I'm trying to plot a graph between two columns of data from the data frame called "final". I want the p value and r^2 value to show up on the graph.
I'm using this function and code, but it gives me the error "cannot find y value"
library(ggplot2)
lm_eqn <- function(final, x, y){
m <- lm(final[,y] ~ final[,x])
output <- paste("r.squared = ", round(summary(m)$adj.r.squared, digits = 4), " | p.value = ", formatC(summary(m)$coefficients[8], format = "e", digits = 4))
return(output)
}
output_plot <- lm_eqn(final, x, y)
p1 <- ggplot(final, aes(x=ENSG00000153563, y= ENSG00000163599)) + geom_point() + geom_smooth(method=lm, se=FALSE) + labs(x = "CD8A", y = "CTLA-4") + ggtitle("CD8 v/s CTLA-4", subtitle = paste("Linear Regression of Expression |", output_plot))
How do I get both columns of data x and y to flow through the function and for the graph to plot with the p value and residual value printed on the graph?
Thanks in advance.
When you call function for output_plot generation you have to use the same ENS... variables as in your plot. After simplifying slightly function, should work now
library(stats)
library(ggplot2)
lm_eqn <- function(x, y){
m <- lm(y ~ x)
output <- paste("r.squared = ", round(summary(m)$adj.r.squared, digits = 4), " | p.value = ", formatC(summary(m)$coefficients[8], format = "e", digits = 4))
return(output)
}
x <-c(1,2,5,2,3,6,7,0)
y <-c(2,3,5,9,8,3,3,1)
final <- data_frame(x,y)
output_plot <- lm_eqn(x, y)
p1 <- ggplot(final, aes(x=x, y= y)) + geom_point() + geom_smooth(method=lm, se=FALSE) + labs(x = "x", y = "y") + ggtitle("CD8 v/s CTLA-4", subtitle = paste("Linear Regression of Expression |", output_plot))
Related
Most of the time we run a regression with interactive terms, we are interested in a partial derivative. For example, consider the model below,
If I am interested to know the effect of X1 on P(Y), or the partial derivative of X1 on P(Y), I need the following combination of coefficients:
Instead of calculating it by hand, I can use, for example, the lincom function in R to calculate linear combination of regression parameters. But I would like not only to know the numbers from calculations like this; I would like to plot them. The problem is, if I am using a R package to plot coefficients (e.g., coefplot) it plots the coefficients from my model, but with no option for linear combination of coefficients. Is there any way to combine the lincom function (or other function that calculates combination of parameter) with coefplot (or other coefficient plot packages with this option)?
Of course, in the example above I only consider the derivative of X1, and if I plot it I will have a plot with one dot and its confidence intervals only, but I would like to show in the plot the coefficients for the partial derivatives of X1, X2, and Z, as in the example below.
Coefficients plot (the one I have):
Combination of parameters or partial derivatives plot (the one I am trying to get):
I discovered that Stata has a function that does what I am looking for, called "plotbeta." Does R have something similar?
Here's a start. This defined a function called plotBeta(), the ... are arguments that get passed down to geom_text() for the estimate text.
plotBeta <- function(mod, confidence_level = .95, include_est=TRUE, which.terms=NULL, plot=TRUE, ...){
require(glue)
require(ggplot2)
b <- coef(mod)
mains <- grep("^[^:]*$", names(b), value=TRUE)
mains.ind <- grep("^[^:]*$", names(b))
if(!is.null(which.terms)){
if(!(all(which.terms %in% mains)))stop("Not all terms in which.terms are in the model\n")
ins <- match(which.terms, mains)
mains <- mains[ins]
mains.ind <- mains.ind[ins]
}
icept <- grep("Intercept", mains)
if(length(icept) > 0){
mains <- mains[-icept]
mains.ind <- mains.ind[-icept]
}
if(inherits(mod, "lm") & !inherits(mod, "glm")){
crit <- qt(1-(1-confidence_level)/2, mod$df.residual)
}else{
crit <- qnorm(1-(1-confidence_level)/2)
}
out.df <- NULL
for(i in 1:length(mains)){
others <- grep(glue("^{mains[i]}:"), names(b))
others <- c(others, grep(glue(":{mains[i]}:"), names(b)))
others <- c(others, grep(glue(":{mains[i]}$"), names(b)))
all.inds <- c(mains.ind[i], others)
ones <- rep(1, length(all.inds))
est <- c(b[all.inds] %*% ones)
se.est <- sqrt(c(ones %*% vcov(mod)[all.inds, all.inds] %*% ones))
lower <- est - crit*se.est
upper <- est + crit*se.est
tmp <- data.frame(var = mains[i],
lab = glue("dy/d{mains[i]} = {paste('B', all.inds, sep='', collapse=' + ')}"),
labfac = i,
est = est,
se.est = se.est,
lower = lower,
upper=upper)
tmp$est_text <- sprintf("%.2f (%.2f, %.2f)", tmp$est, tmp$lower, tmp$upper)
out.df <- rbind(out.df, tmp)
}
out.df$labfac <- factor(out.df$labfac, labels=out.df$lab)
if(!plot){
return(out.df)
}else{
g <- ggplot(out.df, aes(x=est, y=labfac, xmin=lower, xmax=upper)) +
geom_vline(xintercept=0, lty=2, size=.25, col="gray50") +
geom_errorbarh(height=0) +
geom_point() +
ylab("") + xlab("Estimates Combined") +
theme_classic()
if(include_est){
g <- g + geom_text(aes(label=est_text), vjust=0, ...)
}
g
}
}
Here's an example with some made-up data:
set.seed(2101)
dat <- data.frame(
X1 = rnorm(500),
X2 = rnorm(500),
Z = rnorm(500),
W = rnorm(500)
)
dat <- dat %>%
mutate(yhat = X1 - X2 + X1*X2 - X1*Z + .5*X2*Z - .75*X1*X2*Z + W,
y = yhat + rnorm(500, 0, 1.5))
mod <- lm(y ~ X1*X2*Z + W, data=dat)
plotBeta(mod, position=position_nudge(y=.1), size=3) + xlim(-2.5,2)
EDIT: comparing two models
Using the newly-added plot=FALSE, we can generate the data and then combine and plot.
mod <- lm(y ~ X1*X2*Z + W, data=dat)
p1 <- plotBeta(mod, plot=FALSE)
mod2 <- lm(y ~ X1*X2 + Z + W, data=dat)
p2 <- plotBeta(mod2, plot=FALSE)
p1 <- p1 %>% mutate(model = factor(1, levels=1:2,
labels=c("Model 1", "Model 2")))
p2 <- p2 %>% mutate(model = factor(2, levels=1:2,
labels=c("Model 1", "Model 2")))
p_both <- bind_rows(p1, p2)
p_both <- p_both %>%
arrange(var, model) %>%
mutate(labfac = factor(1:n(), labels=paste("dy/d", var, sep="")))
ggplot(p_both, aes(x=est, y=labfac, xmin=lower, xmax=upper)) +
geom_vline(xintercept=0, lty=2, size=.25, col="gray50") +
geom_linerange(position=position_nudge(y=c(-.1, .1))) +
geom_point(aes(shape=model),
position=position_nudge(y=c(-.1, .1))) +
geom_text(aes(label=est_text), vjust=0,
position=position_nudge(y=c(-.2, .15))) +
scale_shape_manual(values=c(1,16)) +
ylab("") + xlab("Estimates Combined") +
theme_classic()
I have read many postings on this topic using expression(), paste(), and bquote(), or some combination. I think I am close to solving my problem, but I just can't get there. The following script generates a plot labelled with "y = 1 + 2(x); r^2= 0.9". How can I italicize "y" and "x", and italicize the "r" and superscript the 2 of "r^2"? If I have overlooked a relevant earlier post, sorry, but please direct me to it.
df <- data.frame(x=c(1:5), y=c(1:5))
a <- 1
b <- 2
r2 <- 0.9
eq <- paste("y = ", a, " + ", b, "(x); r^2=", r2)
ggplot(data=df, aes(x=x, y=y))+
geom_point(color="black")+
geom_text(x=2, y=4,label=eq, parse=FALSE)
You could use annotate() which allows you to paste directly into the plot.
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_point(color="black") +
annotate('text', 2.5, 4,
label=paste("italic(y)==", a, "+", b,
"~italic(x)~';'~italic(r)^2==", r2),
parse=TRUE,
hjust=1, size=5)
Yields:
Data:
df <- data.frame(x=c(1:5), y=c(1:5))
a <- 1
b <- 2
r2 <- 0.9
You can use a combination of substitute and plotmath (https://www.rdocumentation.org/packages/grDevices/versions/3.5.1/topics/plotmath) to italicize the text-
# setup
set.seed(123)
library(ggplot2)
# dataframe
df <- data.frame(x = c(1:5), y = c(1:5))
# label
eq <- substitute(
expr =
paste(
italic("y"),
" = ",
a,
" + ",
b,
"(",
italic("x"),
"); ",
italic("r") ^ 2,
" = ",
r2
),
env = base::list(a = 1,
b = 2,
r2 = 0.9)
)
# plot
ggplot(data = df, aes(x = x, y = y)) +
geom_point(color = "black") +
labs(subtitle = eq)
Created on 2018-12-04 by the reprex package (v0.2.1)
In addition to the answer by Indrajit Patil & jay-sf, I would like to add that there is an automated way to fit regression lines (I believe there are many), using a package called ggpmisc. The letters that you want in italic, are already formatted in such a way. The code that needs to be used is:
> install.packages('ggpmisc'); library(ggpmisc); formula <- y ~ x
> df <- data.frame(x=c(1:5), y=c(1:5))
> ggplot(data = df, aes(x, y)) + geom_point(color="black") +
geom_smooth(method = "lm", formula = formula) +
stat_poly_eq(aes(label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),
formula = formula, parse = TRUE)
It shows the fitted lines also, which I hope is not an impediment to the main goal.
EDIT: The line can be removed using linetype = 0, compatible with
most of the aesthetics in ggplot2.
... + geom_smooth(method = "lm", formula = formula, linetype = 0) + ...
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.
I am working on a figure for publication and wish to annotate it with some beta and p values; the style guidelines of my area dictate that these numbers be formatted without leading zeros (e.g., ".003", not "0.003"). I have run into what seems like a Catch-22; I have extracted beta and p values from my models and done some preprocessing to correctly format them so that they are now characters rather than numeric:
fake.beta.vals <- c(".53", ".29", ".14")
fake.p.vals <- c(".034", ".001", ".050")
But, when I try to use these values in my figure, parse = TRUE turns them back into numeric values, losing the formatting I need.
fake.beta.vals <- c(".53", ".29", ".14")
fake.p.vals <- c(".034", ".001", ".050")
p <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))
p <- p +
geom_smooth(method = "lm") +
geom_point() +
facet_wrap( ~ Species)
p
len <-length(levels(iris$Species))
vars <- data.frame(expand.grid(levels(iris$Species)))
colnames(vars) <- c("Species")
betalabs <- as.data.frame(fake.beta.vals)
plabs <- as.data.frame(fake.p.vals)
dat <- data.frame(
x = rep(7, len),
y = rep(4, len),
vars,
betalabs,
plabs)
dat$fake.beta.vals <- as.factor(dat$fake.beta.vals)
dat$fake.p.vals <- as.factor(dat$fake.p.vals)
p <- p +
geom_text(
aes(x = x,
y = y,
label = paste("list(beta ==",
fake.beta.vals,
", italic(p) ==",
fake.p.vals,
")"),
group = NULL),
size = 5,
data = dat,
parse = TRUE)
p
I have been banging my head against this problem for a while now but adding as.character():
label = paste("list(beta ==",
as.character(fake.beta.vals),
", italic(p) ==",
as.character(fake.p.vals),
")"),
Is obviously also cancelled out by parse = TRUE
And adding the function I had previously used to format my values:
statformat <- function(val,z){
sub("^(-?)0.", "\\1.", sprintf(paste("%.",z,"f", sep = ""), val))
}
Is even worse:
label = paste("list(beta ==",
statformat(fake.beta.vals, 2),
", italic(p) ==",
statformat(fake.p.vals, 3),
")"),
And just ends up with a mess.
Help?
Use bquote to create the labels, then coerce to a character representation using deparse
For example
# create a list of labels using bquotw
labs <- Map(.beta = fake.beta.vals,
.p = fake.p.vals,
f = function(.beta,.p) bquote(list(beta == .(.beta), italic(p) == .(.p))))
# coerce to a character representation for parse=TRUE to work within
# geom_text
dat <- data.frame(
x = rep(7, len),
y = rep(4, len),
vars,
labels = sapply(labs,deparse))
p <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_smooth(method = "lm") +
geom_point() +
facet_wrap( ~ Species) +
geom_text(data = dat, aes(x=x,y=y,label=labels), parse=TRUE)
p
After getting back to my computer and re-reading your question, I found that I misinterpreted the question. Trying out the I function, I found that it doesn't seem to work with parse.
I found a way to get it to work, and this is by encasing your fake.beta.vals and fake.p.vals with the ` character or the ' character in your call to parse.
p <- p +
geom_text(
aes(x = x,
y = y,
label = paste("list(beta ==",
"`", fake.beta.vals, "`",
", italic(p) ==",
"`", fake.p.vals, "`",
")",
sep=""),
group = NULL),
size = 5,
data = dat,
parse = TRUE)
That should work.
I have a data frame created the following way.
library(ggplot2)
x <- data.frame(letters[1:10],abs(rnorm(10)),abs(rnorm(10)),type="x")
y <- data.frame(letters[1:10],abs(rnorm(10)),abs(rnorm(10)),type="y")
# in reality the number of row could be larger than 10 for each x and y
all <- rbind(x,y)
colnames(all) <- c("name","val1","val2","type")
What I want to do is to create a faceted ggplot that looks roughly like this:
Hence each facet above is the correlation plot of the following:
# Top left facet
subset(all,type=="x")$val1
subset(all,type=="y")$val1
# Top right facet
subset(all,type=="x")$val1
subset(all,type=="y")$val2
# ...etc..
But I'm stuck with the following code:
p <- ggplot(all, aes(val1, val2))+ geom_smooth(method = "lm") + geom_point() +
facet_grid(type ~ )
# Calculate correlation for each group
cors <- ddply(all, c(type ~ ), summarise, cor = round(cor(val1, val2), 2))
p + geom_text(data=cors, aes(label=paste("r=", cor, sep="")), x=0.5, y=0.5)
What's the right way to do it?
Some of your code was incorrect. This works for me:
p <- ggplot(all, aes(val1, val2))+ geom_smooth(method = "lm") + geom_point() +
facet_grid(~type)
# Calculate correlation for each group
cors <- ddply(all, .(type), summarise, cor = round(cor(val1, val2), 2))
p + geom_text(data=cors, aes(label=paste("r=", cor, sep="")), x=1, y=-0.25)
Edit: Following OP's comment and edit. The idea is to re-create the data with all four combinations and then facet.
# I consider the type in your previous data to be xx and yy
dat <- data.frame(val1 = c(rep(all$val1[all$type == "x"], 2),
rep(all$val1[all$type == "y"], 2)),
val2 = rep(all$val2, 2),
grp1 = rep(c("x", "x", "y", "y"), each=10),
grp2 = rep(c("x", "y", "x", "y"), each=10))
p <- ggplot(dat, aes(val1, val2)) + geom_point() + geom_smooth(method = "lm") +
facet_grid(grp1 ~ grp2)
cors <- ddply(dat, .(grp1, grp2), summarise, cor = round(cor(val1, val2), 2))
p + geom_text(data=cors, aes(label=paste("r=", cor, sep="")), x=1, y=-0.25)
Since your data is not in the appropriate format, some reshaping is necessary before it can be plotted.
Firstly, reshape the data to the long format:
library(reshape2)
allM <- melt(all[-1], id.vars = "type")
Split the values along type and val1 vs. val2:
allList <- split(allM$value, interaction(allM$type, allM$variable))
Create a list of all combinations:
allComb <- unlist(lapply(c(1, 3),
function(x)
lapply(c(2 ,4),
function(y)
do.call(cbind, allList[c(x, y)]))),
recursive = FALSE)
Create a new dataset:
allNew <- do.call(rbind,
lapply(allComb, function(x) {
tmp <- as.data.frame(x)
tmp <- (within(tmp, {xval <- names(tmp)[1];
yval <- names(tmp)[2]}))
names(tmp)[1:2] <- c("x", "y")
tmp}))
Plot:
library(ggplot2)
p <- ggplot(allNew, aes(x = x, y = y)) +
geom_smooth(method = "lm") +
geom_point() +
facet_grid(yval ~ xval)
# Calculate correlation for each group
library(plyr)
cors <- ddply(allNew, .(yval, xval), summarise, cor = round(cor(x, y), 2))
p + geom_text(data=cors, aes(label=paste("r=", cor, sep="")), x=0.5, y=0.5)
There is an additional package ggpubr available now addressing exactly this issue with the stat_cor() function.
library(tidyverse)
library(ggpubr)
ggplot(all, aes(val1, val2))+
geom_smooth(method = "lm") +
geom_point() +
facet_grid(~type) +
stat_cor()