Ovidiu
Nomogramas
Mejores “p” con: BMI, CTdiameter y TumorSolido
Pero mejores AUC y confusion matrix con:
BMI, CharlsonIndex, CTdiameter y TumorSolido
Voy a usar Diameter = CTDiameter (pero los valores superiores a 6 lo dejo en 6)
d <- datadist(df)
options(datadist='d')
f<-lrm(Trifecta ~ BMI+ Diameter + TumorSolido,data=df)
summary(f)## Effects Response : Trifecta
##
## Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
## BMI 24.8 30.2 5.4 -0.80635 0.38460 -1.560200 -0.052537
## Odds Ratio 24.8 30.2 5.4 0.44649 NA 0.210100 0.948820
## Diameter 1.9 3.2 1.3 -0.70091 0.30931 -1.307100 -0.094681
## Odds Ratio 1.9 3.2 1.3 0.49613 NA 0.270590 0.909660
## TumorSolido - 0:1 2.0 1.0 NA 1.18850 0.64884 -0.083205 2.460200
## Odds Ratio 2.0 1.0 NA 3.28210 NA 0.920160 11.707000summary(f,BMI=c(20,40),Diameter=c(0,5)) ## Effects Response : Trifecta
##
## Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
## BMI 20 40 20 -2.986500 1.42450 -5.7783000 -0.19458
## Odds Ratio 20 40 20 0.050465 NA 0.0030938 0.82318
## Diameter 0 5 5 -2.695800 1.18960 -5.0275000 -0.36416
## Odds Ratio 0 5 5 0.067488 NA 0.0065554 0.69478
## TumorSolido - 0:1 2 1 NA 1.188500 0.64884 -0.0832050 2.46020
## Odds Ratio 2 1 NA 3.282100 NA 0.9201600 11.70700plot(nomogram(f,fun=plogis,funlabel="Probability of Trifecta"
#, fun.at=c(.05,.1,.25,.5,.75,.9,.95), #Perdidas1Mes=c(0,5,10,20,seq(50,700,by=50))
))
d <- datadist(df)
options(datadist='d')
f<-lrm(Trifecta ~ BMI+ CharlsonIndex+ Diameter + TumorSolido,data=df)
summary(f)## Effects Response : Trifecta
##
## Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
## BMI 24.8 30.2 5.4 -0.95999 0.41555 -1.77450 -0.145530
## Odds Ratio 24.8 30.2 5.4 0.38290 NA 0.16958 0.864570
## CharlsonIndex 4.0 6.0 2.0 -0.61143 0.36051 -1.31800 0.095157
## Odds Ratio 4.0 6.0 2.0 0.54258 NA 0.26767 1.099800
## Diameter 1.9 3.2 1.3 -0.58146 0.32246 -1.21350 0.050555
## Odds Ratio 1.9 3.2 1.3 0.55908 NA 0.29716 1.051900
## TumorSolido - 0:1 2.0 1.0 NA 1.13450 0.65985 -0.15878 2.427800
## Odds Ratio 2.0 1.0 NA 3.10960 NA 0.85318 11.334000summary(f,BMI=c(20,40),Diameter=c(0,5)) ## Effects Response : Trifecta
##
## Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
## BMI 20 40 20 -3.555500 1.53910 -6.5720000 -0.538990
## Odds Ratio 20 40 20 0.028567 NA 0.0013989 0.583340
## CharlsonIndex 4 6 2 -0.611430 0.36051 -1.3180000 0.095157
## Odds Ratio 4 6 2 0.542580 NA 0.2676700 1.099800
## Diameter 0 5 5 -2.236400 1.24020 -4.6672000 0.194440
## Odds Ratio 0 5 5 0.106840 NA 0.0093985 1.214600
## TumorSolido - 0:1 2 1 NA 1.134500 0.65985 -0.1587800 2.427800
## Odds Ratio 2 1 NA 3.109600 NA 0.8531800 11.334000plot(nomogram(f,fun=plogis,funlabel="Probability of Trifecta"
#, fun.at=c(.05,.1,.25,.5,.75,.9,.95), #Perdidas1Mes=c(0,5,10,20,seq(50,700,by=50))
))
Mejores modelos para ComplicationYN, sin usar interacciones. Búsqueda exhaustiva.
Voy a comenzar explorando todos los factores principales y pidiendo una lista de los 5 mejores modelos. Quito CRput por tener muchos missing
glmulti.lm.out <-
glmulti(ComplicationYN ~Sex+ BMI+ CharlsonIndex + TumorSolido+ CTdiameter+RENALscore+OperatorAndTys+Bleeding+ClampingTime+Dren +Handport,
data = df,
family = binomial,
level = 1, # No interaction considered
method = "h", # Exhaustive approach
crit = "aic", # AIC as criteria
confsetsize = 5, # Keep 5 best models
plotty = F, report = F, # No plot or interim reports
)
glmulti.lm.out@formulas## [[1]]
## ComplicationYN ~ 1 + CharlsonIndex + Dren
## <environment: 0x0000000069a82808>
##
## [[2]]
## ComplicationYN ~ 1 + CharlsonIndex + ClampingTime + Dren
## <environment: 0x0000000069a82808>
##
## [[3]]
## ComplicationYN ~ 1 + CharlsonIndex + CTdiameter + Dren
## <environment: 0x0000000069a82808>
##
## [[4]]
## ComplicationYN ~ 1 + CharlsonIndex + RENALscore + Dren
## <environment: 0x0000000069a82808>
##
## [[5]]
## ComplicationYN ~ 1 + Sex + CharlsonIndex + Dren
## <environment: 0x0000000069a82808>El resumen de estos mejores modelos es:
summary(glm(glmulti.lm.out@formulas[[1]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[1]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4505 -0.8479 -0.4529 0.1006 2.5452
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.1461 1.4602 -3.524 0.000425 ***
## CharlsonIndex 0.4868 0.2008 2.424 0.015330 *
## Dren 1.8743 1.0807 1.734 0.082860 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 89.974 on 79 degrees of freedom
## Residual deviance: 77.338 on 77 degrees of freedom
## AIC: 83.338
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[2]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[2]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.84886 -0.76896 -0.48080 0.06678 2.59081
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.83709 1.62758 -3.586 0.000335 ***
## CharlsonIndex 0.49094 0.20629 2.380 0.017320 *
## ClampingTime 0.05527 0.04742 1.166 0.243794
## Dren 1.76099 1.08856 1.618 0.105724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 89.974 on 79 degrees of freedom
## Residual deviance: 75.942 on 76 degrees of freedom
## AIC: 83.942
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[3]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[3]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.36855 -0.75675 -0.47277 -0.08555 2.57268
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.2891 1.5068 -3.510 0.000448 ***
## CharlsonIndex 0.4344 0.2050 2.119 0.034081 *
## CTdiameter 0.2150 0.2333 0.922 0.356770
## Dren 1.6080 1.1148 1.442 0.149178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 89.974 on 79 degrees of freedom
## Residual deviance: 75.985 on 76 degrees of freedom
## AIC: 83.985
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[4]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[4]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.64734 -0.79296 -0.48493 0.09529 2.57356
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.2080 1.8426 -3.369 0.000754 ***
## CharlsonIndex 0.4915 0.2067 2.378 0.017418 *
## RENALscore 0.1935 0.1826 1.060 0.289284
## Dren 1.5937 1.1131 1.432 0.152231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 89.974 on 79 degrees of freedom
## Residual deviance: 76.189 on 76 degrees of freedom
## AIC: 84.189
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[5]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[5]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6471 -0.7875 -0.4198 0.1294 2.4056
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.9866 1.7526 -3.416 0.000636 ***
## Sex 0.5841 0.6076 0.961 0.336448
## CharlsonIndex 0.4955 0.2025 2.447 0.014400 *
## Dren 1.9133 1.0898 1.756 0.079136 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 89.974 on 79 degrees of freedom
## Residual deviance: 76.425 on 76 degrees of freedom
## AIC: 84.425
##
## Number of Fisher Scoring iterations: 5Lo mismo, pero usando como variable dependiente ClavienDindoGrade2
glmulti.lm.out <-
glmulti(ClavienDindoGrade2 ~Sex+ BMI+ CharlsonIndex + TumorSolido+ CTdiameter+RENALscore+OperatorAndTys+Bleeding+ClampingTime+Dren +Handport,
data = df,
family = binomial,
level = 1, # No interaction considered
method = "h", # Exhaustive approach
crit = "aic", # AIC as criteria
confsetsize = 5, # Keep 5 best models
plotty = F, report = F, # No plot or interim reports
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredglmulti.lm.out@formulas## [[1]]
## ClavienDindoGrade2 ~ 1 + CharlsonIndex + ClampingTime + Handport
## <environment: 0x00000000646ea8b0>
##
## [[2]]
## ClavienDindoGrade2 ~ 1 + CharlsonIndex + RENALscore + ClampingTime +
## Handport
## <environment: 0x00000000646ea8b0>
##
## [[3]]
## ClavienDindoGrade2 ~ 1 + CharlsonIndex + ClampingTime + Dren +
## Handport
## <environment: 0x00000000646ea8b0>
##
## [[4]]
## ClavienDindoGrade2 ~ 1 + ClampingTime + Handport
## <environment: 0x00000000646ea8b0>
##
## [[5]]
## ClavienDindoGrade2 ~ 1 + RENALscore + ClampingTime + Handport
## <environment: 0x00000000646ea8b0>El resumen de estos mejores modelos es:
summary(glm(glmulti.lm.out@formulas[[1]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[1]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.12863 -0.53718 -0.35920 -0.00005 2.07942
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -24.08443 2162.37701 -0.011 0.9911
## CharlsonIndex 0.40783 0.24959 1.634 0.1023
## ClampingTime 0.14861 0.06495 2.288 0.0221 *
## Handport 18.51948 2162.37607 0.009 0.9932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.633 on 79 degrees of freedom
## Residual deviance: 50.331 on 76 degrees of freedom
## AIC: 58.331
##
## Number of Fisher Scoring iterations: 18summary(glm(glmulti.lm.out@formulas[[2]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[2]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.18227 -0.51141 -0.32456 -0.00004 2.00647
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -25.32316 2116.69043 -0.012 0.9905
## CharlsonIndex 0.39556 0.26348 1.501 0.1333
## RENALscore 0.29045 0.26736 1.086 0.2773
## ClampingTime 0.12327 0.06868 1.795 0.0727 .
## Handport 18.13051 2116.68897 0.009 0.9932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.633 on 79 degrees of freedom
## Residual deviance: 49.090 on 75 degrees of freedom
## AIC: 59.09
##
## Number of Fisher Scoring iterations: 18summary(glm(glmulti.lm.out@formulas[[3]],data=df,family=binomial))## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred##
## Call:
## glm(formula = glmulti.lm.out@formulas[[3]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0923 -0.5340 -0.3564 0.0000 2.0260
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -41.22232 4706.11935 -0.009 0.993
## CharlsonIndex 0.40673 0.24913 1.633 0.103
## ClampingTime 0.13824 0.06484 2.132 0.033 *
## Dren 16.92883 3396.98652 0.005 0.996
## Handport 18.96254 3256.99844 0.006 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.633 on 79 degrees of freedom
## Residual deviance: 49.102 on 75 degrees of freedom
## AIC: 59.102
##
## Number of Fisher Scoring iterations: 19summary(glm(glmulti.lm.out@formulas[[4]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[4]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.59972 -0.55666 -0.41771 -0.00005 2.10108
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -22.05667 2230.23253 -0.010 0.9921
## ClampingTime 0.15222 0.06293 2.419 0.0156 *
## Handport 18.44378 2230.23214 0.008 0.9934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.633 on 79 degrees of freedom
## Residual deviance: 53.359 on 77 degrees of freedom
## AIC: 59.359
##
## Number of Fisher Scoring iterations: 18summary(glm(glmulti.lm.out@formulas[[5]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[5]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.70974 -0.56704 -0.37078 -0.00004 2.03530
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -23.56600 2162.55295 -0.011 0.9913
## RENALscore 0.31607 0.24501 1.290 0.1971
## ClampingTime 0.12860 0.06552 1.963 0.0497 *
## Handport 18.06133 2162.55208 0.008 0.9933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.633 on 79 degrees of freedom
## Residual deviance: 51.588 on 76 degrees of freedom
## AIC: 59.588
##
## Number of Fisher Scoring iterations: 18Mejores modelos para Trifecta
glmulti.lm.out <-
glmulti(Trifecta ~ BMI+ CharlsonIndex +TumorSolido + CTdiameter + RENALscore + OperatorAndTys + Handport,
data = df,
family = binomial,
level = 1, # No interaction considered
method = "h", # Exhaustive approach
crit = "aic", # AIC as criteria
confsetsize = 5, # Keep 5 best models
plotty = F, report = F, # No plot or interim reports
)
glmulti.lm.out@formulas## [[1]]
## Trifecta ~ 1 + TumorSolido + BMI + CharlsonIndex + CTdiameter
## <environment: 0x00000000623bb0e0>
##
## [[2]]
## Trifecta ~ 1 + TumorSolido + BMI + CTdiameter
## <environment: 0x00000000623bb0e0>
##
## [[3]]
## Trifecta ~ 1 + BMI + CharlsonIndex + CTdiameter
## <environment: 0x00000000623bb0e0>
##
## [[4]]
## Trifecta ~ 1 + TumorSolido + BMI + CharlsonIndex + CTdiameter +
## RENALscore
## <environment: 0x00000000623bb0e0>
##
## [[5]]
## Trifecta ~ 1 + TumorSolido + BMI + CharlsonIndex
## <environment: 0x00000000623bb0e0>El resumen de estos mejores modelos es:
summary(glm(glmulti.lm.out@formulas[[1]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[1]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1838 -0.8874 0.5752 0.8933 1.6815
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.67500 2.55868 3.390 0.000698 ***
## TumorSolido1 -1.18254 0.67075 -1.763 0.077899 .
## BMI -0.17204 0.07714 -2.230 0.025731 *
## CharlsonIndex -0.28816 0.18263 -1.578 0.114617
## CTdiameter -0.42274 0.25165 -1.680 0.092989 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 106.819 on 79 degrees of freedom
## Residual deviance: 86.917 on 75 degrees of freedom
## AIC: 96.917
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[2]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[2]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2050 -0.9235 0.5209 0.9080 1.6055
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.76415 2.13638 3.166 0.00154 **
## TumorSolido1 -1.27010 0.66498 -1.910 0.05614 .
## BMI -0.14311 0.07123 -2.009 0.04453 *
## CTdiameter -0.48669 0.24659 -1.974 0.04842 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 106.819 on 79 degrees of freedom
## Residual deviance: 89.541 on 76 degrees of freedom
## AIC: 97.541
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[3]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[3]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1185 -0.9532 0.5908 0.8800 1.7007
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.65716 2.50946 3.450 0.000561 ***
## BMI -0.20561 0.07402 -2.778 0.005474 **
## CharlsonIndex -0.31520 0.17913 -1.760 0.078467 .
## CTdiameter -0.35434 0.23962 -1.479 0.139201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 106.819 on 79 degrees of freedom
## Residual deviance: 90.411 on 76 degrees of freedom
## AIC: 98.411
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[4]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[4]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2864 -0.8861 0.5107 0.9119 1.5844
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.22122 2.73045 3.377 0.000732 ***
## TumorSolido1 -1.17804 0.66862 -1.762 0.078086 .
## BMI -0.17387 0.07762 -2.240 0.025082 *
## CharlsonIndex -0.27954 0.18540 -1.508 0.131606
## CTdiameter -0.34974 0.26923 -1.299 0.193930
## RENALscore -0.11624 0.17157 -0.678 0.498071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 106.819 on 79 degrees of freedom
## Residual deviance: 86.457 on 74 degrees of freedom
## AIC: 98.457
##
## Number of Fisher Scoring iterations: 5summary(glm(glmulti.lm.out@formulas[[5]],data=df,family=binomial))##
## Call:
## glm(formula = glmulti.lm.out@formulas[[5]], family = binomial,
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1048 -1.0359 0.6037 0.8777 2.0074
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.41921 2.46081 3.421 0.000623 ***
## TumorSolido1 -0.91737 0.61097 -1.501 0.133227
## BMI -0.19762 0.07513 -2.630 0.008531 **
## CharlsonIndex -0.37923 0.17524 -2.164 0.030456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 106.819 on 79 degrees of freedom
## Residual deviance: 90.633 on 76 degrees of freedom
## AIC: 98.633
##
## Number of Fisher Scoring iterations: 4Reservado para estudiar interacciones. De momento no hacemos nada
Cualquiera de esos modelos se puede defender si se encuentra una idea.
Hay modelos que son más complicados de explicar, pero que puede ser conveniente explorar. Aquellos donde hay interacciones entre pares de variables. Podrían decir que eso es ir de pesca, y sin justificación, la verdad es que algo de razón tienen. Pero bueno, ahora estamos explorando a ver qué ideas surgen, así que vamos a ello, pero limitándonos a las variales que han aparecido en la etapa anterior
ComplicationYN ~ Sex + BMI + CharlsonIndex + RENALscore + Age+ClampingTime
glmulti.lm.out@formulas## [[1]]
## Trifecta ~ 1 + Diameter + Diameter:BMI + Diameter:CharlsonIndex +
## TumorSolido:Diameter
## <environment: 0x000000007db91ce8>
##
## [[2]]
## Trifecta ~ 1 + CharlsonIndex + Diameter + Diameter:BMI + TumorSolido:Diameter
## <environment: 0x000000007db91ce8>
##
## [[3]]
## Trifecta ~ 1 + CharlsonIndex + Diameter + Diameter:BMI + TumorSolido:CharlsonIndex
## <environment: 0x000000007db91ce8>
##
## [[4]]
## Trifecta ~ 1 + BMI + Diameter + Diameter:BMI + Diameter:CharlsonIndex +
## TumorSolido:Diameter
## <environment: 0x000000007db91ce8>
##
## [[5]]
## Trifecta ~ 1 + BMI + Diameter + CharlsonIndex:BMI + Diameter:BMI +
## TumorSolido:Diameter
## <environment: 0x000000007db91ce8>