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library(rsm)
## Warning: package 'rsm' was built under R version 3.1.2
library(rgl)
## Warning: package 'rgl' was built under R version 3.1.2
remove(list=ls())
listeria = read.csv("C:/Users/Trevor/Documents/listeria.csv", header=TRUE)
attach(listeria)
model1=rsm(Listeria.count~FO(Herbal.Tea,Concentration,Time), data=listeria)
summary(model1)
##
## Call:
## rsm(formula = Listeria.count ~ FO(Herbal.Tea, Concentration,
## Time), data = listeria)
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.5006 0.0978 66.46 <2e-16 ***
## Herbal.Tea 0.0760 0.0437 1.74 0.0932 .
## Concentration -0.0687 0.0196 -3.51 0.0015 **
## Time 0.0341 0.0196 1.75 0.0918 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Multiple R-squared: 0.397, Adjusted R-squared: 0.332
## F-statistic: 6.13 on 3 and 28 DF, p-value: 0.00243
##
## Analysis of Variance Table
##
## Response: Listeria.count
## Df Sum Sq Mean Sq F value Pr(>F)
## FO(Herbal.Tea, Concentration, Time) 3 0.282 0.0939 6.13 0.0024
## Residuals 28 0.429 0.0153
## Lack of fit 28 0.429 0.0153
## Pure error 0 0.000
##
## Direction of steepest ascent (at radius 1):
## Herbal.Tea Concentration Time
## 0.7038 -0.6361 0.3162
##
## Corresponding increment in original units:
## Herbal.Tea Concentration Time
## 0.7038 -0.6361 0.3162
model4=rsm(Listeria.count~FO(Herbal.Tea,Concentration,Time)
+ TWI(Herbal.Tea,Concentration,Time), data=listeria)
summary(model4)
##
## Call:
## rsm(formula = Listeria.count ~ FO(Herbal.Tea, Concentration,
## Time) + TWI(Herbal.Tea, Concentration, Time), data = listeria)
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.80515 0.25560 26.62 <2e-16 ***
## Herbal.Tea -0.14807 0.14592 -1.01 0.32
## Concentration -0.12184 0.07620 -1.60 0.12
## Time -0.02188 0.07620 -0.29 0.78
## Herbal.Tea:Concentration 0.04385 0.03935 1.11 0.28
## Herbal.Tea:Time 0.04578 0.03935 1.16 0.26
## Concentration:Time -0.00506 0.01760 -0.29 0.78
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Multiple R-squared: 0.455, Adjusted R-squared: 0.324
## F-statistic: 3.48 on 6 and 25 DF, p-value: 0.0123
##
## Analysis of Variance Table
##
## Response: Listeria.count
## Df Sum Sq Mean Sq F value Pr(>F)
## FO(Herbal.Tea, Concentration, Time) 3 0.282 0.0939 6.06 0.003
## TWI(Herbal.Tea, Concentration, Time) 3 0.041 0.0138 0.89 0.459
## Residuals 25 0.387 0.0155
## Lack of fit 25 0.387 0.0155
## Pure error 0 0.000
##
## Stationary point of response surface:
## Herbal.Tea Concentration Time
## 1.815 12.102 -8.358
##
## Eigenanalysis:
## $values
## [1] 0.030460 0.002526 -0.032986
##
## $vectors
## [,1] [,2] [,3]
## Herbal.Tea 0.7210 -0.003473 0.6929
## Concentration 0.4774 -0.722333 -0.5004
## Time 0.5022 0.691537 -0.5192
par(mfrow=c(2,2))
contour(model4, ~Herbal.Tea+Concentration+Time, image=TRUE,
at=summary(model4$canonical$xs))
par(mfrow=c(1,1))
persp(model4, ~ Herbal.Tea+Concentration, image = TRUE,
at = c(summary(model4)$canonical$xs, Block="B2"),
theta=30,zlab="Listeria Count",col.lab=33,contour="colors")
## Warning: "image" is not a graphical parameter
## Warning: "image" is not a graphical parameter
## Warning: "image" is not a graphical parameter
persp(model4, ~ Herbal.Tea+Time, image = TRUE,
at = c(summary(model4)$canonical$xs, Block="B2"),
theta=-30,zlab="Listeria Count",col.lab=33,contour="colors")
## Warning: "image" is not a graphical parameter
## Warning: "image" is not a graphical parameter
## Warning: "image" is not a graphical parameter
persp(model4, ~ Time+Concentration, image = TRUE,
at = c(summary(model4)$canonical$xs, Block="B2"),
theta=130,zlab="Listeria Count",col.lab=33,contour="colors")
## Warning: "image" is not a graphical parameter
## Warning: "image" is not a graphical parameter
## Warning: "image" is not a graphical parameter
