I am using the plotFit function in the investr package in R to display my data as follows:
Figure 1
The code I am using to generate this is simply:
plotFit(nls model, interval = "confidence", level = 0.95, pch = 19, shade = TRUE,
col.conf = "seagreen2", col.fit = "green", lwd.fit = 2,
ylim = c(y1,y2), xlim = c(x1,x2),
xaxp = c(0,200,10), n = 100,
ylab = "", xlab = "",
main = "")
Is there a simple way that I could adapt the code to plot the data as bars, rather than points?
Yes, use type = "h". For example,
fit <- lm(dist ~ speed, data = cars)
library(investr)
plotFit(fit)
plotFit(fit, type = "h", lwd = 3)
Related
I am creating a plot based on INLA predictions. Everything works perfectly for the modelling, but for the plot,the legend on the graph are points (like https://gis.stackexchange.com/questions/350918/how-do-i-reverse-spplot-colour-key-so-the-values-are-decreasing) rather than a scale (like here http://www.nickeubank.com/wp-content/uploads/2015/10/RGIS3_MakingMaps_part1_mappingVectorData.html):
And here is the code that I would like to change. I guess there is a problem of factor (R spplot: colorbar rather than legend for SpatialPoint data, spplot issue with legend range and colors distribution) but I cannot understand how/what to change:
m_grid <- inla(formWITHOUT, data = inla.stack.data(region.stack.predGrid, spde = inlaSetting$region.spde),
family = "gamma",
control.predictor = list(A = inla.stack.A(region.stack.predGrid), compute = TRUE, link=1),
control.compute = list(cpo = TRUE, dic = TRUE),
control.family=list(link="default"))
summary(m_grid)
index.pred <- inla.stack.index(region.stack.predGrid, "region.pred")$data
region.grid_sf$Sbiomass <- m_grid$summary.fitted.values[index.pred, "mean"]
region.grid_sf$Sbiomass_sd <- m_grid$summary.fitted.values[index.pred, "sd"]
my.palette <- brewer.pal(n = 7, name = "OrRd")
par(mar = c(0,0,0,0))
spplot(region.grid_sf, c("Sbiomass"), col.regions = my.palette, col = "transparent")
Thanks in advance for any tips !
I finally found the answer:
cuts <- c(0,5000,10000,15000,20000,25000,30000)
spplot(region.grid_sf,
c("Sbiomass"),
col.regions = my.palette,
col = "transparent",
key.space = list(x=0.1,y=0.3),
colorkey =T,
cuts = cuts,
cex = 2,
pch = 22)
I am trying to prepare a graph for a poster presentation, but am getting very frustrated by how difficult things that should be simple are in plot. I want to plot a qq-plot of residuals from a mixed-effects model. All I want to do is change the font size of the axis title
. Here's a reproducible example.
library(lme4)
library(lattice)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
qqmath(fm1,
main = list("QQ-Plot", cex = 2),
id=0.05,
cex = list(x = 2),
scales = list(x = list(cex = 2), y = list(cex = 2)))
This all works fine. But when I try to increase the font size of the axis title
qqmath(fm1,
main = list("QQ-Plot", cex = 2),
xlab = list("x-axis", cex = 2),
id=0.05,
cex = list(x = 2),
scales = list(x = list(cex = 2), y = list(cex = 2)))
I get
Error in qqmath.formula(x = ~x, ylab = "Standardized residuals", xlab = "Standard normal quantiles", :
formal argument "xlab" matched by multiple actual arguments
I gather from this post that this is due to competing arguments in the function call and some ellipsis in the original qqmath.formula object, but surely there has to be an easier way to set the fontsize of the axis titles than reprogramming the original function?!
The lattice system has functions trellis.par.get and trellis.par.set and this can be used to control the fontsize of the xlab and ylab components:
?trellis.par.get
names( trellis.par.get() )
trellis.par.set(par.ylab.text=list(cex=.5))
qqmath(fm1,
main = list("QQ-Plot", cex = 2), id=0.05,
cex=list(left=.55,bottom=.5),
scales = list(x = list(cex = 1), y = list(cex = 1)))
... reduces the size of the ylab. You can find a more complete list of the components and features that can be set from a chart onpage 127 in the "Lattice" book by Sarkar.
I'm currently trying to plot the components found via EM algorithm. However, the estimated densities do not extend fully to the end. It looks like this:
My code is:
plot(EM_data, which=2, xlim= c(0, 80), xlab2= "", yaxt= "n", main2 ="", lwd2=0.8, border = "azure3")
lines(density(EM_data), lty=2, lwd=0.8)
The plot is truncated wether I specify xlim or not. xlim2 is not defined for this type of plot. Where am I going wrong?
The method to plot mixEM only draws within the range of the data, if you want to extend the densities you must build your own function.
Use something like this:
Example data:
library(mixtools)
data(faithful)
attach(faithful)
set.seed(100)
EM_data<-normalmixEM(waiting, arbvar = FALSE, epsilon = 1e-03)
mixtools plot:
plot(EM_data, which=2, xlim= c(30, 110), xlab2= "", yaxt= "n", main2 ="",
lwd2=0.8, border = "azure3")
lines(density(EM_data$x), lty=2, lwd=0.8)
Adaptation by extending densities:
a <- hist(EM_data$x, plot = FALSE)
maxy <- max(max(a$density), 0.3989 * EM_data$lambda/EM_data$sigma)
hist(EM_data$x, prob = TRUE, main = "", xlab = "", xlim= c(30, 110),
ylim = c(0, maxy), yaxt= "n", border = "azure3")
for (i in 1:ncol(EM_data$posterior)) {
curve(EM_data$lambda[i] * dnorm(x, mean = EM_data$mu[i], sd = EM_data$sigma[i]),
col = 1 + i, lwd = 0.8, add = TRUE)
}
lines(density(EM_data$x), lty=2, lwd=0.8)
The first figure in link here shows a very nice example of how to visualise standard error and I would like to replicate that in R.
I'm getting there with the following
set.seed(1)
pop<-rnorm(1000,175,10)
mean(pop)
hist(pop)
#-------------------------------------------
# Plotting Standard Error for small Samples
#-------------------------------------------
smallSample <- replicate(10,sample(pop,3,replace=TRUE)) ; smallSample
smallMeans<-colMeans(smallSample)
par(mfrow=c(1,2))
x<-c(1:10)
plot(x,smallMeans,ylab="",xlab = "",pch=16,ylim = c(150,200))
abline(h=mean(pop))
#-------------------------------------------
# Plotting Standard Error for Large Samples
#-------------------------------------------
largeSample <- replicate(10,sample(pop,20,replace=TRUE))
largeMeans<-colMeans(largeSample)
x<-c(1:10)
plot(x,largeMeans,ylab="",xlab = "",pch=16,ylim = c(150,200))
abline(h=mean(pop))
But I'm not sure how to plot the raw data as they have with the X symbols. Thanks.
Using base plotting, you need to use the arrows function.
In R there is no function (ASAIK) that computes standard error so try this
sem <- function(x){
sd(x) / sqrt(length(x))
}
Plot (using pch = 4 for the x symbols)
plot(x, largeMeans, ylab = "", xlab = "", pch = 4, ylim = c(150,200))
abline(h = mean(pop))
arrows(x0 = 1:10, x1 = 1:10, y0 = largeMeans - sem(largeSample) * 5, largeMeans + sem(largeSample) * 5, code = 0)
Note: the SE's from the data you provided were quite small, so i multiplied them by 5 to make them more obvious
Edit
Ahh, to plot all the points, then perhaps ?matplot, and ?matpoints would be helpful? Something like:
matplot(t(largeSample), ylab = "", xlab = "", pch = 4, cex = 0.6, col = 1)
abline(h = mean(pop))
points(largeMeans, pch = 19, col = 2)
Is this more the effect you're after?
how to
Combine a bar chart and line in single plot in R (from different data sources)?
Say I have two data sources as:
barData<-c(0.1,0.2,0.3,0.4) #In percentage
lineData<-c(100,22,534,52,900)
Note that they may not be in the same scale.
Can I plot both barData and LineData in one plot and make them good looking ?
I cant use ggplot in this case so this is not a duplicated question..
Something like the following:
Maybe this helps as a starting point:
par(mar = rep(4, 4))
barData<-c(0.1,0.2,0.3,0.4) * 100
y <- lineData<-c(100,22,534,900);
x <- barplot(barData,
axes = FALSE,
col = "blue",
xlab = "",
ylab = "",
ylim = c(0, 100) )[, 1]
axis(1, at = x, labels = c("Julia", "Pat", "Max", "Norman"))
ats <- c(seq(0, 100, 15), 100); axis(4, at = ats, labels = paste0(ats, "%"), las = 2)
axis(3, at = x, labels = NA)
par(new = TRUE)
plot(x = x, y = y, type = "b", col = "red", axes = FALSE, xlab = "", ylab = "")
axis(2, at = c(pretty(lineData), max(lineData)), las = 2)
mtext(text="Lines of code by Programmer", side = 3, line = 1)
box()