fn_figs {Morefi}

Customize the appearance of the plot

Description

The function creates a customized scatter plot with the observed values (points), fitted regression (solid line) and its confidence interval (shaded area) using the package ggplot2.

The function use the theme_papers() to customize the appearance of the plot.

The function returns a plot.

The function requires defining:

datos: A data frame containing the values of: independent variable (x); dependent (y) and adjusted (yc) variable; and Fleet (as factor)
CIPI: A data frame containing the a seseqquence of the independent variable; the fitted independent variable and their confidence and predicted intervals (lowest and highest value).
i: An integer value indicating the regression analyzed
modelos: A list of variables (integer values) that are used for the regression model.
etiquetas: A list indicating the labels of the axes (the name of the variable and its unit of measurement).

See also: ggplot2 (version 3.4.4) See also: theme_papers(): customized for the present work

fn_figs

The function is included in the Morefi package Morphological Relationships Fitted by Robust Regression.

library(Morefi)

The function is detailed below.

fn_figs <- function(datos, CIPI, i, etiquetas, modelos){
          Fig <- ggplot(CIPI,aes(x = CIPI[,1])) +
                 geom_ribbon(aes(x = CIPI[,1], ymin = CIPI[,3], ymax = CIPI[,4],
                                 fill = "aquamarine"), alpha = 1/50)    +
                 geom_ribbon(aes(x = CIPI[,1], ymin = CIPI[,5], ymax = CIPI[,6],
                                 fill = "grey", alpha = 1/50)) +
                 geom_line(aes(x= CIPI[,1], y = CIPI[,2])) +
                 geom_point(datos, mapping=aes(x= datos[,1], y = datos[,2],
                                 shape = factor(datos[,4]), stroke = 1,
                                 fill = datos[,4]), alpha = 1/10)+
                 labs(tag = letters[i],x= etiquetas[modelos[[i]][1]],
                                 y = etiquetas[modelos[[i]][2]])
          print(Fig)
}   # End fn_figs

Examples

A total of 100 simulated data of total length cm (LT) and total weight (g) data for a hypothetical fish species. The x values range from 15 to 60 cm. The parameters of the potential model are: ordinate = 0.011 and slope = 2.5. The values of observed y were calculated using the parameters of the potential equation plus a random number between -20 and 20. The lower CIl and upperCIu confidence interval values were calculated using the Standard Error of the Estimate SE.

set.seed(123)
rvar <- sample(-20:20, 100, replace=TRUE)
x <- sample(15:60,100,replace=T)
y <- (0.011*x^2.5)+rvar
n <- length(x)
Fleet <- as.factor(sample(1:2,100,replace = TRUE))

yc <- 0.011*x^2.5
datos <- data.frame(x1=x,y1=y,yc=yc,Fleet=Fleet)

# CI & PI

#SE <- sqrt(sum((y-yc)^2)/(100-2))
MSE <- sum((y-yc)^2)/(100-2)

xseq <- seq(15,60, by=2.5)
yseq <- 0.011*xseq^2.5

CP <- (1/n)+(((xseq-mean(xseq))^2)/sum((xseq-mean(xseq))^2))

ICl <- yseq -(1.96*(sqrt(MSE*CP)))
ICu <- yseq +(1.96*(sqrt(MSE*CP)))

IPl <- yseq -(1.96*(sqrt(MSE*(1+CP))))
IPu <- yseq +(1.96*(sqrt(MSE*(1+CP))))

df <- as.data.frame(cbind(xseq, yseq, ICl,ICu, IPl, IPu))
                    colnames(df) <- c("x1", "fit","IC_L","IC_U", "IP_L","IP_U")
                    df 
#>      x1        fit       IC_L      IC_U       IP_L      IP_U
#> 1  15.0   9.585634   1.197678  17.97359 -13.499379  32.67065
#> 2  17.5  14.092492   6.571710  21.61327  -8.691760  36.87674
#> 3  20.0  19.677398  13.014851  26.33994  -2.838142  42.19294
#> 4  22.5  26.414901  20.597692  32.23211   4.134867  48.69493
#> 5  25.0  34.375000  29.383676  39.36632  12.296202  56.45380
#> 6  27.5  43.623893  39.427497  47.82029  21.711117  65.53667
#> 7  30.0  54.224533  50.770669  57.67840  32.441761  76.00731
#> 8  32.5  66.237066  63.431423  69.04271  44.547630  87.92650
#> 9  35.0  79.719175  77.387414  82.05094  58.085934 101.35242
#> 10 37.5  94.726361  92.575640  96.87708  73.111885 116.34084
#> 11 40.0 111.312174 108.980413 113.64393  89.678933 132.94541
#> 12 42.5 129.528403 126.722759 132.33405 107.838967 151.21784
#> 13 45.0 149.425243 145.971378 152.87911 127.642470 171.20802
#> 14 47.5 171.051430 166.855034 175.24783 149.138654 192.96421
#> 15 50.0 194.454365 189.463040 199.44569 172.375567 216.53316
#> 16 52.5 219.680214 213.863006 225.49742 197.400180 241.96025
#> 17 55.0 246.774005 240.111458 253.43655 224.258465 269.28954
#> 18 57.5 275.779701 268.258919 283.30048 252.995449 298.56395
#> 19 60.0 306.740281 298.352325 315.12824 283.655268 329.82529

CIPI <- df

i <- 1

# To create the List etiquetas


lLT <- "LT (cm)"
lWT <- "WT (g)"

etiquetas <- list(lLT,lWT)

#To carry out each of the regressions, a list `modelos` is created to indicate 
#the variables that will be taken into account.

modelos <- list (LTvs.WT  =  c(1,2))

library(ggplot2)
#theme_papers <- readRDS("theme_papers.rds")
FY <- fn_figs(datos, CIPI, i, etiquetas, modelos)

20 May 2024

Customize the appearance of the plot

Description

Examples