update Examples for new SSmodel examples

Author: jarioksa

tests/Examples/vegan-Ex.Rout.save

@@ -411,6 +411,22 @@ Nonlinear regression model
 
 Number of iterations to convergence: 5 
 Achieved convergence tolerance: 1.056e-06
+> summary(marr)
+
+Formula: S ~ SSarrhenius(area, k, z)
+
+Parameters:
+  Estimate Std. Error t value Pr(>|t|)    
+k  3.40619    0.40790   8.351 3.16e-07 ***
+z  0.43644    0.02743  15.910 3.15e-11 ***
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 2.209 on 16 degrees of freedom
+
+Number of iterations to convergence: 5 
+Achieved convergence tolerance: 1.056e-06
+
 > ## confidence limits from profile likelihood
 > confint(marr)
 Waiting for profiling to be done...
@@ -423,14 +439,25 @@ z 0.3813576 0.4944693
 > ## The normal way is to use linear regression on log-log data,
 > ## but this will be different from the previous:
 > mloglog <- lm(log(S) ~ log(area), data=sipoo.map)
-> mloglog
+> summary(mloglog) # (Intercept) is log(k) of SSarrhenius result
 
 Call:
 lm(formula = log(S) ~ log(area), data = sipoo.map)
 
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.87519 -0.11897  0.05503  0.19842  0.40087 
+
 Coefficients:
-(Intercept)    log(area)  
-     1.0111       0.4925  
+            Estimate Std. Error t value Pr(>|t|)    
+(Intercept)  1.01109    0.14897   6.787 4.36e-06 ***
+log(area)    0.49252    0.05565   8.850 1.46e-07 ***
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 0.3163 on 16 degrees of freedom
+Multiple R-squared:  0.8304,	Adjusted R-squared:  0.8198 
+F-statistic: 78.32 on 1 and 16 DF,  p-value: 1.462e-07
 
 > lines(xtmp, exp(predict(mloglog, newdata=data.frame(area=xtmp))),
 +    lty=2)
@@ -444,16 +471,23 @@ Coefficients:
 +   lwd=2, col = 3)
 > ## Lomolino: using original names of the parameters (Lomolino 2000):
 > mlom <- nls(S ~ SSlomolino(area, Smax, A50, Hill), sipoo.map)
-> mlom
-Nonlinear regression model
-  model: S ~ SSlomolino(area, Smax, A50, Hill)
-   data: sipoo.map
-  Smax    A50   Hill 
-53.493 94.697  2.018 
- residual sum-of-squares: 55.37
+> summary(mlom)
+
+Formula: S ~ SSlomolino(area, Smax, A50, Hill)
+
+Parameters:
+     Estimate Std. Error t value Pr(>|t|)    
+Smax   53.493     13.849   3.863  0.00153 ** 
+A50    94.697     74.964   1.263  0.22578    
+Hill    2.018      0.235   8.587 3.56e-07 ***
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 1.921 on 15 degrees of freedom
 
 Number of iterations to convergence: 6 
 Achieved convergence tolerance: 9.715e-07
+
 > lines(xtmp, predict(mlom, newdata=data.frame(area=xtmp)),
 +   lwd=2, col = 4)
 > ## One canned model of standard R:
@@ -9177,7 +9211,7 @@ Procrustes sum of squares:
 > cleanEx()
 > options(digits = 7L)
 > base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed:  8.784 0.398 9.198 0 0 
+Time elapsed:  8.8 0.411 9.234 0 0 
 > grDevices::dev.off()
 null device 
           1