Bom dia, estou rodando um experimento do tipo Box Behnken e usei a função reg e comparei com o obtido pela função rsm, o problema é que o que os dois consideram significativo difere, aonde posso estar errando? Testei também a função AIC para o modelo que ele fornece e dá diferente pela função AIC().

Ari Clecius Alves de Lima
Engenheiro Químico
Me. Engenharia Civil
(085)88412345
(085)33669042

#CMR

library(DoE.wrapper)

Design.1_2<- bbd.design(nfactors= 3 , ncenter= 4 ,randomize= FALSE,seed=7480,

factor.names=list( A=c(0,400),B=c(600,2200),C=c(1,10) ) )

res=c(73.450,75.725,63.980,67.670,76.070,77.345,66.005,67.335,84.740,81.595,74.830,70.325,72.460,73.170,73.925,73.800)

length(res)

#Medidas resumo

Design.1_2<- add.response(Design.1_2,res, replace=FALSE)

plancoded<- code.design(Design.1_2)

plancoded

LinearModel.1 <- aov(res ~(x1+x2+x3)^2+ I(x1^2) + I(x2^2) + I(x3^2),

data=plancoded)

sreg = step(LinearModel.1,direction="both")

#Melhor modelo

#Step: AIC=14.07

#res ~ x1 + x2 + x3 + I(x1^2) + I(x2^2) + I(x3^2)

# Df Sum of Sq RSS AIC

#<none> 16.074 14.074

#+ x1:x2 1 0.501 15.574 15.568

#+ x2:x3 1 0.462 15.612 15.607

#+ x1:x3 1 0.001 16.073 16.073

#- x1 1 9.181 25.255 19.303

#- I(x2^2) 1 9.310 25.384 19.385

#- I(x3^2) 1 36.195 52.269 30.941

#- x2 1 79.223 95.297 40.551

#- I(x1^2) 1 86.793 102.867 41.774

#- x3 1 212.747 228.821 54.566

summary(sreg)

# Df Sum Sq Mean Sq F value Pr(>F)

#x1 1 9.18 9.18 5.140 0.04959 *

#x2 1 79.22 79.22 44.357 9.27e-05 ***

#x3 1 212.75 212.75 119.118 1.72e-06 ***

#I(x1^2) 1 86.79 86.79 48.596 6.53e-05 ***

#I(x2^2) 1 9.31 9.31 5.213 0.04831 *

#I(x3^2) 1 36.20 36.20 20.266 0.00149 **

#Residuals 9 16.07 1.79

#---

#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Design.1_2.coded<-plancoded

rsmModel.2 <- rsm(res ~ FO(x1, x2, x3) + TWI(x1, x2, x3) + PQ(x1, x2, x3),

data=Design.1_2.coded)

summary(rsmModel.2)

# Estimate Std. Error t value Pr(>|t|)

#(Intercept) 73.33875 0.79347 92.4275 1.081e-10 ***

#x1 1.07125 0.56107 1.9093 0.104810

#x2 -3.14688 0.56107 -5.6087 0.001370 **

#x3 -5.15688 0.56107 -9.1911 9.350e-05 ***

#x1:x2 0.35375 0.79347 0.4458 0.671360

#x1:x3 0.01375 0.79347 0.0173 0.986736

#x2:x3 -0.34000 0.79347 -0.4285 0.683246

#x1^2 -4.65813 0.79347 -5.8705 0.001081 **

#x2^2 1.52563 0.79347 1.9227 0.102878

#x3^2 3.00813 0.79347 3.7911 0.009062 **

#---

#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Multiple R-squared: 0.9664, Adjusted R-squared: 0.916

#F-statistic: 19.17 on 9 and 6 DF, p-value: 0.0009315

LinearModel.2 <- aov(res ~ x1 + x2 + x3 + I(x1^2) + I(x2^2) + I(x3^2),

data=plancoded)

summary(LinearModel.2 )

AIC(LinearModel.2)

#[1] 61.47997

#valor obtido anteriormente Step: AIC=14.07