S3 method for class 'sars'. summary.sars creates
summary statistics for objects of class 'sars'. The exact summary
statistics computed depends on the 'Type' attribute (e.g. 'multi') of
the 'sars' object. The summary method generates more useful information
for the user than the standard model fitting functions. Another S3
method (print.summary.sars; not documented) is used to print the
output.
# S3 method for sars summary(object, ...)
| object | An object of class 'sars'. |
|---|---|
| … | Further arguments. |
The summary.sars function returns an object of class
"summary.sars". A print function is used to obtain and print a summary
of the model fit results.
For a 'sars' object of Type 'fit', a list with 16 elements is returned that contains useful information from the model fit, including the model parameter table (with t-values, p-values and confidence intervals), model fit statistics (e.g. R2, AIC), the observed shape of the model and whether or not the fit is asymptotic, and the results of any additional model checks undertaken (e.g. normality of the residuals).
For a 'sars' object of Type 'multi', a list with 4 elements is returned:
(i) a vector of the names of the models that were successfully fitted
and passed any additional checks, (ii) a character string containing the
name of the criterion used to rank models, (iii) a data frame of the
ranked models, and (iv) a vector of the names of any models that were
not fitted or did not pass any additional checks. In regards to (iii;
Model_table), the dataframe contains the fit summaries for each
successfully fitted model (including the value of the model criterion
used to compare models, the R2 and adjusted R2, and the observed shape
of the fit); the models are ranked in decreasing order of information
criterion weight.
For a 'sars' object of Type 'lin_pow', a list with up to 7 elements is
returned: (i) the model fit output from the lm function, (ii)
the fitted values of the model, (iii) the observed data, (iv and v) the
results of the residuals normality and heterogeneity tests, and (vi) the
log-transformation function used. If the argument compare = TRUE is
used in lin_pow, a 7th element is returned that contains the
parameter values from the non-linear power model.
#> #> Now attempting to fit the 20 SAR models: #> #> -- multi_sars --------------------------- multi-model SAR -- #> > power : v #> > powerR : v #> > epm1 : v #> > epm2 : v #> > p1 : v #> > p2 : v #> > loga : v #> > koba : v #> > mmf : v #> > monod : v #> > negexpo : v #> > chapman : Warning: could not compute parameters statistics #> > weibull3 : v #> > asymp : v #> > ratio : v #> > gompertz : v #> > weibull4 : v #> > betap : v #> > heleg : v #> > linear : v #> #> Model fitting completed - all models succesfully fitted. Now undertaking model validation checks. #> Additional models will be excluded if necessary:#> #> #>#> 16 remaining models used to construct the multi SAR: #> Power, PowerR, Extended Power model 2, Persistence function 1, Persistence function 2, Logarithmic, Kobayashi, MMF, Monod, Negative exponential, Chapman Richards, Cumulative Weibull 3 par., Rational function, Gompertz, Beta-P cumulative, Heleg(Logistic) #> -------------------------------------------------------------summary(mf)#> #> Sar_average object summary: #> #> 16 models successfully fitted #> #> The following models could not be fitted or were removed due to model checks: #> Extended Power model 1, Asymptotic regression, Cumulative Weibull 4 par., Linear model #> #> Ranked models based on AICc weights: #> #> Model Weight AICc R2 R2a Shape Asymptote #> 1 negexpo 0.219 186.832 0.557 0.488 convex up TRUE #> 2 monod 0.178 187.246 0.545 0.475 convex up TRUE #> 3 koba 0.118 188.073 0.521 0.447 convex up FALSE #> 4 power 0.073 189.031 0.491 0.413 convex up FALSE #> 5 loga 0.067 189.191 0.486 0.407 convex up FALSE #> 6 p1 0.066 189.246 0.589 0.486 convex up FALSE #> 7 gompertz 0.061 189.393 0.585 0.482 convex up TRUE #> 8 weibull3 0.038 190.324 0.561 0.451 convex up TRUE #> 9 chapman 0.036 190.468 0.557 0.446 convex up TRUE #> 10 ratio 0.035 190.494 0.556 0.445 convex up TRUE #> 11 heleg 0.029 190.882 0.545 0.431 convex up TRUE #> 12 mmf 0.029 190.882 0.545 0.431 convex up TRUE #> 13 p2 0.019 191.712 0.521 0.401 convex up FALSE #> 14 powerR 0.014 192.283 0.503 0.379 convex up FALSE #> 15 epm2 0.014 192.329 0.502 0.377 sigmoid FALSE #> 16 betap 0.002 196.018 0.522 0.349 convex up FALSE#> Model Weight AICc R2 R2a Shape Asymptote #> 1 negexpo 0.219 186.832 0.557 0.488 convex up TRUE #> 2 monod 0.178 187.246 0.545 0.475 convex up TRUE #> 3 koba 0.118 188.073 0.521 0.447 convex up FALSE #> 4 power 0.073 189.031 0.491 0.413 convex up FALSE #> 5 loga 0.067 189.191 0.486 0.407 convex up FALSE #> 6 p1 0.066 189.246 0.589 0.486 convex up FALSE #> 7 gompertz 0.061 189.393 0.585 0.482 convex up TRUE #> 8 weibull3 0.038 190.324 0.561 0.451 convex up TRUE #> 9 chapman 0.036 190.468 0.557 0.446 convex up TRUE #> 10 ratio 0.035 190.494 0.556 0.445 convex up TRUE #> 11 heleg 0.029 190.882 0.545 0.431 convex up TRUE #> 12 mmf 0.029 190.882 0.545 0.431 convex up TRUE #> 13 p2 0.019 191.712 0.521 0.401 convex up FALSE #> 14 powerR 0.014 192.283 0.503 0.379 convex up FALSE #> 15 epm2 0.014 192.329 0.502 0.377 sigmoid FALSE #> 16 betap 0.002 196.018 0.522 0.349 convex up FALSE#Get a summary of the fit of the linear power model fit <- lin_pow(galap, con = 1, compare = TRUE) summary(fit)#> Model = Log-log power #> #> Log-transformation function used: log() #> #> Call: #> lm(formula = logT(S) ~ logT(A), data = data) #> #> Residuals: #> Min 1Q Median 3Q Max #> -1.3591 -0.7584 0.1177 0.6009 1.0739 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> LogC 3.01865 0.35442 8.517 6.56e-07 *** #> z 0.33854 0.08523 3.972 0.00139 ** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.7626 on 14 degrees of freedom #> Multiple R-squared: 0.5298, Adjusted R-squared: 0.4962 #> F-statistic: 15.78 on 1 and 14 DF, p-value: 0.001391 #> #> Power (non-linear) parameters: #> c = 33.18 #> z = 0.28