library(GLMsData); data(turbines)
turbines
## Hours Turbines Fissures
## 1 400 39 0
## 2 1000 53 4
## 3 1400 33 2
## 4 1800 73 7
## 5 2200 30 5
## 6 2600 39 9
## 7 3000 42 9
## 8 3400 13 6
## 9 3800 34 22
## 10 4200 40 21
## 11 4600 36 21
tur.m1 <- glm( Fissures/Turbines ~ Hours, family=binomial,
weights=Turbines, data=turbines)
tur.m2 <- glm( cbind(Fissures, Turbines-Fissures) ~ Hours,
family=binomial, data=turbines)
####
Load<-c(2500,2700,2900,3100,3300,3500,3700,3900,4100,4300)
sample<-c(50,70,100,60,40,85,90,50,80,65)
failling<-c(10,17,30,21,18,43,54,33,60,51)
lista<-cbind(Load,sample,failling)
datos<-as.data.frame(lista)
datos
## Load sample failling
## 1 2500 50 10
## 2 2700 70 17
## 3 2900 100 30
## 4 3100 60 21
## 5 3300 40 18
## 6 3500 85 43
## 7 3700 90 54
## 8 3900 50 33
## 9 4100 80 60
## 10 4300 65 51
attach(datos)
## The following objects are masked _by_ .GlobalEnv:
##
## failling, Load, sample
#pag 335
forma1 <- glm( failling/sample ~ Load, family=binomial(link = "logit"),
weights=sample, data=datos)
summary(forma1)
##
## Call:
## glm(formula = failling/sample ~ Load, family = binomial(link = "logit"),
## data = datos, weights = sample)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.29475 -0.11129 0.04162 0.08847 0.35016
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.3397115 0.5456932 -9.785 <2e-16 ***
## Load 0.0015484 0.0001575 9.829 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 112.83207 on 9 degrees of freedom
## Residual deviance: 0.37192 on 8 degrees of freedom
## AIC: 49.088
##
## Number of Fisher Scoring iterations: 3
forma2 <- glm( failling/sample ~ Load, family=binomial(link = "cloglog"),
weights=sample, data=datos)
forma3 <- glm( failling/sample ~ Load, family=binomial(link = "probit"),
weights=sample, data=datos,control=list(trace=TRUE) )
## Deviance = 0.43738 Iterations - 1
## Deviance = 0.4373459 Iterations - 2
## Deviance = 0.4373459 Iterations - 3
forma5<- glm( cbind(failling, sample-failling) ~ Load,
family=binomial, data=datos,control=list(trace=TRUE) )
## Deviance = 0.3720094 Iterations - 1
## Deviance = 0.3719169 Iterations - 2
## Deviance = 0.3719169 Iterations - 3
coef(forma1)
## (Intercept) Load
## -5.339711512 0.001548434
coef(forma5)
## (Intercept) Load
## -5.339711512 0.001548434
coef(forma2)
## (Intercept) Load
## -4.191745285 0.001094139
coef(forma3)
## (Intercept) Load
## -3.2712087961 0.0009488532
##
summary(forma1)
##
## Call:
## glm(formula = failling/sample ~ Load, family = binomial(link = "logit"),
## data = datos, weights = sample)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.29475 -0.11129 0.04162 0.08847 0.35016
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.3397115 0.5456932 -9.785 <2e-16 ***
## Load 0.0015484 0.0001575 9.829 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 112.83207 on 9 degrees of freedom
## Residual deviance: 0.37192 on 8 degrees of freedom
## AIC: 49.088
##
## Number of Fisher Scoring iterations: 3
summary(forma5)
##
## Call:
## glm(formula = cbind(failling, sample - failling) ~ Load, family = binomial,
## data = datos, control = list(trace = TRUE))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.29475 -0.11129 0.04162 0.08847 0.35016
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.3397115 0.5456932 -9.785 <2e-16 ***
## Load 0.0015484 0.0001575 9.829 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 112.83207 on 9 degrees of freedom
## Residual deviance: 0.37192 on 8 degrees of freedom
## AIC: 49.088
##
## Number of Fisher Scoring iterations: 3
coefficients(forma5)
## (Intercept) Load
## -5.339711512 0.001548434
confint(forma5)
## Waiting for profiling to be done...
## Deviance = 0.6190478 Iterations - 1
## Deviance = 0.6190477 Iterations - 2
## Deviance = 0.6190477 Iterations - 3
## Deviance = 1.350781 Iterations - 1
## Deviance = 1.350781 Iterations - 2
## Deviance = 1.350781 Iterations - 3
## Deviance = 2.552616 Iterations - 1
## Deviance = 2.552616 Iterations - 2
## Deviance = 2.552616 Iterations - 3
## Deviance = 4.210077 Iterations - 1
## Deviance = 4.210077 Iterations - 2
## Deviance = 4.210077 Iterations - 3
## Deviance = 6.308761 Iterations - 1
## Deviance = 6.30876 Iterations - 2
## Deviance = 6.30876 Iterations - 3
## Deviance = 8.834382 Iterations - 1
## Deviance = 8.834382 Iterations - 2
## Deviance = 8.834382 Iterations - 3
## Deviance = 0.6238617 Iterations - 1
## Deviance = 0.6238616 Iterations - 2
## Deviance = 0.6238616 Iterations - 3
## Deviance = 1.389269 Iterations - 1
## Deviance = 1.389269 Iterations - 2
## Deviance = 1.389269 Iterations - 3
## Deviance = 2.682377 Iterations - 1
## Deviance = 2.682377 Iterations - 2
## Deviance = 2.682377 Iterations - 3
## Deviance = 4.517212 Iterations - 1
## Deviance = 4.517212 Iterations - 2
## Deviance = 4.517212 Iterations - 3
## Deviance = 6.907517 Iterations - 1
## Deviance = 6.907517 Iterations - 2
## Deviance = 0.6238197 Iterations - 1
## Deviance = 0.6238196 Iterations - 2
## Deviance = 0.6238196 Iterations - 3
## Deviance = 1.388933 Iterations - 1
## Deviance = 1.388933 Iterations - 2
## Deviance = 1.388933 Iterations - 3
## Deviance = 2.681244 Iterations - 1
## Deviance = 2.681244 Iterations - 2
## Deviance = 2.681244 Iterations - 3
## Deviance = 4.514532 Iterations - 1
## Deviance = 4.514531 Iterations - 2
## Deviance = 4.514531 Iterations - 3
## Deviance = 6.902297 Iterations - 1
## Deviance = 6.902296 Iterations - 2
## Deviance = 0.6190895 Iterations - 1
## Deviance = 0.6190895 Iterations - 2
## Deviance = 0.6190895 Iterations - 3
## Deviance = 1.351115 Iterations - 1
## Deviance = 1.351114 Iterations - 2
## Deviance = 1.351114 Iterations - 3
## Deviance = 2.553737 Iterations - 1
## Deviance = 2.553737 Iterations - 2
## Deviance = 2.553737 Iterations - 3
## Deviance = 4.212721 Iterations - 1
## Deviance = 4.212721 Iterations - 2
## Deviance = 4.212721 Iterations - 3
## Deviance = 6.313897 Iterations - 1
## Deviance = 6.313897 Iterations - 2
## Deviance = 8.843204 Iterations - 1
## Deviance = 8.843204 Iterations - 2
## 2.5 % 97.5 %
## (Intercept) -6.430606344 -4.289373629
## Load 0.001245115 0.001863257
vcov(forma5)
## (Intercept) Load
## (Intercept) 2.977811e-01 -8.497023e-05
## Load -8.497023e-05 2.481820e-08
#
library(ResourceSelection)
## ResourceSelection 0.3-5 2019-07-22
hoslem.test(forma5$y, forma5$fitted)
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: forma5$y, forma5$fitted
## X-squared = 0.0055883, df = 8, p-value = 1
###3
forma1 <- glm( failling/sample ~ log(Load) + (log(Load))^2, family=binomial(link = "logit"),
weights=sample, data=datos , control = list(trace=TRUE))
## Deviance = 1.363322 Iterations - 1
## Deviance = 1.361951 Iterations - 2
## Deviance = 1.361951 Iterations - 3
vcov(forma1)
## (Intercept) log(Load)
## (Intercept) 18.568840 -2.2832959
## log(Load) -2.283296 0.2808665
summary(forma1)
##
## Call:
## glm(formula = failling/sample ~ log(Load) + (log(Load))^2, family = binomial(link = "logit"),
## data = datos, weights = sample, control = list(trace = TRUE))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.52946 -0.11592 -0.07591 0.28265 0.60107
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -42.111 4.309 -9.772 <2e-16 ***
## log(Load) 5.177 0.530 9.769 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 112.832 on 9 degrees of freedom
## Residual deviance: 1.362 on 8 degrees of freedom
## AIC: 50.078
##
## Number of Fisher Scoring iterations: 3
coefficients(forma1)
## (Intercept) log(Load)
## -42.110558 5.177246