Bicuspid Expansion v4
Juan Pablo Gomez Bravo / Jorge Andres Chavarria
31/5/2023
library(readxl)
library(ggplot2)
library(CGPfunctions)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric#Definir directorio
setwd("G:/TRABAJO/CONSULTORIAS/TRABAJOS VARIOS/JORGE CHAVARRIA/analisis3")data1 = read_excel("Bicuspid3.xlsx")
head(data1,5)## # A tibble: 5 × 18
## StudyI…¹ Relat…² RSEcalc Ellip…³ Ellip…⁴ MaxSi…⁵ MinSi…⁶ Predi…⁷ Postd…⁸ CCV
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.919 1 1.05 0 1.23 1.09 1 0 1186
## 2 2 0.879 0 1.08 0 1.63 1.35 0 0 1780
## 3 3 0.915 1 1.13 1 1.3 1.19 0 0 454
## 4 4 0.905 1 1.12 1 1.46 1.28 0 0 388
## 5 5 0.904 1 1.11 1 1.27 1.17 1 0 629
## # … with 8 more variables: Raphaecalcification <dbl>, AVAi <dbl>,
## # MeanGradientmmHg <dbl>, ICD4mm <dbl>, ADDiameter <dbl>, ICD4mm_calc <dbl>,
## # SVDmax <dbl>, SVDmin <dbl>, and abbreviated variable names ¹StudyIDglobal,
## # ²RelativeStentExpansion, ³Ellipticity, ⁴Ellipticitycalc, ⁵MaxSinAnnDcalc,
## # ⁶MinSinusAnnDcalc, ⁷Predilatation, ⁸PostdilationANALISIS RELATIVE STENT EXPANSION
#Relacion entre Maximum Sinus Diameter Indexed vs Relative Stent Expansion %
g1=ggplot(data=data1,mapping=
aes(x=MaxSinAnnDcalc,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g1 + labs(title = "Relative Stent Expansion % vs Maximum Sinus Diameter Indexed",
x = "Maximum Sinus Diameter Indexed",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'
correlation_test1 <- cor.test(data1$MaxSinAnnDcalc, data1$RelativeStentExpansion)
print(correlation_test1)##
## Pearson's product-moment correlation
##
## data: data1$MaxSinAnnDcalc and data1$RelativeStentExpansion
## t = 0.89325, df = 99, p-value = 0.3739
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1079097 0.2799622
## sample estimates:
## cor
## 0.0894153Podemos observar que no existe una correlacion significativa entre MaxSinAnnDcalc y RelativeStentExpansion
#Relacion entre Minimun Sinus Diameter Indexed vs Relative Stent Expansion %
g2=ggplot(data=data1,mapping=
aes(x=MinSinusAnnDcalc,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g2 + labs(title = "Relative Stent Expansion % vs Minimun Sinus Diameter Indexed",
x = "Minimun Sinus Diameter Indexed",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'
correlation_test2 <- cor.test(data1$MinSinusAnnDcalc, data1$RelativeStentExpansion)
print(correlation_test2)##
## Pearson's product-moment correlation
##
## data: data1$MinSinusAnnDcalc and data1$RelativeStentExpansion
## t = 1.4881, df = 99, p-value = 0.1399
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04894158 0.33370504
## sample estimates:
## cor
## 0.1479125#Relacion entre Postdilatation vs Relative Stent Expansion %
boxplot(data1$RelativeStentExpansion~data1$Postdilation,
xlab = 'Postdilation',
ylab = 'Relative Stent Expansion %',
title=('Postdilatation vs Relative Stent Expansion %'),
col= 'bisque')
t_test1 <- t.test(data1$RelativeStentExpansion ~ as.factor(data1$Postdilation))
print(t_test1)##
## Welch Two Sample t-test
##
## data: data1$RelativeStentExpansion by as.factor(data1$Postdilation)
## t = -4.3214, df = 41.991, p-value = 9.307e-05
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.04023644 -0.01461914
## sample estimates:
## mean in group 0 mean in group 1
## 0.8993949 0.9268227Se evidencia una diferencia significativa de RelativeStentExpansion en funcion de la Postdilatacion, si postdilata aumenta la expacion relativa.
ggplot(data = data1) + geom_density(aes(x=RelativeStentExpansion,fill=factor(Postdilation)),
bins=10, position = "identity",alpha = 0.5)## Warning: Ignoring unknown parameters: bins
#Relacion entre Predilatation vs Relative Stent Expansion %
boxplot(data1$RelativeStentExpansion~data1$Predilatation,
xlab = 'Predilatation',
ylab = 'Relative Stent Expansion %',
title=('Predilatation vs Relative Stent Expansion %'),
col= 'bisque')
ggplot(data = data1) + geom_density(aes(x=RelativeStentExpansion,fill=factor(Predilatation)),
bins=10, position = "identity",alpha = 0.5)## Warning: Ignoring unknown parameters: bins
t_test2 <- t.test(data1$RelativeStentExpansion ~ as.factor(data1$Predilatation))
print(t_test2)##
## Welch Two Sample t-test
##
## data: data1$RelativeStentExpansion by as.factor(data1$Predilatation)
## t = -0.72359, df = 70.094, p-value = 0.4717
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.018060308 0.008444153
## sample estimates:
## mean in group 0 mean in group 1
## 0.9037507 0.9085588No existe una diferencia significativa de RelativeStentExpansion en función de la Predilatation
#Relacion entre Raphe Calcification vs Relative Stent Expansion %
boxplot(data1$RelativeStentExpansion~data1$Raphaecalcification,
xlab = 'Raphae Calcification',
ylab = 'Relative Stent Expansion %',
title=('Raphae Calcification vs Relative Stent Expansion %'),
col= 'ivory')
ggplot(data = data1) + geom_density(aes(x=RelativeStentExpansion,fill=factor(Raphaecalcification)),
bins=10, position = "identity",alpha = 0.5)## Warning: Ignoring unknown parameters: bins
t_test3 <- t.test(data1$RelativeStentExpansion ~ as.factor(data1$Raphaecalcification))
print(t_test3)##
## Welch Two Sample t-test
##
## data: data1$RelativeStentExpansion by as.factor(data1$Raphaecalcification)
## t = 0.50361, df = 84.234, p-value = 0.6158
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.009511937 0.015963964
## sample estimates:
## mean in group 0 mean in group 1
## 0.9074135 0.9041875No existe diferencia significativa de RelativeStentExpansion en función del RelativeStentExpansion
#Relacion entre AVAi vs Relative Stent Expansion %
g4=ggplot(data=data1,mapping=
aes(x=AVAi,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g4 + labs(title = "Relative Stent Expansion % vs AVAi",
x = "AVAi",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 26 rows containing non-finite values (stat_smooth).## Warning: Removed 26 rows containing missing values (geom_point).
correlation_test3 <- cor.test(data1$AVAi, data1$RelativeStentExpansion)
print(correlation_test3)##
## Pearson's product-moment correlation
##
## data: data1$AVAi and data1$RelativeStentExpansion
## t = 1.9905, df = 73, p-value = 0.05029
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -7.606249e-05 4.316249e-01
## sample estimates:
## cor
## 0.2268897Existe correlacion positiva significativa entre AVAi y RelativeStentExpansion
#Relacion entre Mean Gradient (mmHg) vs Relative Stent Expansion %
g5=ggplot(data=data1,mapping=
aes(x=MeanGradientmmHg,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g5 + labs(title = "Relative Stent Expansion % vs Mean Gradient (mmHg)",
x = "Mean Gradient (mmHg)",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'
correlation_test4 <- cor.test(data1$MeanGradientmmHg, data1$RelativeStentExpansion)
print(correlation_test4)##
## Pearson's product-moment correlation
##
## data: data1$MeanGradientmmHg and data1$RelativeStentExpansion
## t = -3.408, df = 99, p-value = 0.0009477
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4885334 -0.1372872
## sample estimates:
## cor
## -0.3240323la correlacion entre MeanGradientmmHg y RelativeStentExpansion es negativa y significativa
#Relacion entre ICD4mm_calc(IDC4mm/Area Derived Diameter) vs Relative Stent Expansion %
g6=ggplot(data=data1,mapping=
aes(x=ICD4mm_calc,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g6 + labs(title = "Relative Stent Expansion % vs Intercomisural Diameter at 4 mm Indexed",
x = "Intercomisural Diameter at 4 mm Indexed",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'
correlation_test5 <- cor.test(data1$ICD4mm_calc, data1$RelativeStentExpansion)
print(correlation_test5)##
## Pearson's product-moment correlation
##
## data: data1$ICD4mm_calc and data1$RelativeStentExpansion
## t = 0.08373, df = 99, p-value = 0.9334
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1873324 0.2035195
## sample estimates:
## cor
## 0.008414921La correlacion es nula para estas variables
#Relacion entre CCV vs Relative Stent Expansion %
g7=ggplot(data=data1,mapping=
aes(x=CCV,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g7 + labs(title = "Relative Stent Expansion % vs Calcium contrast volume",
x = "Calcium contrast volume",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'
correlation_test6 <- cor.test(data1$CCV, data1$RelativeStentExpansion)
print(correlation_test6)##
## Pearson's product-moment correlation
##
## data: data1$CCV and data1$RelativeStentExpansion
## t = 0.21349, df = 99, p-value = 0.8314
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1747201 0.2159854
## sample estimates:
## cor
## 0.02145167La correlacion entre CCV y RelativeStentExpansion es nula.
#Relacion entre SVDmax vs Relative Stent Expansion %
g8=ggplot(data=data1,mapping=
aes(x=SVDmax,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g8 + labs(title = "Relative Stent Expansion % vs SVDmax",
x = "SVDmax",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
correlation_test7 <- cor.test(data1$SVDmax, data1$RelativeStentExpansion)
print(correlation_test7)##
## Pearson's product-moment correlation
##
## data: data1$SVDmax and data1$RelativeStentExpansion
## t = 2.0889, df = 97, p-value = 0.03933
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01049871 0.38896086
## sample estimates:
## cor
## 0.2074805la correlacion entre SVDmax y RelativeSten expasion es significativa
#Relacion entre SVDmax vs Relative Stent Expansion %
g9=ggplot(data=data1,mapping=
aes(x=SVDmin,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
geom_smooth(method = "lm")
g9 + labs(title = "Relative Stent Expansion % vs SVDmin",
x = "SVDmin",
y= "Relative Stent Expansion %") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
correlation_test8 <- cor.test(data1$SVDmin, data1$RelativeStentExpansion)
print(correlation_test8)##
## Pearson's product-moment correlation
##
## data: data1$SVDmin and data1$RelativeStentExpansion
## t = 2.5185, df = 97, p-value = 0.01342
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05291916 0.42439847
## sample estimates:
## cor
## 0.2477429la correlacion entre SVDmin y RelativeSten expasion es significativa
#Relacion entre CCV vs RSEcalc
boxplot(data1$CCV~data1$RSEcalc,
xlab = 'RSEcalc',
ylab = 'CCV',
title=('RSEcalc vs CCV'),
col= 'ivory')
REGRESION LINEAL EN FUNCION DE RELATIVE STENT EXPANSION
#Regresion inicial
mod1=lm(RelativeStentExpansion ~ MaxSinAnnDcalc + MinSinusAnnDcalc + ICD4mm_calc +
CCV + Raphaecalcification + Predilatation + Postdilation,
data = data1)
summary(mod1)##
## Call:
## lm(formula = RelativeStentExpansion ~ MaxSinAnnDcalc + MinSinusAnnDcalc +
## ICD4mm_calc + CCV + Raphaecalcification + Predilatation +
## Postdilation, data = data1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095751 -0.012325 0.004435 0.021326 0.066917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.472e-01 6.014e-02 14.086 < 2e-16 ***
## MaxSinAnnDcalc -4.695e-03 3.161e-02 -0.149 0.882248
## MinSinusAnnDcalc 4.654e-02 3.785e-02 1.230 0.221975
## ICD4mm_calc 1.149e-03 5.354e-02 0.021 0.982921
## CCV 2.392e-06 5.574e-06 0.429 0.668804
## Raphaecalcification -4.606e-03 6.895e-03 -0.668 0.505758
## Predilatation 4.301e-03 6.847e-03 0.628 0.531449
## Postdilation 2.635e-02 7.471e-03 3.526 0.000656 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03069 on 93 degrees of freedom
## Multiple R-squared: 0.1504, Adjusted R-squared: 0.08649
## F-statistic: 2.353 on 7 and 93 DF, p-value: 0.02944# Aplicar el procedimiento stepwise
mod1_1 <- step(mod1)## Start: AIC=-696.08
## RelativeStentExpansion ~ MaxSinAnnDcalc + MinSinusAnnDcalc +
## ICD4mm_calc + CCV + Raphaecalcification + Predilatation +
## Postdilation
##
## Df Sum of Sq RSS AIC
## - ICD4mm_calc 1 0.0000004 0.087581 -698.08
## - MaxSinAnnDcalc 1 0.0000208 0.087602 -698.06
## - CCV 1 0.0001734 0.087754 -697.88
## - Predilatation 1 0.0003716 0.087952 -697.65
## - Raphaecalcification 1 0.0004203 0.088001 -697.60
## - MinSinusAnnDcalc 1 0.0014236 0.089004 -696.45
## <none> 0.087581 -696.08
## - Postdilation 1 0.0117107 0.099291 -685.41
##
## Step: AIC=-698.08
## RelativeStentExpansion ~ MaxSinAnnDcalc + MinSinusAnnDcalc +
## CCV + Raphaecalcification + Predilatation + Postdilation
##
## Df Sum of Sq RSS AIC
## - MaxSinAnnDcalc 1 0.0000205 0.087602 -700.06
## - CCV 1 0.0001758 0.087757 -699.88
## - Predilatation 1 0.0003727 0.087954 -699.65
## - Raphaecalcification 1 0.0004304 0.088012 -699.59
## - MinSinusAnnDcalc 1 0.0014374 0.089019 -698.44
## <none> 0.087581 -698.08
## - Postdilation 1 0.0118207 0.099402 -687.29
##
## Step: AIC=-700.06
## RelativeStentExpansion ~ MinSinusAnnDcalc + CCV + Raphaecalcification +
## Predilatation + Postdilation
##
## Df Sum of Sq RSS AIC
## - CCV 1 0.0001568 0.087758 -701.88
## - Predilatation 1 0.0003847 0.087986 -701.62
## - Raphaecalcification 1 0.0004112 0.088013 -701.58
## <none> 0.087602 -700.06
## - MinSinusAnnDcalc 1 0.0019732 0.089575 -699.81
## - Postdilation 1 0.0118180 0.099420 -689.28
##
## Step: AIC=-701.88
## RelativeStentExpansion ~ MinSinusAnnDcalc + Raphaecalcification +
## Predilatation + Postdilation
##
## Df Sum of Sq RSS AIC
## - Raphaecalcification 1 0.0002981 0.088057 -703.53
## - Predilatation 1 0.0005818 0.088340 -703.21
## <none> 0.087758 -701.88
## - MinSinusAnnDcalc 1 0.0018529 0.089611 -701.77
## - Postdilation 1 0.0118333 0.099592 -691.10
##
## Step: AIC=-703.53
## RelativeStentExpansion ~ MinSinusAnnDcalc + Predilatation + Postdilation
##
## Df Sum of Sq RSS AIC
## - Predilatation 1 0.0004955 0.088552 -704.97
## - MinSinusAnnDcalc 1 0.0017105 0.089767 -703.59
## <none> 0.088057 -703.53
## - Postdilation 1 0.0120953 0.100152 -692.53
##
## Step: AIC=-704.97
## RelativeStentExpansion ~ MinSinusAnnDcalc + Postdilation
##
## Df Sum of Sq RSS AIC
## - MinSinusAnnDcalc 1 0.0015918 0.090144 -705.17
## <none> 0.088552 -704.97
## - Postdilation 1 0.0122817 0.100834 -693.85
##
## Step: AIC=-705.17
## RelativeStentExpansion ~ Postdilation
##
## Df Sum of Sq RSS AIC
## <none> 0.090144 -705.17
## - Postdilation 1 0.012945 0.103089 -693.62summary(mod1_1)##
## Call:
## lm(formula = RelativeStentExpansion ~ Postdilation, data = data1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.101095 -0.013223 0.003905 0.019005 0.069305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.899395 0.003395 264.919 < 2e-16 ***
## Postdilation 0.027428 0.007274 3.771 0.000277 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03018 on 99 degrees of freedom
## Multiple R-squared: 0.1256, Adjusted R-squared: 0.1167
## F-statistic: 14.22 on 1 and 99 DF, p-value: 0.0002772REGRESION LOGISTICA EN FUNCION DE RSEcalc(1;0)
#Modelo binario
mod2 <- glm(RSEcalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + ICD4mm_calc +
CCV + Raphaecalcification + Predilatation + Postdilation,
data = data1, family = "binomial")
summary(mod2)##
## Call:
## glm(formula = RSEcalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + ICD4mm_calc +
## CCV + Raphaecalcification + Predilatation + Postdilation,
## family = "binomial", data = data1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8469 -1.2394 0.5693 1.0364 1.2547
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.2522660 4.3529152 -0.977 0.3286
## MaxSinAnnDcalc 0.4677107 2.2286440 0.210 0.8338
## MinSinusAnnDcalc 1.4704389 2.6871074 0.547 0.5842
## ICD4mm_calc 1.9145984 3.7622012 0.509 0.6108
## CCV -0.0002413 0.0003972 -0.608 0.5435
## Raphaecalcification 0.2461632 0.4859645 0.507 0.6125
## Predilatation 0.1988075 0.4853123 0.410 0.6821
## Postdilation 1.5937102 0.6722459 2.371 0.0178 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 132.71 on 100 degrees of freedom
## Residual deviance: 123.60 on 93 degrees of freedom
## AIC: 139.6
##
## Number of Fisher Scoring iterations: 4# Aplicar el procedimiento stepwise
mod2_1 <- step(mod2)## Start: AIC=139.6
## RSEcalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + ICD4mm_calc + CCV +
## Raphaecalcification + Predilatation + Postdilation
##
## Df Deviance AIC
## - MaxSinAnnDcalc 1 123.65 137.65
## - Predilatation 1 123.77 137.77
## - Raphaecalcification 1 123.86 137.86
## - ICD4mm_calc 1 123.86 137.86
## - MinSinusAnnDcalc 1 123.90 137.90
## - CCV 1 123.97 137.97
## <none> 123.60 139.60
## - Postdilation 1 130.79 144.79
##
## Step: AIC=137.65
## RSEcalc ~ MinSinusAnnDcalc + ICD4mm_calc + CCV + Raphaecalcification +
## Predilatation + Postdilation
##
## Df Deviance AIC
## - Predilatation 1 123.81 135.81
## - Raphaecalcification 1 123.88 135.88
## - ICD4mm_calc 1 123.97 135.97
## - CCV 1 123.98 135.98
## - MinSinusAnnDcalc 1 124.28 136.28
## <none> 123.65 137.65
## - Postdilation 1 130.84 142.84
##
## Step: AIC=135.81
## RSEcalc ~ MinSinusAnnDcalc + ICD4mm_calc + CCV + Raphaecalcification +
## Postdilation
##
## Df Deviance AIC
## - Raphaecalcification 1 124.04 134.04
## - CCV 1 124.04 134.04
## - ICD4mm_calc 1 124.08 134.08
## - MinSinusAnnDcalc 1 124.44 134.44
## <none> 123.81 135.81
## - Postdilation 1 131.03 141.03
##
## Step: AIC=134.04
## RSEcalc ~ MinSinusAnnDcalc + ICD4mm_calc + CCV + Postdilation
##
## Df Deviance AIC
## - CCV 1 124.15 132.15
## - ICD4mm_calc 1 124.24 132.24
## - MinSinusAnnDcalc 1 124.79 132.79
## <none> 124.04 134.04
## - Postdilation 1 131.09 139.09
##
## Step: AIC=132.15
## RSEcalc ~ MinSinusAnnDcalc + ICD4mm_calc + Postdilation
##
## Df Deviance AIC
## - ICD4mm_calc 1 124.34 130.34
## - MinSinusAnnDcalc 1 125.03 131.03
## <none> 124.15 132.15
## - Postdilation 1 131.22 137.22
##
## Step: AIC=130.34
## RSEcalc ~ MinSinusAnnDcalc + Postdilation
##
## Df Deviance AIC
## - MinSinusAnnDcalc 1 125.51 129.51
## <none> 124.34 130.34
## - Postdilation 1 131.26 135.26
##
## Step: AIC=129.51
## RSEcalc ~ Postdilation
##
## Df Deviance AIC
## <none> 125.51 129.51
## - Postdilation 1 132.71 134.71summary(mod2_1)##
## Call:
## glm(formula = RSEcalc ~ Postdilation, family = "binomial", data = data1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9962 -1.2985 0.5415 1.0609 1.0609
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2803 0.2272 1.234 0.217
## Postdilation 1.5655 0.6615 2.367 0.018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 132.71 on 100 degrees of freedom
## Residual deviance: 125.51 on 99 degrees of freedom
## AIC: 129.51
##
## Number of Fisher Scoring iterations: 4