I am trying to add lm model coefs of two parallel modelling results onto the same ggplot plot. Here is my working example:
library(ggplot2)
set.seed(100)
dat <- data.frame(
x <- rnorm(100, 1),
y <- rnorm(100, 10),
lev <- gl(n = 2, k = 50, labels = letters[1:2])
)
mod1 <- lm(y~x, dat = dat[lev %in% "a", ])
r1 <- paste("R^2==", round(summary(mod1)[[9]], 3))
p1<- paste("p==", round(summary(mod1)[[4]][2, 4], 3), sep= "")
lab1 <- paste(r1, p1, sep =",")
mod2 <- lm(y~x, dat = dat[lev %in% "b", ])
r2 <- paste("R^2==", round(summary(mod2)[[9]], 3))
p2 <- paste("p==", round(summary(mod2)[[4]][2, 4], 3), sep= "")
lab2 <- paste(r2, p2, sep =",")
ggplot(dat, aes(x = x, y = y, col = lev)) + geom_jitter() + geom_smooth(method = "lm") + annotate("text", x = 2, y = 12, label = lab1, parse = T) + annotate("text", x = 10, y = 8, label = lab2, parse = T)
Here is the promot shows:
Error in parse(text = text[[i]]) : <text>:1:12: unexpected ','
1: R^2== 0.008,
Now the problem is that I could label either R2 or p value seperately, but not both of them together. How could I do to put the two results into one single line on the figure?
BTW, any other efficienty way of doing the same thing as my code? I have nine subplots that I want to put into one full plot, and I don't want to add them one by one.
++++++++++++++++++++++++++ Some update ++++++++++++++++++++++++++++++++++
Following #G. Grothendieck 's kind suggestion and idea, I tried to wrap the most repeatative part of the codes into a function, so I could finish all the plot with a few lines. Now the problem is that, whatever I changed the input variables, the output plot are basically the same, except the axis labels. Can anyone explain why? The following is the working code I used:
library(ggplot2)
library(ggpubr)
set.seed(100)
dat <- data.frame(
x = rnorm(100, 1),
y = rnorm(100, 10),
z = rnorm(100, 25),
lev = gl(n = 2, k = 50, labels = letters[1:2])
)
test <- function(dat, x, y){
fmt <- "%s: Adj ~ R^2 == %.3f * ',' ~ {p == %.3f}"
mod1 <- lm(y ~ x, dat, subset = lev == "a")
sum1 <- summary(mod1)
lab1 <- sprintf(fmt, "a", sum1$adj.r.squared, coef(sum1)[2, 4])
mod2 <- lm(y ~ x, dat, subset = lev == "b")
sum2 <- summary(mod2)
lab2 <- sprintf(fmt, "b", sum2$adj.r.squared, coef(sum2)[2, 4])
colors <- 1:2
p <- ggplot(dat, aes(x = x, y = y, col = lev)) +
geom_jitter() +
geom_smooth(method = "lm") +
annotate("text", x = 2, y = c(12, 8), label = c(lab1, lab2),
parse = TRUE, hjust = 0, color = colors) +
scale_color_manual(values = colors)
return(p)
}
ggarrange(test(dat, x, z), test(dat, y, z))
There are several problems here:
x, y and lev are arguments to data.frame so they must be specified using = rather than <-
make use of the subset= argument in lm
use sprintf instead of paste to simplify the specification of labels
label the text strings a and b and make them the same color as the corresponding lines to identify which is which
the formula syntax needs to be corrected. See fmt below.
it would be clearer to use component names and accessor functions of the summary objects where available
use TRUE rather than T because the latter can be overridden if there is a variable called T but TRUE can never be overridden.
use hjust=0 and adjust the x= and y= in annotate to align the two text strings
combine the annotate statements
place the individual terms of the ggplot statement on separate lines for improved readability
This gives:
library(ggplot2)
set.seed(100)
dat <- data.frame(
x = rnorm(100, 1),
y = rnorm(100, 10),
lev = gl(n = 2, k = 50, labels = letters[1:2])
)
fmt <- "%s: Adj ~ R^2 == %.3f * ',' ~ {p == %.3f}"
mod1 <- lm(y ~ x, dat, subset = lev == "a")
sum1 <- summary(mod1)
lab1 <- sprintf(fmt, "a", sum1$adj.r.squared, coef(sum1)[2, 4])
mod2 <- lm(y ~ x, dat, subset = lev == "b")
sum2 <- summary(mod2)
lab2 <- sprintf(fmt, "b", sum2$adj.r.squared, coef(sum2)[2, 4])
colors <- 1:2
ggplot(dat, aes(x = x, y = y, col = lev)) +
geom_jitter() +
geom_smooth(method = "lm") +
annotate("text", x = 2, y = c(12, 8), label = c(lab1, lab2),
parse = TRUE, hjust = 0, color = colors) +
scale_color_manual(values = colors)
Unless I'm misunderstanding your question, the problem's with the parse = T arguments to your annotate calls. I don't think your strings need to be parsed. Try parse = F instead, or just drop the parameter, as the default value seems to be FALSE anyway
Related
We often want individual regression equations in ggplot facets. The best way to do this is build the labels in a dataframe and then add them manually. But what if the labels contain plotmath, e.g., superscripts?
Here is a way to do it. The plotmath is converted to a string and then parsed by ggplot. The test_eqn function is taken from another Stackoverflow post, I'll link it when I find it again. Sorry about that.
library(ggplot2)
library(dplyr)
test_eqn <- function(y, x){
m <- lm(log(y) ~ log(x)) # fit y = a * x ^ b in log space
p <- exp(predict(m)) # model prediction of y
eq <- substitute(expression(Y==a~X^~b),
list(
a = format(unname(exp(coef(m)[1])), digits = 3),
b = format(unname(coef(m)[2]), digits = 3)
))
list(eq = as.character(eq)[2], pred = p)
}
set.seed(123)
x <- runif(20)
y <- runif(20)
test_eqn(x,y)$eq
#> [1] "Y == \"0.57\" ~ X^~\"0.413\""
data <- data.frame(x = x,
y = y,
f = sample(c("A","B"), 20, replace = TRUE)) %>%
group_by(f) %>%
mutate(
label = test_eqn(y,x)$eq, # add label
labelx = mean(x),
labely = mean(y),
pred = test_eqn(y,x)$pred # add prediction
)
# plot fits (use slice(1) to avoid multiple copies of labels)
ggplot(data) +
geom_point(aes(x = x, y = y)) +
geom_line(aes(x = x, y = pred), colour = "red") +
geom_text(data = slice(data, 1), aes(x = labelx, y = labely, label = label), parse = TRUE) +
facet_wrap("f")
Created on 2021-10-20 by the reprex package (v2.0.1)
How can I include a legend inside one of the empty panels of the following matrix plot?
I have color coded different regression lines in the plots. I need a legend based on color.
I believe this answer comes closest to answer my question, yet I do not know how exactly to modify my code to get a legend based on color for different regression lines.
As for the background of the code, I am trying to study different robust and non-robust regression methods applied to multivariate data with and without outliers.
library(ggplot2)
library(GGally)
library(MASS)
library(robustbase)
## Just create data -- you can safely SKIP this function.
##
## Take in number of input variables (k), vector of ranges of k inputs
## ranges = c(min1, max1, min2, max2, ...) (must have 2k elements),
## parameters to create data (must be consistent with the number of
## input variables plus one), parameters are vector of linear
## coefficients (b) and random seed (seed), number of observations
## (n), vector of outliers (outliers)
##
## Return uncontaminated dataframe and contaminated dataframe
create_data <- function(k, ranges, b, seed = 6, n,
outliers = NULL) {
x <- NULL # x: matrix of input variables
for (i in 1:k) {
set.seed(seed^i)
## x <- cbind(x, runif(n, ranges[2*i-1], ranges[2*i]))
x <- cbind(x, rnorm(n, ranges[2*i-1], ranges[2*i]))
}
set.seed(seed - 2)
x_aug = cbind(rep(1, n), x)
y <- x_aug %*% b
y_mean = mean(y)
e <- rnorm(n, 0, 0.20 * y_mean) # rnorm x
y <- y + e
df <- data.frame(x = x, y = y)
len <- length(outliers)
n_rows <- len %/% (k+1)
if (!is.null(outliers)) {
outliers <- matrix(outliers, n_rows, k+1, byrow = TRUE)
df_contamin <- data.frame(x = rbind(x, outliers[,1:k]), y = c(y, outliers[,k+1]))
} else {
df_contamin <- df
}
dat <- list(df, df_contamin)
}
# plot different regression models (some are robust) for two types of
# data (one is contaminated with outliers)
plot_models <- function(data, mapping, data2) {
cb_palette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
## 1.grey, 2.light orange, 3.light blue, 4.green, 5.yellow, 6.blue, 7.red, 8.purple
plt <- ggplot(data = data, mapping = mapping) +
geom_point() +
theme_bw() +
geom_smooth(method = lm, formula = y ~ x, data = data2, color = cb_palette[3], se = FALSE) +
geom_smooth(method = lm, formula = y ~ x, color = cb_palette[7], se = FALSE) +
geom_smooth(method = rlm, formula = y ~ x, color = cb_palette[4], se = FALSE) +
geom_smooth(method = lmrob, formula = y ~ x, color = cb_palette[1], se = FALSE)
plt
}
# trim the upper and right panels of plots
trim_gg <- function(gg) {
n <- gg$nrow
gg$nrow <- gg$ncol <- n-1
v <- 1:n^2
gg$plots <- gg$plots[v > n & v%%n != 0]
gg$xAxisLabels <- gg$xAxisLabels[-n]
gg$yAxisLabels <- gg$yAxisLabels[-1]
gg
}
dat <- create_data(3, c(1, 10, 1, 10, 1, 10), c(5, 8, 6, 7), 6, 20, c(30, 30, 50, 400))
df <- dat[[1]]
df_contamin <- dat[[2]]
## Note that plot_models is called here
g <- ggpairs(df_contamin, columns = 1:4, lower = list(continuous = wrap(plot_models, data2 = df)), diag = list(continuous = "blankDiag"), upper = list(continuous = "blank")) #, legend = lgd)
gr <- trim_gg(g)
print(gr)
Created on 2019-10-09 by the reprex package (v0.3.0)
Sorry for the long code, but most probably only the plot_models function and the line where ggpairs is called need to be modified.
I want to get a legend in the blank upper half of the plots. It may be done by somehow tweaking the plot_models function, setting the mapping in ggpairs to color using ggplot2::aes_string, and using getPlot and putPlot of the GGally package. But I can't wrap my head around how to do it exactly.
I need to create some gam plots in ggplot. I can do them with the general plot function, but am unsure how to do with ggplot. Here is my code and plots with the regular plot function. I'm using the College data set from the ISLR package.
train.2 <- sample(dim(College)[1],2*dim(College)[1]/3)
train.college <- College[train.2,]
test.college <- College[-train.2,]
gam.college <- gam(Outstate~Private+s(Room.Board)+s(Personal)+s(PhD)+s(perc.alumni)+s(Expend)+s(Grad.Rate), data=train.college)
par(mfrow=c(2,2))
plot(gam.college, se=TRUE,col="blue")
See update below old answer.
Old answer:
There is an implementation of GAM plotting using ggplot2 in voxel library. Here is how you would go about it:
library(ISLR)
library(mgcv)
library(voxel)
library(tidyverse)
library(gridExtra)
data(College)
set.seed(1)
train.2 <- sample(dim(College)[1],2*dim(College)[1]/3)
train.college <- College[train.2,]
test.college <- College[-train.2,]
gam.college <- gam(Outstate~Private+s(Room.Board)+s(Personal)+s(PhD)+s(perc.alumni)+s(Expend)+s(Grad.Rate), data=train.college)
vars <- c("Room.Board", "Personal", "PhD", "perc.alumni","Expend", "Grad.Rate")
map(vars, function(x){
p <- plotGAM(gam.college, smooth.cov = x) #plot customization goes here
g <- ggplotGrob(p)
}) %>%
{grid.arrange(grobs = (.), ncol = 2, nrow = 3)}
after a bunch of errors: In plotGAM(gam.college, smooth.cov = x) :
There are one or more factors in the model fit, please consider plotting by group since plot might be unprecise
To compare to the plot.gam:
par(mfrow=c(2,3))
plot(gam.college, se=TRUE,col="blue")
You might also want to plot the observed values:
map(vars, function(x){
p <- plotGAM(gam.college, smooth.cov = x) +
geom_point(data = train.college, aes_string(y = "Outstate", x = x ), alpha = 0.2) +
geom_rug(data = train.college, aes_string(y = "Outstate", x = x ), alpha = 0.2)
g <- ggplotGrob(p)
}) %>%
{grid.arrange(grobs = (.), ncol = 3, nrow = 2)}
or per group (especially important if you used the by argument (interaction in gam).
map(vars, function(x){
p <- plotGAM(gam.college, smooth.cov = x, groupCovs = "Private") +
geom_point(data = train.college, aes_string(y = "Outstate", x = x, color= "Private"), alpha = 0.2) +
geom_rug(data = train.college, aes_string(y = "Outstate", x = x, color= "Private" ), alpha = 0.2) +
scale_color_manual("Private", values = c("#868686FF", "#0073C2FF")) +
theme(legend.position="none")
g <- ggplotGrob(p)
}) %>%
{grid.arrange(grobs = (.), ncol = 3, nrow = 2)}
Update, 08. Jan. 2020.
I currently think the package mgcViz offers superior functionality compared to the voxel::plotGAMfunction. An example using the above data set and models:
library(mgcViz)
viz <- getViz(gam.college)
print(plot(viz, allTerms = T), pages = 1)
plot customization is similar go ggplot2 syntax:
trt <- plot(viz, allTerms = T) +
l_points() +
l_fitLine(linetype = 1) +
l_ciLine(linetype = 3) +
l_ciBar() +
l_rug() +
theme_grey()
print(trt, pages = 1)
This vignette shows many more examples.
I'm attempting to use library(scales) and scale_color_gradientn() to create a custom mapping of colors to a continuous variable, in an attempt to limit the effect of outliers on the coloring of my plot. This works for a single plot, but does not work when I use a loop to generate several plots and store them in a list.
Here is a minimal working example:
library(ggplot2)
library(scales)
data1 <- as.data.frame(cbind(x = rnorm(100),
y = rnorm(100),
v1 = rnorm(100, mean = 2, sd = 1),
v2 = rnorm(100, mean = -2, sd = 1)))
#add outliers
data1[1,"v1"] <- 200
data1[2,"v1"] <- -200
data1[1,"v2"] <- 50
data1[2,"v2"] <- -50
#define color palette
cols <- colorRampPalette(c("#3540FF","black","#FF3535"))(n = 100)
#simple color scale
col2 <- scale_color_gradient2(low = "#3540FF",
mid = "black",
high = "#FF3535"
)
#outlier-adjusted color scale
{
aa <- min(data1$v1)
bb <- quantile(data1$v1, 0.05)
cc <- quantile(data1$v1, 0.95)
dd <- max(data1$v1)
coln <- scale_color_gradientn(colors = cols[c(1,5,95,100)],
values = rescale(c(aa,bb,cc,dd),
limits = c(aa,dd))
)
}
Plots:
1. Plot with simple scales - outliers cause scales to stretch out.
ggplot(data1, aes(x = x, y = y, color = v1))+
geom_point()+
col2
2. Plot with outlier-adjusted scales - correct color scaling.
ggplot(data1, aes(x = x, y = y, color = v1))+
geom_point()+
coln
3. The scales for v1 do not work for v2 as the data is different.
ggplot(data1, aes(x = x, y = y, color = v2))+
geom_point()+
coln
#loop to produce list of plots each with own scale
{
plots <- list()
k <- 1
for (i in c("v1","v2")){
aa <- min(data1[,i])
bb <- quantile(data1[,i],0.05)
cc <- quantile(data1[,i], 0.95)
dd <- max(data1[,i])
colm <- scale_color_gradientn(colors = cols[c(1,5,95,100)],
values = rescale(c(aa,bb,cc,dd),
limits = c(aa,dd)))
plots[[k]] <- ggplot(data1, aes_string(x = "x",
y = "y",
color = i
))+
geom_point()+
colm
k <- k + 1
}
}
4. First plot has the wrong scales.
plots[[1]]
5. Second plot has the correct scales.
plots[[2]]
So I'm guessing this has something to do with the scale_color_gradientn() function being called when the plotting takes place, rather than within the loop.
If anyone can help with this, it'd be much appreciated. In base R I would bin the continuous data and assigning hex colors into a vector used for fill color, but I'm unsure how I can apply this within ggplot.
You need to use a closure (function with associated environment):
{
plots <- list()
k <- 1
for (i in c("v1", "v2")){
colm <- function() {
aa <- min(data1[, i])
bb <- quantile(data1[, i], 0.05)
cc <- quantile(data1[, i], 0.95)
dd <- max(data1[, i])
scale_color_gradientn(colors = cols[c(1, 5, 95, 100)],
values = rescale(c(aa, bb, cc, dd),
limits = c(aa, dd)))
}
plots[[k]] <- ggplot(data1, aes_string(x = "x",
y = "y",
color = i)) +
geom_point() +
colm()
k <- k + 1
}
}
plots[[1]]
plots[[2]]
This code gives me a plot with the regression equation and R2: (but i need to mention in which x and y the equation will be (manually)
CORRELATIONP3 <-CORRELATIONP2[product=='a',]
x<-CORRELATIONP3$b
y<-CORRELATIONP3$p
df <- data.frame(x = x)
m <- lm(y ~ x, data = df)
p <- ggplot(data = df, aes(x = x, y = y)) +
scale_x_continuous("b (%)") +
scale_y_continuous("p (%)")+
geom_smooth(method = "lm", formula = y ~ x) +
geom_point()
p
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list( a = format(coef(m)[1], digits = 4),
b = format(coef(m)[2], digits = 4),
r2 = format(summary(m)$r.squared, digits = 3)))
dftext <- data.frame(x = 3, y = 0.2, eq = as.character(as.expression(eq)))
p + geom_text(aes(label = eq), data = dftext, parse = TRUE)
But, with this code I have R and p-value: And here the information about R and p values fits automatically in the plot, why? I want this in the first one as well.
CORRELATIONP3 <-CORRELATIONP2[product=='a',]
x<-CORRELATIONP3$b
y<-CORRELATIONP3$p
df <- data.frame(x = x)
m <- lm(y ~ x, data = df)
p <- ggplot(data = df, aes(x = x, y = y)) +
scale_x_continuous("b (%)") +
scale_y_continuous("p (%)")+
geom_smooth(method = "lm", formula = y ~ x) +
geom_point()
p
eq <- substitute(italic(r)~"="~rvalue*","~italic(p)~"="~pvalue, list(rvalue = sprintf("%.2f",sign(coef(m)[2])*sqrt(summary(m)$r.squared)), pvalue = format(summary(m)$coefficients[2,4], digits = 3)))
dftext <- data.frame(x = 30, y = 0.4, eq = as.character(as.expression(eq)))
p + geom_text(aes(label = eq), data = dftext, parse = TRUE)
Can you tell me how can I join all the 4 informations in one sigle plot? (R, R2, equation and p-value)
Besides that, i would like that these informations could be fitted automatically in the plot, not manually.
Ok, I am not sure if this works as you have not given a reproducible example of your data but I guess you just have to rename one of your variables e.g.:
eq2 <- substitute(italic(r)~"="~rvalue*","~italic(p)~"="~pvalue,
list(rvalue = sprintf("%.2f",sign(coef(m)[2])*sqrt(summary(m)$r.squared)),
pvalue = format(summary(m)$coefficients[2,4], digits = 3)))
and then you change the points you put it on in your plot just a bit below your other block in the first plot. x and y here refer to the position of the text lable so play around with these until your text looks ok.
dftext2 <- data.frame(x = 30, y = 0.12, eq2 = as.character(as.expression(eq2)))
p + geom_text(aes(label = eq2), data = dftext2, parse = TRUE)
please let me know if this works and if this is what you meant.