Outputs a general summary of the structure and content of the object. The particular output obtained depends on the class of the argument object.
# S4 method for FLArray summary(object, ...)
summary(object)
#> An object of class "FLQuant" with: #> dim : 3 10 1 1 1 1 #> quant: quant #> units: kg #> #> Min : 0.2058224 #> 1st Qu.: 0.5695396 #> Mean : 1.695865 #> Median : 0.8955741 #> 3rd Qu.: 2.161326 #> Max : 8.377503 #> NAs : 0 %data(ple4) summary(ple4)#> An object of class "FLStock" #> #> Name: Plaice in IV #> Description: Imported from a VPA file. ( N:\Projecten\ICES WG\Demersale werkgroep WGNSS [...] #> Quant: age #> Dims: age year unit season area iter #> 10 52 1 1 1 1 #> #> Range: min max pgroup minyear maxyear minfbar maxfbar #> 1 10 10 1957 2008 2 6 #> #> catch : [ 1 52 1 1 1 1 ], units = t #> catch.n : [ 10 52 1 1 1 1 ], units = 10^3 #> catch.wt : [ 10 52 1 1 1 1 ], units = kg #> discards : [ 1 52 1 1 1 1 ], units = t #> discards.n : [ 10 52 1 1 1 1 ], units = 10^3 #> discards.wt : [ 10 52 1 1 1 1 ], units = kg #> landings : [ 1 52 1 1 1 1 ], units = t #> landings.n : [ 10 52 1 1 1 1 ], units = 10^3 #> landings.wt : [ 10 52 1 1 1 1 ], units = kg #> stock : [ 1 52 1 1 1 1 ], units = t #> stock.n : [ 10 52 1 1 1 1 ], units = 10^3 #> stock.wt : [ 10 52 1 1 1 1 ], units = kg #> m : [ 10 52 1 1 1 1 ], units = m #> mat : [ 10 52 1 1 1 1 ], units = NA #> harvest : [ 10 52 1 1 1 1 ], units = f #> harvest.spwn : [ 10 52 1 1 1 1 ], units = NA #> m.spwn : [ 10 52 1 1 1 1 ], units = NAdata(nsher) summary(nsher)#> An object of class "FLSR" #> #> Name: #> Description: #> Quant: age #> Dims: age year unit season area iter #> 1 45 1 1 1 1 #> #> Range: min minyear max maxyear #> 0 1960 0 2004 #> #> rec : [ 1 45 1 1 1 1 ], units = 10^3 #> ssb : [ 1 45 1 1 1 1 ], units = t*10^3 #> residuals : [ 1 45 1 1 1 1 ], units = NA #> fitted : [ 1 45 1 1 1 1 ], units = 10^3 #> #> Model: rec ~ a * ssb * exp(-b * ssb) #> <environment: 0xb7fa730> #> Parameters: #> params #> iter a b #> 1 119.4 0.009451 #> #> Log-likelihood: 15.862(0) #> Variance-covariance: #> a b #> a 255.3388181 1.808870e-02 #> b 0.0180887 1.992659e-06