I want to set limits to the x-axis of the plot of the output of the mixfit function of the mixR package. This output is of class mixfitEM.
Reproducable example: First I simulate a mixture of log normals.
rm(list=ls())
library(mixR)
library("tidyverse")
set.seed(07062022)
N <- 1000
lbda=.5
mu1=1
Del=3
mu2=mu1+Del
components <- sample(1:2,prob=c((1-lbda), lbda),size=N,replace=TRUE)
mus <- c(mu1,mu2)
sds <- sqrt(c(1,.5))
Y <- rlnorm(n=N,meanlog=mus[components],sdlog=sds[components])
Then I try to fit two log normals into this data using mixfit() from mixR. In the first attempt I tried to use the plot() function from the base package; but plot ignores the xlim argument.
mod4 <- mixfit(Y, ncomp = 2, family = 'lnorm')
plot(mod4, title = 'Log-Normal Mixture (2 components)', xlim=c(0,200))
Then I tried to plot with ggplot, which, in theory should be possible according to the mixR manual. But ggplot does not understand the mixfitEM class.
ggplot(mod4)+
+ coord_cartesian(xlim = c(0, 200))
produces the following error:
> ggplot(data.frame(mod4))+
+ + coord_cartesian(xlim = c(0, 200))
Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) :
cannot coerce class ‘"mixfitEM"’ to a data.frame
> ggplot(mod4)+
+ + coord_cartesian(xlim = c(0, 200))
Error in `fortify()`:
! `data` must be a data frame, or other object coercible by `fortify()`, not an S3 object with class mixfitEM.
Run `rlang::last_error()` to see where the error occurred.
When you call plot on the mixfitEM object, you are creating a ggplot. The reason for this is that plot is a generic function, so when package authors create a new class, they are free to use whatever method they want to draw the plot. In this case, if you examine the source code of mixR:::plot.mixfitEM you will see it actually uses ggplot to draw its output. This means you can use ggplot syntax to modify the output:
plot(mod4, title = 'Log-Normal Mixture (2 components)') + xlim(c(0, 200))
Related
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
I am trying to create an R function that would run a GWR on variables that the user specifies from a Spatial Polygons Data Frame. The end result of running the function are two mappings - one of the independent variable's values and one of the coefficient values from the GWR model. I'm having trouble with the second map.
I have managed to create the GWR model and a 'results' object for the coefficients that I would be visualizing.
gwr.model <- gwr(SpatialPolygonsDataFrame#data[, y] ~ SpatialPolygonsDataFrame#data[, x],
data = SpatialPolygonsDataFrame,
adapt = GWRbandwidth,
hatmatrix = TRUE,
se.fit = TRUE)
results <- as.data.frame(gwr.model$SDF)
gwr.map <- SpatialPolygonsDataFrame
gwr.map#data <- cbind(SpatialPolygonsDataFrame#data, as.matrix(results))
To create the visualization of the GWR coefficients, I have to specify my tm_fill() to be a column from the 'results' object, but I do not know how to do it so that the function may be used will any Spatial Polygons Data Frame. So far, I have tried using the paste0() function, as so:
map2 <- tm_shape(gwr.map) + tm_fill(paste0("SpatialPolygonsDataFrame.", x), n = 5, style = "quantile", title = "Coefficient") +
tm_layout(frame = FALSE, legend.text.size = 0.5, legend.title.size = 0.6)
But I got an error saying that the fill argument is neither colors nor a valid variable name.
I'll be grateful for any tips that could help me resolve the issue.
Switching to the package sf - leaving sp behind - probably will solve your problem here.
In the absence of a reproducible example, let me try to suggest the following here:
convert your results with gwr.map.sf <- sf::st_as_sf(gwr.map). Then you add the results of your GWR simply as a new column: gwr.map$results <- results (my understanding is that the dimensions should fit).
Finally you should be able to plot like this:
map2 <- tm_shape(gwr.map.sf) + tm_fill("results", n = 5, style = "quantile", title = "Coefficient") +
tm_layout(frame = FALSE, legend.text.size = 0.5, legend.title.size = 0.6)
From library mgcv
i get the points to plot with:
fsb <- fs.boundary(r0=0.1, r=1.1, l=2173)
if with standard graphic package i plot fsb and then i add lines i get :
x11()
plot(fsb)
lines(fsb$x,fsb$y)
I try now with ggplot (this is the line within a bigger code) :
tpdf <- data.frame(ts=fsb$x,ps=fsb$y)
ts=fsb$x
ps=fsb$y
geom_line(data=tpdf, aes(ts,ps), inherit.aes = FALSE)
i get a messy plot:
I think that i'm failing the order in geom_line
This can be solved by using geom_path:
ggplot(tpdf)+
geom_point(aes(ts,ps)) +
geom_path(aes(ts,ps))
You have a very odd way of using ggplot I recommend you to reexamine it.
data:
library(mgcv)
fsb <- fs.boundary(r0 = 0.1, r=2, l=13)
tpdf <- data.frame(ts=fsb$x,ps=fsb$y)
You'll have to specify the group parameter - for example, this
ggplot(tpdf) +
geom_point(aes(ts, ps)) +
geom_line(aes(ts, ps, group = gl(4, 40)))
gives me a plot similar to the one in base R.
I am currently trying to plot some density distributions functions with R's ggplot2. I have the following code:
f <- stat_function(fun="dweibull",
args=list("shape"=1),
"x" = c(0,10))
stat_F <- stat_function(fun="pweibull",
args=list("shape"=1),
"x" = c(0,10))
S <- function() 1 - stat_F
h <- function() f / S
wei_h <- ggplot(data.frame(x=c(0,10))) +
stat_function(fun=h) +
...
Basically I want to plot hazard functions based on a Weibull Distribution with varying parameters, meaning I want to plot:
The above code gives me this error:
Computation failed in stat_function():
unused argument (x_trans)
I also tried to directly use
S <- 1 - stat_function(fun="pweibull", ...)
instead of above "workaround" with the custom function construction. This threw another error, since I was trying to do numeric arithmetics on an object:
non-numeric argument for binary operator
I get that error, but I have no idea for a solution.
I have done some research, but without success. I feel like this should be straightforward. Also I would like to do it "manually" as much as possible, but if there is no simple way to do this, then a packaged solution is just fine aswell.
Thanks in advance for any suggestions!
PS: I basically want to recreate the graph you can find in Kiefer, 1988 on page 10 of the linked PDF file.
Three comments:
stat_function is a function statistic for ggplot2, you cannot divide two stat_function expressions by each other or otherwise use them in mathematical expressions, as in S <- 1 - stat_function(fun="pweibull", ...). That's a fundamental misunderstanding of what stat_function is. stat_function always needs to be added to a ggplot2 plot, as in the example below.
The fun argument for stat_function takes a function as an argument, not a string. You can define functions on the fly if you need ones that don't exist already.
You need to set up an aesthetic mapping, via the aes function.
This code works:
args = list("shape" = 1.2)
ggplot(data.frame(x = seq(0, 10, length.out = 100)), aes(x)) +
stat_function(fun = dweibull, args = args, color = "red") +
stat_function(fun = function(...){1-pweibull(...)}, args = args, color = "green") +
stat_function(fun = function(...){dweibull(...)/(1-pweibull(...))},
args = args, color = "blue")
I am running quantile regressions for several independent variables separately (same dependent). I want to plot only the slope estimates over several quantiles of each variable in a single plot.
Here's a toy data:
set.seed(1988)
y <- rnorm(50, 5, 3)
x1 <- rnorm(50, 3, 1)
x2 <- rnorm(50, 1, 0.5)
# Running Quantile Regression
require(quantreg)
fit1 <- summary(rq(y~x1, tau=1:9/10), se="boot")
fit2 <- summary(rq(y~x2, tau=1:9/10), se="boot")
I want to plot only the slope estimates over quantiles. Hence, I am giving parm=2 in plot.
plot(fit1, parm=2)
plot(fit2, parm=2)
Now, I want to combine both these plots in a single page.
What I have tried so far;
I tried setting par(mfrow=c(2,2)) and plotting them. But it's producing a blank page.
I have tried using gridExtra and gridGraphics without success. Tried to convert base graphs into Grob objects as stated here
Tried using function layout function as in this document
I am trying to look into the source code of plot.rqs. But I am unable to understand how it's plotting confidence bands (I'm able to plot only the coefficients over quantiles) or to change mfrow parameter there.
Can anybody point out where am I going wrong? Should I look into the source code of plot.rqs and change any parameters there?
While quantreg::plot.summary.rqs has an mfrow parameter, it uses it to override par('mfrow') so as to facet over parm values, which is not what you want to do.
One alternative is to parse the objects and plot manually. You can pull the tau values and coefficient matrix out of fit1 and fit2, which are just lists of values for each tau, so in tidyverse grammar,
library(tidyverse)
c(fit1, fit2) %>% # concatenate lists, flattening to one level
# iterate over list and rbind to data.frame
map_dfr(~cbind(tau = .x[['tau']], # from each list element, cbind the tau...
coef(.x) %>% # ...and the coefficient matrix,
data.frame(check.names = TRUE) %>% # cleaned a little
rownames_to_column('term'))) %>%
filter(term != '(Intercept)') %>% # drop intercept rows
# initialize plot and map variables to aesthetics (positions)
ggplot(aes(x = tau, y = Value,
ymin = Value - Std..Error,
ymax = Value + Std..Error)) +
geom_ribbon(alpha = 0.5) +
geom_line(color = 'blue') +
facet_wrap(~term, nrow = 2) # make a plot for each value of `term`
Pull more out of the objects if you like, add the horizontal lines of the original, and otherwise go wild.
Another option is to use magick to capture the original images (or save them with any device and reread them) and manually combine them:
library(magick)
plots <- image_graph(height = 300) # graphics device to capture plots in image stack
plot(fit1, parm = 2)
plot(fit2, parm = 2)
dev.off()
im1 <- image_append(plots, stack = TRUE) # attach images in stack top to bottom
image_write(im1, 'rq.png')
The function plot used by quantreg package has it's own mfrow parameter. If you do not specify it, it enforces some option which it chooses on it's own (and thus overrides your par(mfrow = c(2,2)).
Using the mfrow parameter within plot.rqs:
# make one plot, change the layout
plot(fit1, parm = 2, mfrow = c(2,1))
# add a new plot
par(new = TRUE)
# create a second plot
plot(fit2, parm = 2, mfrow = c(2,1))