Regresiones Expansion v6

library(readxl)
library(ggplot2)
library(CGPfunctions)
library(plotly)
library(lmtest)

#Definir directorio
setwd("G:/TRABAJO/CONSULTORIAS/TRABAJOS VARIOS/JORGE CHAVARRIA/analisis4")

data1 = read_excel("Bicuspid4.xlsx")
head(data1,5)

Modelo 1

mod1=lm(RelativeStentExpansion ~ 
        Predilatation + Postdilation +
        Raphaecalcification +
        SVDmax +
        ICD4mm + 
        CCV
        , 
        data = data1)
summary(mod1)

Call:
lm(formula = RelativeStentExpansion ~ Predilatation + Postdilation + 
    Raphaecalcification + SVDmax + ICD4mm + CCV, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.095076 -0.009110  0.006183  0.015923  0.057555 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          8.184e-01  3.273e-02  25.002  < 2e-16 ***
Predilatation        3.359e-03  6.708e-03   0.501 0.617715    
Postdilation         2.917e-02  7.373e-03   3.957 0.000149 ***
Raphaecalcification -1.790e-03  6.630e-03  -0.270 0.787762    
SVDmax               1.232e-03  9.888e-04   1.246 0.215808    
ICD4mm               1.466e-03  1.618e-03   0.906 0.367438    
CCV                 -6.865e-06  6.108e-06  -1.124 0.263972    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03005 on 92 degrees of freedom
Multiple R-squared:  0.1939,    Adjusted R-squared:  0.1413 
F-statistic: 3.688 on 6 and 92 DF,  p-value: 0.002502

Calculos Intervalo de confianza Regresion Lineal Modelo 1

# Obtener el resumen del modelo
summary_mod1 <- summary(mod1)

# Extraer los coeficientes
coefficients <- coef(summary_mod1)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod1)

# Extraer los p-values
p_values <- summary_mod1$coefficients[, 4]

# Crear el data frame consolidado
consolidado1 <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado Modelo 1
print(consolidado1)

NA

Modelo 2

mod2=lm(RelativeStentExpansion ~ 
        Predilatation + Postdilation +
        Raphaecalcification +
        SVDmax +
        ICD4mm_calc +  #indexado 
        CCV
        , 
        data = data1)
summary(mod2)

Call:
lm(formula = RelativeStentExpansion ~ Predilatation + Postdilation + 
    Raphaecalcification + SVDmax + ICD4mm_calc + CCV, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.095424 -0.011741  0.005365  0.014660  0.056493 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          8.260e-01  5.926e-02  13.940  < 2e-16 ***
Predilatation        3.456e-03  6.804e-03   0.508 0.612712    
Postdilation         2.822e-02  7.334e-03   3.848 0.000219 ***
Raphaecalcification -2.323e-03  6.653e-03  -0.349 0.727725    
SVDmax               1.809e-03  7.495e-04   2.414 0.017773 *  
ICD4mm_calc          9.590e-03  5.092e-02   0.188 0.851034    
CCV                 -5.186e-06  5.833e-06  -0.889 0.376285    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03018 on 92 degrees of freedom
Multiple R-squared:  0.187, Adjusted R-squared:  0.134 
F-statistic: 3.527 on 6 and 92 DF,  p-value: 0.003468

Calculos Intervalo de confianza Regresion Lineal Modelo 2

# Obtener el resumen del modelo
summary_mod2 <- summary(mod2)

# Extraer los coeficientes
coefficients <- coef(summary_mod2)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod2)

# Extraer los p-values
p_values <- summary_mod2$coefficients[, 4]

# Crear el data frame consolidado
consolidado2 <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado Modelo 1
print(consolidado2)

NA

Modelo 3

mod3=lm(RelativeStentExpansion ~ 
        Predilatation + Postdilation +
        Raphaecalcification +
        SVDmax +
        ICD4mm + 
        CCV_calc        #indexado 
        , 
        data = data1)
summary(mod3)

Call:
lm(formula = RelativeStentExpansion ~ Predilatation + Postdilation + 
    Raphaecalcification + SVDmax + ICD4mm + CCV_calc, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.095884 -0.009699  0.005565  0.015716  0.058284 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.8289477  0.0300761  27.562  < 2e-16 ***
Predilatation        0.0033817  0.0067118   0.504 0.615570    
Postdilation         0.0290700  0.0073675   3.946 0.000155 ***
Raphaecalcification -0.0017349  0.0066443  -0.261 0.794586    
SVDmax               0.0012032  0.0009864   1.220 0.225680    
ICD4mm               0.0011291  0.0015506   0.728 0.468379    
CCV_calc            -0.0037446  0.0033261  -1.126 0.263157    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03005 on 92 degrees of freedom
Multiple R-squared:  0.1939,    Adjusted R-squared:  0.1413 
F-statistic: 3.689 on 6 and 92 DF,  p-value: 0.002498

Calculos Intervalo de confianza Regresion Lineal Modelo 3

# Obtener el resumen del modelo
summary_mod3 <- summary(mod3)

# Extraer los coeficientes
coefficients <- coef(summary_mod3)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod3)

# Extraer los p-values
p_values <- summary_mod3$coefficients[, 4]

# Crear el data frame consolidado
consolidado3 <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado Modelo 1
print(consolidado3)

NA

Modelo 4

mod4=lm(RelativeStentExpansion ~ 
        Predilatation + Postdilation +
        Raphaecalcification +
        SVDmax +
        ICD4mm_calc +   #indexado
        CCV_calc        #indexado 
        , 
        data = data1)
summary(mod4)

Call:
lm(formula = RelativeStentExpansion ~ Predilatation + Postdilation + 
    Raphaecalcification + SVDmax + ICD4mm_calc + CCV_calc, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.096496 -0.010932  0.007331  0.015341  0.056704 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.825267   0.058974  13.994  < 2e-16 ***
Predilatation        0.003845   0.006817   0.564 0.574031    
Postdilation         0.028434   0.007325   3.882 0.000195 ***
Raphaecalcification -0.001751   0.006707  -0.261 0.794677    
SVDmax               0.001695   0.000695   2.439 0.016642 *  
ICD4mm_calc          0.015031   0.051262   0.293 0.770015    
CCV_calc            -0.003557   0.003336  -1.066 0.289104    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03012 on 92 degrees of freedom
Multiple R-squared:   0.19, Adjusted R-squared:  0.1372 
F-statistic: 3.597 on 6 and 92 DF,  p-value: 0.003006

Calculos Intervalo de confianza Regresion Lineal Modelo 4

# Obtener el resumen del modelo
summary_mod4 <- summary(mod4)

# Extraer los coeficientes
coefficients <- coef(summary_mod4)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod4)

# Extraer los p-values
p_values <- summary_mod4$coefficients[, 4]

# Crear el data frame consolidado
consolidado4 <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado Modelo 1
print(consolidado4)

Modelo 5

mod5=lm(RelativeStentExpansion ~ 
        Predilatation + Postdilation +
        Raphaecalcification +
        MaxSinAnnDcalc + #indexado
        ICD4mm_calc +    #indexado
        CCV_calc         #indexado 
        , 
        data = data1)
summary(mod5)

Call:
lm(formula = RelativeStentExpansion ~ Predilatation + Postdilation + 
    Raphaecalcification + MaxSinAnnDcalc + ICD4mm_calc + CCV_calc, 
    data = data1)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.09955 -0.01282  0.00505  0.01926  0.06358 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.857513   0.060085  14.272  < 2e-16 ***
Predilatation        0.006333   0.006952   0.911 0.364647    
Postdilation         0.027388   0.007554   3.626 0.000472 ***
Raphaecalcification -0.001544   0.006900  -0.224 0.823422    
MaxSinAnnDcalc       0.019804   0.026421   0.750 0.455449    
ICD4mm_calc          0.014205   0.055750   0.255 0.799440    
CCV_calc            -0.002005   0.003368  -0.595 0.553017    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03099 on 92 degrees of freedom
Multiple R-squared:  0.1429,    Adjusted R-squared:  0.08698 
F-statistic: 2.556 on 6 and 92 DF,  p-value: 0.02465

Calculos Intervalo de confianza Regresion Lineal Modelo 5

# Obtener el resumen del modelo
summary_mod5 <- summary(mod5)

# Extraer los coeficientes
coefficients <- coef(summary_mod5)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod5)

# Extraer los p-values
p_values <- summary_mod5$coefficients[, 4]

# Crear el data frame consolidado
consolidado5 <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado Modelo 1
print(consolidado5)

Modelo 6

mod6=lm(RelativeStentExpansion ~ 
        Predilatation + Postdilation +
        Raphaecalcification +
        MaxSinAnnDcalc + #indexado
        ICD4mm_calc +    #indexado
        CCV
        , 
        data = data1)
summary(mod6)

Call:
lm(formula = RelativeStentExpansion ~ Predilatation + Postdilation + 
    Raphaecalcification + MaxSinAnnDcalc + ICD4mm_calc + CCV, 
    data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.096493 -0.013085  0.004841  0.018938  0.065123 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          8.623e-01  5.983e-02  14.413  < 2e-16 ***
Predilatation        4.913e-03  6.979e-03   0.704 0.483223    
Postdilation         2.729e-02  7.566e-03   3.607 0.000503 ***
Raphaecalcification -3.103e-03  6.836e-03  -0.454 0.650940    
MaxSinAnnDcalc       1.755e-02  2.633e-02   0.666 0.506778    
ICD4mm_calc          1.008e-02  5.563e-02   0.181 0.856627    
CCV                  5.428e-07  5.441e-06   0.100 0.920761    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03105 on 92 degrees of freedom
Multiple R-squared:  0.1397,    Adjusted R-squared:  0.08356 
F-statistic: 2.489 on 6 and 92 DF,  p-value: 0.02816

Calculos Intervalo de confianza Regresion Lineal Modelo 6

# Obtener el resumen del modelo
summary_mod6 <- summary(mod6)

# Extraer los coeficientes
coefficients <- coef(summary_mod6)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod6)

# Extraer los p-values
p_values <- summary_mod6$coefficients[, 4]

# Crear el data frame consolidado
consolidado6 <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado Modelo 1
print(consolidado6)

Regresion Logistica

mod7 <- glm(formula = RSEcalc ~ 
              Predilatation + Postdilation +
        Raphaecalcification +
        MaxSinAnnDcalc + #indexado
        ICD4mm_calc +    #indexado
        CCV_calc         #indexado 
        , 
        family = "binomial", data = data1)
summary(mod7)

Call:
glm(formula = RSEcalc ~ Predilatation + Postdilation + Raphaecalcification + 
    MaxSinAnnDcalc + ICD4mm_calc + CCV_calc, family = "binomial", 
    data = data1)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.875  -1.197   0.553   1.039   1.403  

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -5.1949     4.3806  -1.186   0.2357  
Predilatation         0.4193     0.4980   0.842   0.3998  
Postdilation          1.7149     0.6793   2.524   0.0116 *
Raphaecalcification   0.4041     0.4920   0.821   0.4115  
MaxSinAnnDcalc        1.4201     1.9183   0.740   0.4591  
ICD4mm_calc           3.4552     3.9749   0.869   0.3847  
CCV_calc             -0.4157     0.2457  -1.692   0.0907 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 130.86  on 98  degrees of freedom
Residual deviance: 119.03  on 92  degrees of freedom
AIC: 133.03

Number of Fisher Scoring iterations: 4

Calculos de Odd Ratio modelo 7

#Resumen modelo logistico
summary_mod7 <- summary(mod7)

# Extraer los odds ratios
odds_ratios <- exp(summary_mod7$coefficients[, 1])

# Extraer los intervalos de confianza
conf_intervals <- exp(confint(mod7))

Waiting for profiling to be done...

# Extraer los p-values
p_values <- summary_mod7$coefficients[, 4]

# Crear el data frame consolidado
consolidado7 <- data.frame(Odds_Ratio = odds_ratios,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado
print(consolidado7)

NA