I am trying to generate a plot for Lognormal Probability Density in R, with 3 different means log and standards deviation log. I have tried the following, but my graph is so ugly and does not look good at all.
x<- seq(0,10,length = 100)
a <- dlnorm(x, meanlog = 0, sdlog = 1, log = FALSE)
b <- dlnorm(x, meanlog = 0, sdlog = 1.5, log = FALSE)
g <- dlnorm(x, meanlog = 1.5, sdlog = 0.2, log = FALSE)
plot(x,a, lty=5, col="blue", lwd=3)
lines(x,b, lty=2, col = "red")
lines(x,g, lty=4, col = "green")
I even was trying to add legend on the right top for each mean log and standard deviation log, but it would not work with me. I was wondering if someone could guide me out with that.
Right top of the graph
There is really nothing wrong in your code. You just forgot to:
use type = "l" in plot;
set a good ylim to hold all lines.
Here is a simple solution with matplot:
matplot(x, cbind(a,b,g), type = "l", ylab = "density", main = "log-normal",
col = 1:3, lty = 1:3)
To add legend, use
legend("topright",
legend = c("mu = 0, sd = 1", "mu = 0, sd = 1.5", "mu = 1.5, sd = 0.2"),
col = 1:3,
lty = 1:3)
You can also read ?plotmath for adding expressions. Try changing the legend argument above to:
legend = c(expression(ln(y) %~% N(0,1)),
expression(ln(y) %~% N(0,1.5)),
expression(ln(y) %~% N(1.5,0.2)))
Related
I am using base R, and had a code for teaching about normal distribution, and have ran the code successfully many times.
Now, however, when I superimpose the normal density curve, it doesn't seem to function properly.
Here is an example code:
set.seed(100)
data <- rnorm(1000, mean = 0, sd = 1)
hist(data, main = "Normal Distribution", xlab = "X", ylab = "Frequency", col = "444", xlim=c(-4,4))
Now I try to superimpose a density curve over the plot, using the density() command:
lines(density(data), col = "red", lwd = 2)
As you see, the line is flat, and I am perplexed as to why? So I tried another method:
x <- seq(-4, 4, length.out = 100)
lines(x, dnorm(x, mean = 0, sd = 1), col = "red", lwd = 2)
But I get the same result.
Any thoughts why it's not working properly?
The answer came to me thanks to one of the users comments.
Using base R, the hist() function will not plot a probability function by default, which is what needed here. Thus, if I set freq=F the code will worked.
Here is the correct answer:
set.seed(100)
data <- rnorm(1000, mean = 0, sd = 1)
hist(data, main = "Normal Distribution", xlab = "X", ylab = "Frequency", col = "444", xlim=c(-4,4), freq = F)
lines(density(data), col ='777', lwd = 2)
I compare two treatments A and B. The objective is to show that A is not inferior to B. The non inferiority margin delta =-2
After comparing Treatment A - Treatment B I have these results
Mean difference and 95% CI = -0.7 [-2.1, 0.8]
I would like to plot this either with a package or manually. I have no idea how to do it.
Welch Two Sample t-test
data: mydata$outcome[mydata$traitement == "Bras S"] and mydata$outcome[mydata$traitement == "B"]
t = 0.88938, df = 258.81, p-value = 0.3746
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.133224 0.805804
sample estimates:
mean of x mean of y
8.390977 9.054688
I want to create this kind of plot:
You could abstract the relevant data from the t.test results and then plot in base R using segments and points to plot the data and abline to draw in the relevant vertical lines. Since there were no reproducible data, I made some up but the process is generally the same.
#sample data
set.seed(123)
tres <- t.test(runif(10), runif(10))
# get values to plot from t test results
ci <- tres$conf.int
ests <- tres$estimate[1] - tres$estimate[2]
# plot
plot(x = ci, ylim = c(0,2), xlim = c(-4, 4), type = "n", # blank plot
bty = "n", xlab = "Treatment A - Treatment B", ylab = "",
axes = FALSE)
points(x = ests, y = 1, pch = 20) # dot for point estimate
segments(x0 = ci[1], x1 = ci[2], y0 = 1) #CI line
abline(v = 0, lty = 2) # vertical line, dashed
abline(v = 2, lty = 1, col = "darkblue") # vertical line, solid, blue
axis(1, col = "darkblue") # add in x axis, blue
EDIT:
If you wanted to more accurately recreate your figure with the x axis in descending order and using your statement "Mean difference and 95% CI = -0.7 [-2.1, 0.8]", you can do the following manipulations to the above approach:
diff <- -0.7
ci <- c(-2.1, 0.8)
# plot
plot(1, xlim = c(-4, 4), type = "n",
bty = "n", xlab = "Treatment A - Treatment B", ylab = "",
axes = FALSE)
points(x = -diff, y = 1, pch = 20)
segments(x0 = -ci[2], x1 = -ci[1], y0 = 1)
abline(v = 0, lty = 2)
abline(v = 2, lty = 1, col = "darkblue")
axis(1, at = seq(-4,4,1), labels = seq(4, -4, -1), col = "darkblue")
I have created the following fanchart using the fanplot package. I'm trying to add axis ticks and labels to the y axis, however it's only giving me the decimals and not the full number. Looking for a solution to display the full number (e.g 4.59 and 4.61) on the y axis
I am also unsure of how to specify the breaks and number of decimal points for the labels on the y-axis using plot(). I know doing all of this in ggplot2 it would look something like this scale_y_continuous(breaks = seq(min(data.ts$Index),max(data.ts$Index),by=0.02)) . Any ideas on how to specify the breaks in the y axis as well as the number of decimal points using the base plot() feature in R?
Here is a reproductible of my dataset data.ts
structure(c(4.6049904235401, 4.60711076016453, 4.60980084146652,
4.61025389170935, 4.60544515681515, 4.60889021700954, 4.60983993107244,
4.61091608826696, 4.61138799159174, 4.61294431148318, 4.61167545843765,
4.61208284263432, 4.61421991328081, 4.61530485425155, 4.61471465043043,
4.6155992084451, 4.61195799200607, 4.61178486640435, 4.61037927954796,
4.60744590947049, 4.59979957741728, 4.59948551500254, 4.60078678080182,
4.60556092645471, 4.60934962087565, 4.60981147563749, 4.61060477704678,
4.61158365084251, 4.60963435263623, 4.61018215733317, 4.61209710959768,
4.61231368335184, 4.61071363571141, 4.61019496497916, 4.60948652606191,
4.61068813487859, 4.6084092003352, 4.60972706132393, 4.60866915174087,
4.61192565195909, 4.60878767339377, 4.61341471281265, 4.61015272152397,
4.6093479714315, 4.60750965935653, 4.60768790690338, 4.60676463096309,
4.60746490411374, 4.60885670935448, 4.60686846708382, 4.60688947889575,
4.60867708110485, 4.60448791268212, 4.60387348166032, 4.60569806689426,
4.6069320880709, 4.6087143894128, 4.61059688801283, 4.61065399116698,
4.61071421014339), .Tsp = c(2004, 2018.75, 4), class = "ts")
and here is a reproductible of the code I'm using
# # Install and Load Packages
## pacman::p_load(forecast,fanplot,tidyverse,tsbox,lubridate,readxl)
# Create an ARIMA Model using the auto.arima function
model <- auto.arima(data.ts)
# Simulate forecasts for 4 quarters (1 year) ahead
forecasts <- simulate(model, n=4)
# Create a data frame with the parameters needed for the uncertainty forecast
table <- ts_df(forecasts) %>%
rename(mode=value) %>%
mutate(time0 = rep(2019,4)) %>%
mutate(uncertainty = sd(mode)) %>%
mutate(skew = rep(0,4))
y0 <- 2019
k <- nrow(table)
# Set Percentiles
p <- seq(0.05, 0.95, 0.05)
p <- c(0.01, p, 0.99)
# Simulate a qsplitnorm distribution
fsval <- matrix(NA, nrow = length(p), ncol = k)
for (i in 1:k)
fsval[, i] <- qsplitnorm(p, mode = table$mode[i],
sd = table$uncertainty[i],
skew = table$skew[i])
# Create Plot
plot(data.ts, type = "l", col = "#75002B", lwd = 4,
xlim = c(y0 - 2,y0 + 0.75), ylim = range(fsval, data.ts),
xaxt = "n", yaxt = "n", ylab = "",xlab='',
main = '')
title(ylab = 'Log AFSI',main = 'Four-Quarter Ahead Forecast Fan - AFSI',
xlab = 'Date')
rect(y0 - 0.25, par("usr")[3] - 1, y0 + 2, par("usr")[4] + 1,
border = "gray90", col = "gray90")
fan(data = fsval, data.type = "values", probs = p,
start = y0, frequency = 4,
anchor = data.ts[time(data.ts) == y0 - .25],
fan.col = colorRampPalette(c("#75002B", "pink")),
ln = NULL, rlab = NULL)
# Add axis labels and ticks
axis(1, at = y0-2:y0 + 2, tcl = 0.5)
axis(1, at = seq(y0-2, y0 + 2, 0.25), labels = FALSE, tcl = 0.25)
abline(v = y0 - 0.25, lty = 1)
abline(v = y0 + 0.75, lty = 2)
axis(2, at = range(fsval, data.ts), las = 2, tcl = 0.5)
range(blah) will only return two values (the minimum and maximum). The at parameter of axis() requires a sequence of points at which you require axis labels. Hence, these are the only two y values you have on your plot. Take a look at using pretty(blah) or seq(min(blah), max(blah), length.out = 10).
The suggestions of #Feakster are worth looking at, but the problem here is that the y-axis margin isn't wide enough. You could do either of two things. You could round the labels so they fit within the margins, for example you could replace this
axis(2, at = range(fsval, data.ts), las = 2, tcl = 0.5)
with this
axis(2, at = range(fsval, data.ts),
labels = sprintf("%.3f", range(fsval, data.ts)), las = 2, tcl = 0.5)
Or, alternatively you could increase the y-axis margin before you make the plot by specifying:
par(mar=c(5,5,4,2)+.1)
plot(data.ts, type = "l", col = "#75002B", lwd = 4,
xlim = c(y0 - 2,y0 + 0.75), ylim = range(fsval, data.ts),
xaxt = "n", yaxt = "n", ylab = "",xlab='',
main = '')
Then everything below that should work. The mar element of par sets the number of lines printed in the margin of each axis. The default is c(5,4,4,2).
I have plotted five graphs and a legend. The graphs work just fine, however the legens disappears without an error.
My preview in RStudio looks like this
When I zoom in, the area where the legend should be is blank.
I use the following code:
opar <- par (no.readonly = TRUE)
par (mfrow = c(3, 2))
library(deSolve)
# Plot A
LotVmod <- function (Time, State, Pars) {
with(as.list(c(State, Pars)), {
dx = (b*x) - (b*x*x/K) - (y*(x^k/(x^k+C^k)*(l*x/(1+l*h*x))))
dy = (y*e*(x^k/(x^k+C^k)*(l*x/(1+l*h*x)))) - (m*y)
return(list(c(dx, dy)))
})
}
Pars <- c(b = 1.080, e = 2.200, K = 130.000, k = 20.000, l = 2.000,
h = 0.030, C = 2.900, m = 0.050)
State <- c(x = 0.25, y = 2.75)
Time <- seq(1, 9, by = 1)
out <- as.data.frame(ode(func = LotVmod, y = State, parms = Pars, times = Time))
matplot(out[,-1], type = "l", xlim = c(1, 9), ylim = c(0, 45),
xlab = "time",
ylab = "population",
main = "Compartment A")
mtext ( "Coefficient of Variance 4.96", cex = 0.8 )
x <- c(# Validation data)
y <- c(# Validation data)
lines (Time, x, type="l", lty=1, lwd=2.5, col="black")
lines (Time, y, type="l", lty=1, lwd=2.5, col="red")
# Legend
plot.new()
legend("center", c(expression (italic ("F. occidentalis")*" observed"),
expression (italic ("M. pygmaeus")*" observed"),
expression (italic ("F. occidentalis")*" simulated"),
expression (italic ("M. pygmaeus")*" simulated")),
lty = c(1, 1, 1, 2),
col = c(1, 2, 1, 2),
lwd = c(2.5, 2.5, 1, 1),
box.lwd = 0, bty = "n")
# Plot C to F = same as A
par(opar)
My output doesn't give an error. I have used the exact same code before without any trouble, thus I restarted R, removed all objects, cleared all plots and restarted both RStudio and my computer.
Try to add xpd=TRUE in your legend statement. I.e.
legend("center", c(expression (italic ("F. occidentalis")*" observed"),
expression (italic ("M. pygmaeus")*" observed"),
expression (italic ("F. occidentalis")*" simulated"),
expression (italic ("M. pygmaeus")*" simulated")),
lty = c(1, 1, 1, 2),
col = c(1, 2, 1, 2),
lwd = c(2.5, 2.5, 1, 1),
box.lwd = 0, bty = "n", xpd=TRUE)
By default, the legend is cut off by the plotting region. This xpd parameter enables plotting outside the plot region. See e.g. ?par for more on xpd.
This is due to how the plot canvas is set up and how rescaling that device works. The way you do it, you add the legend in the plotting region of the top right plot. The plotting region is however not the complete device, but only the part inside the space formed by the axes. If you rescale, that plotting region will be rescaled as well. The margins around the plotting region don't change size though, so zooming in makes your plotting region so small that it doesn't fit the legend any longer. It is hidden by the margins around the plotting region.
For that reason AEBilgrau is very right you need to add xpd = TRUE. This allows the legend to extend outside of the plotting region, so it doesn't disappear behind the margins when resizing the plotting device.
I want to create 3 plots for illustration purposes:
- normal distribution
- right skewed distribution
- left skewed distribution
This should be an easy task, but I found only this link, which only shows a normal distribution. How do I do the rest?
If you are not too tied to normal, then I suggest you use beta distribution which can be symmetrical, right skewed or left skewed based on the shape parameters.
hist(rbeta(10000,5,2))
hist(rbeta(10000,2,5))
hist(rbeta(10000,5,5))
Finally I got it working, but with both of your help, but I was relying on this site.
N <- 10000
x <- rnbinom(N, 10, .5)
hist(x,
xlim=c(min(x),max(x)), probability=T, nclass=max(x)-min(x)+1,
col='lightblue', xlab=' ', ylab=' ', axes=F,
main='Positive Skewed')
lines(density(x,bw=1), col='red', lwd=3)
This is also a valid solution:
curve(dbeta(x,8,4),xlim=c(0,1))
title(main="posterior distrobution of p")
just use fGarch package and these functions:
dsnorm(x, mean = 0, sd = 1, xi = 1.5, log = FALSE)
psnorm(q, mean = 0, sd = 1, xi = 1.5)
qsnorm(p, mean = 0, sd = 1, xi = 1.5)
rsnorm(n, mean = 0, sd = 1, xi = 1.5)
** mean, sd, xi location parameter mean, scale parameter sd, skewness parameter xi.
Examples
## snorm -
# Ranbdom Numbers:
par(mfrow = c(2, 2))
set.seed(1953)
r = rsnorm(n = 1000)
plot(r, type = "l", main = "snorm", col = "steelblue")
# Plot empirical density and compare with true density:
hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue")
box()
x = seq(min(r), max(r), length = 201)
lines(x, dsnorm(x), lwd = 2)
# Plot df and compare with true df:
plot(sort(r), (1:1000/1000), main = "Probability", col = "steelblue",
ylab = "Probability")
lines(x, psnorm(x), lwd = 2)
# Compute quantiles:
round(qsnorm(psnorm(q = seq(-1, 5, by = 1))), digits = 6)