> library(rsm) > > tilapia.rsm1 <- rsm(mean_WBC ~ SO(temp,protein)) > summary(tilapia.rsm1) Call: rsm(formula = mean_WBC ~ SO(temp, protein)) Residuals: Min 1Q Median 3Q Max -12.238 -9.668 -5.398 10.541 14.683 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -615.12410 248.45437 -2.476 0.04247 * temp 47.20166 12.95047 3.645 0.00823 ** protein 8.89176 6.58767 1.350 0.21911 temp:protein -0.05245 0.15830 -0.331 0.75010 temp^2 -0.75344 0.21237 -3.548 0.00937 ** protein^2 -0.09036 0.06644 -1.360 0.21602 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.65 on 7 degrees of freedom Multiple R-squared: 0.8396, Adjusted R-squared: 0.7251 F-statistic: 7.33 on 5 and 7 DF, p-value: 0.01050 Analysis of Variance Table Response: mean_WBC Df Sum Sq Mean Sq F value Pr(>F) FO(temp, protein) 2 4319.9 2159.94 11.5900 0.006009 TWI(temp, protein) 1 20.5 20.46 0.1098 0.750101 PQ(temp, protein) 2 2490.2 1245.08 6.6809 0.023822 Residuals 7 1304.5 186.36 Lack of fit 3 708.9 236.29 1.5868 0.325086 Pure error 4 595.7 148.92 Stationary point of response surface: temp protein 29.91379 40.52278 Eigenanalysis: $values [1] -0.08931963 -0.75447368 $vectors [,1] [,2] [1,] 0.03945517 -0.99922134 [2,] -0.99922134 -0.03945517