Computes a vector of relative weights specific to a species in an entire data frame.

Returns a vector that contains the relative weight specific to each species for all individuals in an entire data frame.

Usage

wrAdd(wt, ...)

# Default S3 method
wrAdd(wt, len, spec, units = c("metric", "English"), WsOpts = NULL, ...)

# S3 method for class 'formula'
wrAdd(wt, data, units = c("metric", "English"), ...)

Arguments

wt: A numeric vector that contains weight measurements or a formula of the form wt~len+spec where “wt” generically represents the weight variable, “len” generically represents the length variable, and “spec” generically represents the species variable. Note that this formula can only contain three variables and they must be in the order of weight first, length second, species third.
...: Not used.
len: A numeric vector that contains length measurements. Not used if wt is a formula.
spec: A character or factor vector that contains the species names. Not used if wt is a formula.
units: A string that indicates whether the weight and length data in formula are in "metric" (DEFAULT; mm and g) or "English" (in and lbs) units.
WsOpts: A named list that provides specific choices for group, ref, or method for species for which more than one standard weight equation exists in WSlit.
data: A data.frame that minimally contains variables of the the observed lengths, observed weights, and the species names given in the formula=.

Value

A numeric vector that contains the computed relative weights, in the same order as in data=.

Details

This computes a vector that contains the relative weight specific to each species for all individuals in an entire data frame. The vector can be appended to an existing data.frame to create a variable that contains the relative weights for each individual. The relative weight value will be NA for each individual for which a standard weight equation does not exist in WSlit, a standard weight equation for the units given in units= does not exist in WSlit, or if the individual is shorter or longer than the lengths for which the standard weight equation should be applied. Either the linear or quadratic equation has been listed as preferred for each species, so only that equation will be used.

The species names in species must match the spelling and capitalization of species in WSlit. Use wsVal() to see a list of all species for which standard weight equations exist in WSlit and, more importantly, how the species names are spelled and capitalized.

Some (few) species have more than one equation listed in WSlit (for the specified units). In these instances the user must select one of the equations to use with WsOpts. WsOpts is a list of lists where the inside list contains one or more of group, ref, or method (see WSlit) required to specify a single equation for a particular species, which is the name of the inner list. See the examples for an illustration of how to use WsOpts.

See examples and this article for a demonstration.

IFAR Chapter

8-Condition.

References

Ogle, D.H. 2016. Introductory Fisheries Analyses with R. Chapman & Hall/CRC, Boca Raton, FL.

Author

Derek H. Ogle, DerekOgle51@gmail.com

Examples

#===== Create random data for three species
#----- just to control the randomization
set.seed(345234534)
dbt <- data.frame(species=factor(rep(c("Bluefin Tuna"),30)),
                  tl=round(rnorm(30,1900,300),0))
dbt$wt <- round(4.5e-05*dbt$tl^2.8+rnorm(30,0,6000),1)
dbg <- data.frame(species=factor(rep(c("Bluegill"),30)),
                  tl=round(rnorm(30,130,50),0))
dbg$wt <- round(4.23e-06*dbg$tl^3.316+rnorm(30,0,10),1)
dlb <- data.frame(species=factor(rep(c("Largemouth Bass"),30)),
                  tl=round(rnorm(30,350,60),0))
dlb$wt <- round(2.96e-06*dlb$tl^3.273+rnorm(30,0,60),1)
df <- rbind(dbt,dbg,dlb)
str(df)
#> 'data.frame':	90 obs. of  3 variables:
#>  $ species: Factor w/ 3 levels "Bluefin Tuna",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ tl     : num  1371 1558 2031 2226 2124 ...
#>  $ wt     : num  19231 38147 90530 104718 88761 ...

#===== Add Wr variable
#----- using formula interface
df$Wr1 <- wrAdd(wt~tl+species,data=df)

#----- same but with non-formula interface
df$Wr2 <- wrAdd(df$wt,df$tl,df$species)

#----- same but using dplyr
if (require(dplyr)) {
  df <- df %>%
    mutate(Wr3=wrAdd(wt,tl,species))
}

#----- examine results
peek(df,n=10)
#>            species   tl       wt       Wr1       Wr2       Wr3
#> 1     Bluefin Tuna 1371  19231.0        NA        NA        NA
#> 10    Bluefin Tuna 2060  84458.4        NA        NA        NA
#> 20    Bluefin Tuna 2545 142705.1        NA        NA        NA
#> 30    Bluefin Tuna 1889  59611.6        NA        NA        NA
#> 40        Bluegill   52     -5.8        NA        NA        NA
#> 50        Bluegill  122     36.7 104.78446 104.78446 104.78446
#> 60        Bluegill   72     19.0        NA        NA        NA
#> 70 Largemouth Bass  396    900.0  95.49622  95.49622  95.49622
#> 80 Largemouth Bass  328    553.9 108.88706 108.88706 108.88706
#> 90 Largemouth Bass  371    612.6  80.46670  80.46670  80.46670
 
#===== Example with only one species in the data.frame
bg <- droplevels(subset(df,species=="Bluegill"))
bg$Wr4 <- wrAdd(wt~tl+species,data=bg)
bg
#>     species  tl    wt       Wr1       Wr2       Wr3       Wr4
#> 31 Bluegill 158  75.3  91.21118  91.21118  91.21118  91.21118
#> 32 Bluegill 157  80.3  99.33729  99.33729  99.33729  99.33729
#> 33 Bluegill 126  26.0  66.70289  66.70289  66.70289  66.70289
#> 34 Bluegill 203 196.2 103.52426 103.52426 103.52426 103.52426
#> 35 Bluegill 136  59.9 119.29213 119.29213 119.29213 119.29213
#> 36 Bluegill 192 159.4 101.17226 101.17226 101.17226 101.17226
#> 37 Bluegill 132  54.9 120.71165 120.71165 120.71165 120.71165
#> 38 Bluegill 121  30.7  90.07870  90.07870  90.07870  90.07870
#> 39 Bluegill 136  42.4  84.44051  84.44051  84.44051  84.44051
#> 40 Bluegill  52  -5.8        NA        NA        NA        NA
#> 41 Bluegill 111  17.5  68.35134  68.35134  68.35134  68.35134
#> 42 Bluegill 226 267.8  98.98901  98.98901  98.98901  98.98901
#> 43 Bluegill 217 238.2 100.74912 100.74912 100.74912 100.74912
#> 44 Bluegill 163  86.9  94.93048  94.93048  94.93048  94.93048
#> 45 Bluegill 119  18.3  56.74628  56.74628  56.74628  56.74628
#> 46 Bluegill  79  17.9        NA        NA        NA        NA
#> 47 Bluegill  78   3.5        NA        NA        NA        NA
#> 48 Bluegill 172 113.9 104.11454 104.11454 104.11454 104.11454
#> 49 Bluegill 106  28.0 127.42181 127.42181 127.42181 127.42181
#> 50 Bluegill 122  36.7 104.78446 104.78446 104.78446 104.78446
#> 51 Bluegill  95  14.1  92.27576  92.27576  92.27576  92.27576
#> 52 Bluegill  47  -3.3        NA        NA        NA        NA
#> 53 Bluegill 155  72.8  93.97048  93.97048  93.97048  93.97048
#> 54 Bluegill 167 101.5 102.31474 102.31474 102.31474 102.31474
#> 55 Bluegill 146  65.1 102.46768 102.46768 102.46768 102.46768
#> 56 Bluegill 125  54.5 143.56312 143.56312 143.56312 143.56312
#> 57 Bluegill  66  -7.1        NA        NA        NA        NA
#> 58 Bluegill 160  92.1 107.00345 107.00345 107.00345 107.00345
#> 59 Bluegill  85   1.7  16.08777  16.08777  16.08777  16.08777
#> 60 Bluegill  72  19.0        NA        NA        NA        NA

#===== Example with a species that has Ws eqns for multiple groups and a
#      group needs to be specified with WsOpts
wae <- data.frame(species=factor(rep(c("Walleye"),30)),
                  tl=round(rnorm(30,500,200),0))
wae$wt <- round(3.33e-06*wae$tl^3.16+rnorm(30,0,50),1)
# wae$Wr <- wrAdd(wt~tl+species,data=wae) # will err b/c multiple groups
wae$Wr <- wrAdd(wt~tl+species,data=wae,
                WsOpts=list(Walleye=list(group="overall")))
peek(wae,n=10)
#>    species  tl     wt        Wr
#> 1  Walleye 791 4768.4  82.25673
#> 3  Walleye 300  271.9 102.36786
#> 7  Walleye 659 2719.5  83.83656
#> 10 Walleye 493 1066.4  82.73074
#> 13 Walleye 505 1151.5  82.75559
#> 17 Walleye 676 2794.8  79.45482
#> 20 Walleye 754 4131.0  82.98781
#> 23 Walleye 500 1067.4  79.17765
#> 27 Walleye 544 1431.0  81.17740
#> 30 Walleye 490 1117.6  88.40216