-
-
Save kdauria/524eade46135f6348140 to your computer and use it in GitHub Desktop.
| stat_smooth_func <- function(mapping = NULL, data = NULL, | |
| geom = "smooth", position = "identity", | |
| ..., | |
| method = "auto", | |
| formula = y ~ x, | |
| se = TRUE, | |
| n = 80, | |
| span = 0.75, | |
| fullrange = FALSE, | |
| level = 0.95, | |
| method.args = list(), | |
| na.rm = FALSE, | |
| show.legend = NA, | |
| inherit.aes = TRUE, | |
| xpos = NULL, | |
| ypos = NULL) { | |
| layer( | |
| data = data, | |
| mapping = mapping, | |
| stat = StatSmoothFunc, | |
| geom = geom, | |
| position = position, | |
| show.legend = show.legend, | |
| inherit.aes = inherit.aes, | |
| params = list( | |
| method = method, | |
| formula = formula, | |
| se = se, | |
| n = n, | |
| fullrange = fullrange, | |
| level = level, | |
| na.rm = na.rm, | |
| method.args = method.args, | |
| span = span, | |
| xpos = xpos, | |
| ypos = ypos, | |
| ... | |
| ) | |
| ) | |
| } | |
| StatSmoothFunc <- ggproto("StatSmooth", Stat, | |
| setup_params = function(data, params) { | |
| # Figure out what type of smoothing to do: loess for small datasets, | |
| # gam with a cubic regression basis for large data | |
| # This is based on the size of the _largest_ group. | |
| if (identical(params$method, "auto")) { | |
| max_group <- max(table(data$group)) | |
| if (max_group < 1000) { | |
| params$method <- "loess" | |
| } else { | |
| params$method <- "gam" | |
| params$formula <- y ~ s(x, bs = "cs") | |
| } | |
| } | |
| if (identical(params$method, "gam")) { | |
| params$method <- mgcv::gam | |
| } | |
| params | |
| }, | |
| compute_group = function(data, scales, method = "auto", formula = y~x, | |
| se = TRUE, n = 80, span = 0.75, fullrange = FALSE, | |
| xseq = NULL, level = 0.95, method.args = list(), | |
| na.rm = FALSE, xpos=NULL, ypos=NULL) { | |
| if (length(unique(data$x)) < 2) { | |
| # Not enough data to perform fit | |
| return(data.frame()) | |
| } | |
| if (is.null(data$weight)) data$weight <- 1 | |
| if (is.null(xseq)) { | |
| if (is.integer(data$x)) { | |
| if (fullrange) { | |
| xseq <- scales$x$dimension() | |
| } else { | |
| xseq <- sort(unique(data$x)) | |
| } | |
| } else { | |
| if (fullrange) { | |
| range <- scales$x$dimension() | |
| } else { | |
| range <- range(data$x, na.rm = TRUE) | |
| } | |
| xseq <- seq(range[1], range[2], length.out = n) | |
| } | |
| } | |
| # Special case span because it's the most commonly used model argument | |
| if (identical(method, "loess")) { | |
| method.args$span <- span | |
| } | |
| if (is.character(method)) method <- match.fun(method) | |
| base.args <- list(quote(formula), data = quote(data), weights = quote(weight)) | |
| model <- do.call(method, c(base.args, method.args)) | |
| m = model | |
| eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2, | |
| list(a = format(coef(m)[1], digits = 3), | |
| b = format(coef(m)[2], digits = 3), | |
| r2 = format(summary(m)$r.squared, digits = 3))) | |
| func_string = as.character(as.expression(eq)) | |
| if(is.null(xpos)) xpos = min(data$x)*0.9 | |
| if(is.null(ypos)) ypos = max(data$y)*0.9 | |
| data.frame(x=xpos, y=ypos, label=func_string) | |
| }, | |
| required_aes = c("x", "y") | |
| ) | |
@kdauria Thanks, I'll have a look.
Great stuff! Thanks! I need to get around to editing it so that the equation and R2 values are on seperate lines. And add p-value.
Been using this successfully for awhile but seeming to run into a problem with the new ggplot2 version (ggplot2_2.2.0).
Error: StatSmooth was built with an incompatible version of ggproto.
Please reinstall the package that provides this extension.
Anyone else have (or hopefully, solve) this issue?
for ggplot2_2.2.0, use temporary workaround with filename specification:
devtools::source_gist("524eade46135f6348140", filename = "ggplot_smooth_func.R")
see: http://stackoverflow.com/questions/38345894/r-source-gist-not-working
It always gets r2 = NULL. What is the problem?
Sorry , I figured out the reason. I used method = 'glm'. Thank you very much! 🎉 💯
Thanks! Worked like a charm! :)
Greeeaaat! Thanks a lot for the code.
Did anyone managed to include p-value on a new line?
I managed to get p-value using lmp ()
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
And adjusted eq as the following:
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2~","~~italic(p)~"="~pval,
list(a = format(coef(m)[1], digits = 3),
b = format(coef(m)[2], digits = 3),
r2 = format(summary(m)$r.squared, digits = 3),
pval = ifelse(lmp(m)<1e-4, "<0.0001",format(lmp(m), digits = 3))))
But still coudlnt find a way to plot it on a new line. If you modify StatSmoothFunc's eq and it's ypos to if(is.null(ypos)) ypos = max(data$y)*0.9 you'll be able to use it, bt you're gonna have to call the fuction twice
This is a great function, thank you! Is it possible to specify unique xpos and ypos if you have multiple equations? For example, if you have three groupings in your data and each has it's own regression, the function does plot all 3, but sometimes they are on top of eachother (unless they happen to have very different min/max values, in which case you luck out and they stagger).
Just a thought...I really appreciate the work.
Here is how I was able to add a newline and p-values:
stat_smooth_func_with_pval <- function(mapping = NULL, data = NULL,
geom = "smooth", position = "identity",
...,
method = "auto",
formula = y ~ x,
se = TRUE,
n = 80,
span = 0.75,
fullrange = FALSE,
level = 0.95,
method.args = list(),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
xpos = NULL,
ypos = NULL,
xpos2 = NULL,
ypos2 = NULL) {
layer(
data = data,
mapping = mapping,
stat = StatSmoothFunc,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
method = method,
formula = formula,
se = se,
n = n,
fullrange = fullrange,
level = level,
na.rm = na.rm,
method.args = method.args,
span = span,
xpos = xpos,
ypos = ypos,
xpos2 = xpos2,
ypos2 = ypos2,
...
)
)
}
StatSmoothFunc <- ggproto("StatSmooth", Stat,
setup_params = function(data, params) {
# Figure out what type of smoothing to do: loess for small datasets,
# gam with a cubic regression basis for large data
# This is based on the size of the _largest_ group.
if (identical(params$method, "auto")) {
max_group <- max(table(data$group))
if (max_group < 1000) {
params$method <- "loess"
} else {
params$method <- "gam"
params$formula <- y ~ s(x, bs = "cs")
}
}
if (identical(params$method, "gam")) {
params$method <- mgcv::gam
}
params
},
compute_group = function(data, scales, method = "auto", formula = y~x,
se = TRUE, n = 80, span = 0.75, fullrange = FALSE,
xseq = NULL, level = 0.95, method.args = list(),
na.rm = FALSE, xpos=NULL, ypos=NULL,
xpos2=NULL, ypos2=NULL) {
if (length(unique(data$x)) < 2) {
# Not enough data to perform fit
return(data.frame())
}
if (is.null(data$weight)) data$weight <- 1
if (is.null(xseq)) {
if (is.integer(data$x)) {
if (fullrange) {
xseq <- scales$x$dimension()
} else {
xseq <- sort(unique(data$x))
}
} else {
if (fullrange) {
range <- scales$x$dimension()
} else {
range <- range(data$x, na.rm = TRUE)
}
xseq <- seq(range[1], range[2], length.out = n)
}
}
# Special case span because it's the most commonly used model argument
if (identical(method, "loess")) {
method.args$span <- span
}
if (is.character(method)) method <- match.fun(method)
base.args <- list(quote(formula), data = quote(data), weights = quote(weight))
model <- do.call(method, c(base.args, method.args))
m = model
eq1 <- substitute(italic(y) == a + b %.% italic(x),
list(a = format(coef(m)[1], digits = 3),
b = format(coef(m)[2], digits = 3)))
eq2 <- substitute(italic(r)^2~"="~r2*","~~italic(p)~"="~pval,
list(r2 = format(summary(m)$r.squared, digits = 3),
pval = format(summary(m)$coef[2,4], digits = 3)))
func_string1 = as.character(as.expression(eq1))
func_string2 = as.character(as.expression(eq2))
if(is.null(xpos)) xpos = min(data$x)*0.9
if(is.null(ypos)) ypos = max(data$y)*0.9
if(is.null(xpos2)) xpos2 = xpos
if(is.null(ypos2)) ypos2 = max(data$y)*0.6
data.frame(x = rbind(xpos, xpos2),
y = rbind(ypos, ypos2),
label = rbind(func_string1, func_string2))
},
required_aes = c("x", "y")
)
# source
# https://gist.github.com/kdauria/524eade46135f6348140#file-ggplot_smooth_func-r-L110-L111
Hello, first of all, thank you for this great function. It works great on my plots.
However, I was wondering how to get rid of the c() around the coefficients..
For example, in my graphs, I see:
y = c(9.49) + c(0.797)*x
Thanks,
I have the same problem as @jasonbaik94 the c() around the coefficients when I use the function, can someone help ?
Thanks
@jasonbaik94 and @falltok:
I managed to fix this by changing these lines:
eq1 <- substitute(italic(y) == a + b %.% italic(x),
list(a = format(coef(m)[1], digits = 3),
b = format(coef(m)[2], digits = 3)))
to have a double bracket:
eq1 <- substitute(italic(y) == a + b %.% italic(x),
list(a = format(coef(m)[[1]], digits = 3),
b = format(coef(m)[[2]], digits = 3)))
I don't think that broke anything...
It works well
Excellent! Thanks very much!
This is a great function, thank you! Is it possible to specify unique xpos and ypos if you have multiple equations? For example, if you have three groupings in your data and each has it's own regression, the function does plot all 3, but sometimes they are on top of eachother (unless they happen to have very different min/max values, in which case you luck out and they stagger).
Just a thought...I really appreciate the work.
Have the exact same problem and can't figure out a way
Hi, thank you for creating this. How would you go about changing the size of the equation and r2 value. currently I have a 6x4 facet wrap and the text isn't fully shown.
Thanks
Hey @fawda123, the snippet I wrote above moves the position of the equation according to the data range. For example,
xpos = min(data$x)*0.9means that the equation will be started a little left of the minimum x value. If you want to do something fancier, it shouldn't be too hard to write up another function. Some of the extensions at https://www.ggplot2-exts.org/ might give you some ideas. Maybe looking at the source ofggpmisc'sstat_poly_eqwould help?