Adding a power curve to scatterplot - r

Question

I want to add a power curve with confidence intervals to my diamter-weight relationship, which clearly follows a y=a*x^b regression. So far, I used the geom_smooth "loess" version, but this is not yet quite right and perfect. Any suggestion how to add a power regression line would be much appreciated. Below is the used code:
p2<-ggplot(Data,aes(x=Diameter,y=Wet_weight,colour=Site))+
geom_point(size=3.5,alpha=0.3)+
geom_smooth(aes(group=Species),method=loess,colour="black")+
labs(x="\nUmbrella diamter (mm)",y="Wet weight (mg)\n")+theme_classic()+
scale_colour_manual(values=c("black","dark blue","blue","dark green","green"))+
theme(axis.title.x=element_text(size=20),
axis.text.x=element_text(size=18,colour="black"),
axis.title.y=element_text(size=20),
axis.text.y=element_text(size=18,colour="black"),
axis.ticks=element_line(colour="black",size=1),
axis.line=element_line(colour="black",size=1,linetype="solid"),
legend.position=c(0.18,0.75),
legend.text=element_text(colour="black",size=17),
legend.title=element_text(colour="black",size=18))
p2
Thank you!

Answer 1

I used this to get many equations, R2, and plots.
df= #change your data frame so it fits the current code
variables=c("group","year") #if you have multiple groups/seasons/years/elements add them here
df$y= #which variable will be your y
df$x= #which variable will be your x
#No changes get the equations
text=df %>%
group_by(across(all_of(variables))) %>% #your grouping variables
do(broom::tidy(lm(log(y) ~ log(x), data = .))) %>%
ungroup() %>%
mutate(y = round(ifelse(term=='(Intercept)',exp(estimate),estimate),digits = 2)) %>% #your equation values rounded to 2
select(-estimate,-std.error,-statistic ,-p.value) %>%
pivot_wider(names_from = term,values_from = y) %>%
rename(.,a=`(Intercept)`,b=`log(x)`)
#CHANGE before running!! add your grouping variables
rsq=df %>%
split(list(.$group,.$year)) %>% #---- HERE add the names after $
map(~lm(log(y) ~ log(x), data = .)) %>%
map(summary) %>%
map_dbl("r.squared") %>%
data.frame()
#Join the R2 and y results for the plot in a single data frame and write the equations
labels.df=mutate(rsq,groups=row.names(rsq)) %>%
separate(col = groups,into = c(variables),sep = "[.]",
convert = TRUE, remove = T, fill = "right") %>%
rename("R"='.') %>%
left_join(text,.) %>%
mutate(R=round(R,digits = 4), #round your R2 digits
eq= paste('y==',a,"~x^(",b,")", sep = ""),
rsql=paste("R^2==",R),
full= paste('y==',a,"~x^(",b,")","~~R^2==",R, sep = ""))
# plot
ggplot(df,aes(x = x,y = y)) +
geom_point(size=4,mapping = aes(
colour=factor(ifelse(is.na(get(variables[2])),"",(get(variables[2])))), #points colour
shape=get(variables[1]))) + # different shapes
facet_wrap(get(variables[1])~ifelse(is.na(get(variables[2])),"",get(variables[2])),
scales = "free",labeller = labeller(.multi_line = F))+ #for multiple groups; join text in one line
stat_smooth(mapping=aes(colour=get(variables[1])), #colours for our trend
method = 'nls', formula = 'y~a*x^b',
method.args = list(start=c(a=1,b=1)),se=FALSE) +
geom_text(labels.df,x = Inf, y = Inf,size=5, mapping = aes(label = (eq)), parse = T,vjust=1, hjust=1)+
geom_text(labels.df,x = Inf,y = Inf,size=5, mapping = aes(label = (rsql)), parse = T,vjust=2.5, hjust=1)+
#scale_y_log10() + #add this to avoid problems with big y values
labs(x="Your x label",y="your y label")+
theme_bw(base_size = 16) +
theme(legend.position = "none",
strip.background = element_rect(fill="#b2d6e2"))