Creating a list of ggplots using for loop

I'm fitting four models to a dataset and trying to plot three graphical analysis for each model in a unique figure using ggplot and grid.arrange using the following reproducible code:

# Generate dataset
data <- data.frame(
  IDADE = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20),
  HDOM = c(0.7,1.4,1.9,2.6,3.6,4.9,6.3,8,9.4,10.6,11.5,12.4,13.4,14.3,15.1,16,16.7,17.4,18,18.3)
)
# Fit models
library(minpack.lm)
model_a <- nlsLM(HDOM ~ a*(1-exp(-b*IDADE))^c,
                          start=list(a=30,b=0.5,c=1), control = nls.control(maxiter = 1024),
                          data = data)
model_b <- nlsLM(HDOM ~ b0*exp(-b1*(1/(IDADE^b2))),
                        start = list(b0=40,b1=5,b2=0.5), control = nls.control(maxiter = 1024),
                        data = data)
model_c <- nlsLM(HDOM ~ b0*(1-exp(-b1*IDADE^b2)),
                   start = list(b0=24,b1=0.05, b2=1.5), control = nls.control(maxiter = 1024),
                   data = data)
model_d = nlsLM(HDOM ~ b0*(1+b1*IDADE^b2)^b3,
                      start=list(b0=25,b1=-0.2, b2=-0.1, b3=20), control = nls.control(maxiter = 1024),
                      data = data)
# Model names
modelnames <- c('Model A','Model B','Model C','Model D')
# List of models
models <- list()
models[modelnames] <- list(model_a,model_b,model_c,model_d)
#List of plots
plot_analysis <- list()
for(i in 1:length(modelos)){
  plot_analysis[[paste0(modelnames[i],' - Fit curve')]] <- ggplot()+
    geom_line(aes(x=seq(0,20,0.1),y=predict(models[[i]],data.frame(IDADE=seq(0,idmax,0.1)))))+
    scale_x_continuous(expand=c(0,0), limits = c(0,idmax), breaks = seq(0,20,2))+
    scale_y_continuous(expand=c(0,0),limits = c(0,ls))+
    geom_point(alpha=0.2,aes(x=data$IDADE,y=data$HDOM))+
    xlab('Idade (anos)')+ylab('Hdom (m)')+
    ggtitle(paste0(modelnames[i],' - Syx = ',round(summary(models[[i]])$sigma,2)))
    
  plot_analysis[[paste0(modelos_nomes[i],' - Residual scatter plot')]] <-
    ggplot()+
    geom_point(alpha=0.2,aes(x=data$HDOM,y=residuals(models[[i]])/mean(data$HDOM)*100))+
    scale_x_continuous(expand=c(0,0))+ 
    scale_y_continuous(expand=c(0,0),limits = c(-50,50))+
    geom_line(aes(x=c(0,20),y=c(0,0)))+
    xlab('Hdom estimada (m)')+ylab('Desvio (%)')+
    ggtitle('')
  
  plot_analysis[[paste0(modelos_nomes[i],' - Histogram of residuals')]] <-
    ggplot()+
    geom_histogram(aes(x=residuals(models[[i]])/mean(data$HDOM)*100,
                       y=..count../length(data$HDOM)*100),
                   bins = 9,colour='black')+
    xlab('Classe')+
    ylab('FrequĂȘncia (%)')+
    ggtitle('')
}
# Plot all in one figure
library(gridExtra)
do.call('grid.arrange',c(plot_analysis, ncol = 3))

The resulting plot is:
image

The issue: the four lines of images are showing the same results (Model D results). Only the titles correspond to the appropriate models. All other graphic results correspond to Model D.

Does anyone know how to solve this?

Two packages I suggest you look into parsnip and patchwork. This will require, that you re-write your code, which will serve you well moving forward.

Hope it helps!

1 Like

I haven't gone through your code to try and figure out what's going wrong, but below I've provided what I hope is a simpler method for producing the plots.

Set up the models:

library(minpack.lm)
library(tidyverse)
library(gridExtra)
theme_set(theme_classic())

# Generate dataset
data <- data.frame(
  IDADE = 1:20,
  HDOM = c(0.7,1.4,1.9,2.6,3.6,4.9,6.3,8,9.4,10.6,11.5,12.4,13.4,14.3,15.1,16,16.7,17.4,18,18.3)
)

# Fit models
model_a <- nlsLM(HDOM ~ a*(1-exp(-b*IDADE))^c,
                 start=list(a=30,b=0.5,c=1), control = nls.control(maxiter = 1024),
                 data = data)
model_b <- nlsLM(HDOM ~ b0*exp(-b1*(1/(IDADE^b2))),
                 start = list(b0=40,b1=5,b2=0.5), control = nls.control(maxiter = 1024),
                 data = data)
model_c <- nlsLM(HDOM ~ b0*(1-exp(-b1*IDADE^b2)),
                 start = list(b0=24,b1=0.05, b2=1.5), control = nls.control(maxiter = 1024),
                 data = data)
model_d = nlsLM(HDOM ~ b0*(1+b1*IDADE^b2)^b3,
                start=list(b0=25,b1=-0.2, b2=-0.1, b3=20), control = nls.control(maxiter = 1024),
                data = data)

To set up the plots, we create a list of the model object names and feed that to imap. imap iterates through each model and generates a list of three plots for each model. The overall result of imap, three plots for each model, is stored in a list called plots.

We then use ggarrange from the egg package and grid.arrange from the gridExtra package to lay out the plots.

plots = syms(paste0("model_", letters[1:4])) %>% set_names() %>% 
  imap(function(m, m.name) {
    
    # Get model object
    m = eval(m)
    
    # Reformat model name
    m.name = str_to_title(gsub("_", " ", m.name))
    
    # Create prediction data frame
    d = eval(m$data)
    pd = tibble(IDADE = seq(0, max(d$IDADE), 0.1))
    pd$HDOM = predict(m, newdata=pd)
    
    list(a = ggplot(pd, aes(IDADE, HDOM)) +
           geom_line() +
           geom_point(data=d, alpha=0.2) +
           scale_x_continuous(expand=expand_scale(mult=c(0,0.01)), breaks = seq(0,20,2)) +
           scale_y_continuous(expand=c(0,0)) +
           labs(x='Idade (anos)', y='Hdom (m)',
                title = paste0(m.name,' - Syx = ', round(summary(m)$sigma, 2))),

         b = ggplot(tibble(x=d$HDOM, y=residuals(m)/mean(x)*100)) +
           geom_point(alpha=0.2, aes(x, y)) +
           geom_hline(yintercept=0) +
           scale_x_continuous(expand=expand_scale(mult=c(0,0.01))) +
           scale_y_continuous(expand=c(0,0), limits = c(-50,50)) +
           labs(x='Hdom estimada (m)', y='Desvio (%)'),

         c = ggplot(tibble(x=residuals(m)/mean(d$HDOM)*100)) +
           geom_histogram(aes(x, y=..count../nrow(d)*100), bins = 9, colour='black') +
           labs(x='Classe', y='FrequĂȘncia (%)') +
           scale_y_continuous(expand=expand_scale(mult=c(0,0.01)))
    )

  })

map(plots, ~egg::ggarrange(plots=.x, ncol=3, draw=FALSE)) %>% 
  grid.arrange(grobs=., ncol=1)

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