Pembangkitan Sebaran Normal
set.seed(2702)
data <- rnorm(n = 10000, mean = 12, sd = 5)
hist(data, nclass=50)
## Diduga sebagai sebaran Normal
dugaan <- maxlogL(x = data, dist = 'dnorm', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: Hessian from optim
## _______________________________________________________________
## AIC BIC
## 60568.56 60582.98
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## mean 12.05697 0.04999 241.2 <2e-16 ***
## sd 4.99926 0.03535 141.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---
## Diduga sebagai sebaran Gamma, bandingkan nilai AIC atau BIC-nya
dugaan <- maxlogL(x = data, dist = 'dgamma', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: 'optim' failed
## _______________________________________________________________
## AIC BIC
## Inf Inf
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## shape 1 NA NA NA
## rate 1 NA NA NA
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---
Pembangkitan Sebaran Gamma
set.seed(2702)
data <- rgamma(n = 10000, shape = 5, rate = 10)
hist(data, nclass=50)
## Diduga sebagai sebaran Gamma
dugaan <- maxlogL(x = data, dist = 'dgamma', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: Hessian from optim
## _______________________________________________________________
## AIC BIC
## -3081.395 -3066.974
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## shape 5.08157 0.06963 72.98 <2e-16 ***
## rate 10.14644 0.14614 69.43 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---
## Diduga sebagai sebaran Normal, bandingkan nilai AIC atau BIC-nya
dugaan <- maxlogL(x = data, dist = 'dnorm', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: Hessian from optim
## _______________________________________________________________
## AIC BIC
## -1725.874 -1711.454
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## mean 0.500822 0.002219 225.7 <2e-16 ***
## sd 0.221921 0.001569 141.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---
Pembangkitan Sebaran Beta
set.seed(2702)
data <- rbeta(n = 10000, shape1 = 15, shape2 = 9, ncp = 0)
hist(data, nclass=50)
## Diduga sebagai sebaran Beta
dugaan <- maxlogL(x = data, dist = 'dbeta', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: Hessian from optim
## _______________________________________________________________
## AIC BIC
## -18529.39 -18522.18
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## shape1 15.1782 0.2136 71.05 <2e-16 ***
## shape2 9.0589 0.1261 71.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---
## Diduga sebagai sebaran Gamma, bandingkan nilai AIC atau BIC-nya
dugaan <- maxlogL(x = data, dist = 'dgamma', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: Hessian from optim
## _______________________________________________________________
## AIC BIC
## -18065.46 -18051.04
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## shape 40.1257 0.5651 71.00 <2e-16 ***
## rate 64.0755 0.9081 70.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---
## Diduga sebagai sebaran Normal, bandingkan nilai AIC atau BIC-nya
dugaan <- maxlogL(x = data, dist = 'dnorm', start=c(1, 1), optimizer = 'nlminb')
summary(dugaan)
## _______________________________________________________________
## Optimization routine: nlminb
## Standard Error calculation: Hessian from optim
## _______________________________________________________________
## AIC BIC
## -18429.13 -18414.71
## _______________________________________________________________
## Estimate Std. Error Z value Pr(>|z|)
## mean 0.6262262 0.0009627 650.5 <2e-16 ***
## sd 0.0962703 0.0006804 141.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## _______________________________________________________________
## Note: p-values valid under asymptotic normality of estimators
## ---