04092 0.02414 -1.695 0.121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.804 on 10 degrees of freedom
## Multiple R-squared: 0.2232, Adjusted R-squared: 0.1456
## F-statistic: 2.874 on 1 and 10 DF, p-value: 0.1209
mod8<-lm(visco~jour, data=xanif)
summary(mod8)
##
## Call:
## lm(formula = visco ~ jour, data = xanif)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.8502 -2.1394 0.7595 3.2177 4.5072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82.46317 2.07585 39.73 2.44e-12 ***
## jour -0.08176 0.03699 -2.21 0.0515 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.297 on 10 degrees of freedom
## Multiple R-squared: 0.3282, Adjusted R-squared: 0.2611
## F-statistic: 4.886 on 1 and 10 DF, p-value: 0.05152
mod9<-lm(visco~jour, data=inta)
summary(mod9)
##
## Call:
## lm(formula = visco ~ jour, data = inta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7750 -0.2233 -0.0300 0.3767 0.4317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.523333 0.207867 60.25 3.86e-14 ***
## jour -0.016111 0.003704 -4.35 0.00144 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4303 on 10 degrees of freedom
## Multiple R-squared: 0.6543, Adjusted R-squared: 0.6197
## F-statistic: 18.92 on 1 and 10 DF, p-value: 0.001443
mod10<-lm(visco~jour, data=inf)
summary(mod10)
##
## Call:
## lm(formula = visco ~ jour, data = inf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6382 -0.7442 -0.5978 0.8098 1.9968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.09017 0.60041 20.137 2.01e-09 ***
## jour 0.01276 0.01070 1.192 0.261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.243 on 10 degrees of freedom
## Multiple R-squared: 0.1245, Adjusted R-squared: 0.03692
## F-statistic: 1.422 on 1 and 10 DF, p-value: 0.2606
mod11<-lm(visco~jour, data=inita)
summary(mod11)
##
## Call:
## lm(formula = visco ~ jour, data = inita)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45433 -0.39783 -0.06833 0.17742 1.13467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.861333 0.244936 56.592 7.2e-14 ***
## jour 0.001100 0.004364 0.252 0.806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5071 on 10 degrees of freedom
## Multiple R-squared: 0.006313, Adjusted R-squared: -0.09306
## F-statistic: 0.06353 on 1 and 10 DF, p-value: 0.8061
mod12<-lm(visco~jour, data=inif)
summary(mod12)
##
## Call:
## lm(formula = visco ~ jour, data = inif)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3655 -0.7910 -0.6845 1.3015 2.8545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.757000 0.837613 17.618 7.39e-09 ***
## jour -0.004128 0.014924 -0.277 0.788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.734 on 10 degrees of freedom
## Multiple R-squared: 0.007592, Adjusted R-squared: -0.09165
## F-statistic: 0.0765 on 1 and 10 DF, p-value: 0.7877
mod13<-lm(visco~jour, data=obta)
summary(mod13)
##
## Call:
## lm(formula = visco ~ jour, data = obta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.293 -0.899 1.171 2.139 4.822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 74.02150 2.10898 35.098 8.36e-12 ***
## jour -0.09337 0.03758 -2.485 0.0323 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.366 on 10 degrees of freedom
## Multiple R-squared: 0.3817, Adjusted R-squared: 0.3199
## F-statistic: 6.174 on 1 and 10 DF, p-value: 0.03228
mod14<-lm(visco~jour, data=obf)
summary(mod14)
##
## Call:
## lm(formula = visco ~ jour, data = obf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1780 -0.5527 0.2916 1.0625 3.6720
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.894500 1.403494 51.938 1.69e-13 ***
## jour -0.002572 0.025007 -0.103 0.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.906 on 10 degrees of freedom
## Multiple R-squared: 0.001057, Adjusted R-squared: -0.09884
## F-statistic: 0.01058 on 1 and 10 DF, p-value: 0.9201
mod15<-lm(visco~jour, data=obita)
summary(mod15)
##
## Call:
## lm(formula = visco ~ jour, data = obita)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9983 -0.4833 -0.2533 0.6192 1.1317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.663333 0.343111 211.778 < 2e-16 ***
## jour 0.026833 0.006113 4.389 0.00136 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7103 on 10 degrees of freedom
## Multiple R-squared: 0.6583, Adjusted R-squared: 0.6241
## F-statistic: 19.27 on 1 and 10 DF, p-value: 0.001358
mod16<-lm(visco~jour, data=obif)
summary(mod16)
##
## Call:
## lm(formula = visco ~ jour, data = obif)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2993 -1.2573 0.8627 1.4594 2.5947
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.72733 1.12724 65.405 1.7e-14 ***
## jour -0.02587 0.02008 -1.288 0.227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.334 on 10 degrees of freedom
## Multiple R-squared: 0.1423, Adjusted R-squared: 0.0565
## F-statistic: 1.659 on 1 and 10 DF, p-value: 0.2268
##différence visco selon presence irbesartan
###gellane
tbl_summary(
gellane, include = c("visco"),
type = c(visco) ~ "continuous2",
by="poudre",
statistic = all_continuous() ~ "{mean} [{sd}]",
)%>%
add_p
| Characteristic |
irbesartan, N = 3 |
seul, N = 3 |
p-value |
| visco |
|
|
0.2 |
| Mean [SD] |
82.51 [1.51] |
80.97 [1.72] |
|
###xanthane
tbl_summary(
xanthane, include = c("visco"),
type = c(visco) ~ "continuous2",
by="poudre",
statistic = all_continuous() ~ "{mean} [{sd}]",
)%>%
add_p
| Characteristic |
irbesartan, N = 3 |
seul, N = 3 |
p-value |
| visco |
|
|
0.10 |
| Mean [SD] |
80.01 [0.41] |
74.77 [0.07] |
|
###inorpha
tbl_summary(
inorpha, include = c("visco"),
type = c(visco) ~ "continuous2",
by="poudre",
statistic = all_continuous() ~ "{mean} [{sd}]",
)%>%
add_p
| Characteristic |
irbesartan, N = 3 |
seul, N = 3 |
p-value |
| visco |
|
|
0.10 |
| Mean [SD] |
14.04 [0.08] |
12.79 [0.21] |
|
###orablend
tbl_summary(
orablend, include = c("visco"),
type = c(visco) ~ "continuous2",
by="poudre",
statistic = all_continuous() ~ "{mean} [{sd}]",
)%>%
add_p
| Characteristic |
irbesartan, N = 3 |
seul, N = 3 |
p-value |
| visco |
|
|
0.7 |
| Mean [SD] |
72.15 [0.45] |
72.33 [1.29] |
|
##Difference selon température à J30
tbl_summary(
gellanej30, include=c("visco"),
by="temperature",
statistic = all_continuous() ~"{mean} [{sd}]",
)%>%
add_p
| Characteristic |
fridge, N = 6 |
rt, N = 6 |
p-value |
| visco |
99 [3] |
79 [2] |
0.002 |
tbl_summary(
xanthanej30, include=c("visco"),
by="temperature",
statistic = all_continuous() ~"{mean} [{sd}]",
)%>%
add_p
| Characteristic |
fridge, N = 6 |
rt, N = 6 |
p-value |
| visco |
78.48 [3.95] |
77.57 [3.70] |
0.8 |
tbl_summary(
inorphaj30, include=c("visco"),
by="temperature",
statistic = all_continuous() ~"{mean} [{sd}]",
)%>%
add_p
| Characteristic |
fridge, N = 6 |
rt, N = 6 |
p-value |
| visco |
13.73 [2.34] |
12.67 [1.14] |
0.5 |
tbl_summary(
orablendj30, include=c("visco"),
by="temperature",
statistic = all_continuous() ~"{mean} [{sd}]",
)%>%
add_p
| Characteristic |
fridge, N = 6 |
rt, N = 6 |
p-value |
| visco |
73.95 [0.70] |
73.42 [0.71] |
0.2 |