Drawing a partially transparent density polygon - r

How can I make this red polygon partially transparent so I can see the points underneath it?
library(ks)
set.seed(1234)
x <- runif(1000) + -150
y <- runif(1000) + 20
my.data <- data.frame(x,y)
my.matrix <- as.matrix(my.data)
my_gps_hpi <- Hpi(x = my.matrix, pilot = "samse", pre = "scale")
my.fhat <- kde(x = my.matrix, compute.cont = TRUE, h = my_gps_hpi,
xmin = c(min(my.data$x), min(my.data$y)),
xmax = c(max(my.data$x), max(my.data$y)),
bgridsize = c(100, 100))
my.contours <- c(75)
contourLevels(my.fhat, cont = my.contours)
contourSizes(my.fhat, cont = my.contours, approx = TRUE)
plot(my.data$x, my.data$y)
plot(my.fhat, lwd = 3, display = "filled.contour", cont = my.contours, add = TRUE)
png(file="transparent_polygon_June21_2021.png")
plot(my.data$x, my.data$y)
plot(my.fhat, lwd = 3, display = "filled.contour", cont = my.contours, add = TRUE)
dev.off()

I think I have figured out a solution by digging around in the source code in the file kde.R.
I made several changes to my code.
Changed my.fhat to fhat because the source code might want fhat.
Changed my.contours to contours for the same reason.
Changed contourLevels(my.fhat, cont = my.contours) to hts <- contourLevels(fhat, cont = contours) for the same reason.
Extracted the col.fun from the source code and changed it to return the color of my choice: col.fun <- function(n) {rgb(255, 0, 0, 127, maxColorValue=255)}.
Modified the plot statement to that shown in the code below.
Here is the modified R code:
setwd('C:/Users/mark_/Documents/ctmm/density_in_R/')
set.seed(1234)
library(ks)
x <- runif(1000) + -150
y <- runif(1000) + 20
my.data <- data.frame(x,y)
my.matrix <- as.matrix(my.data)
gps_hpi <- Hpi(x = my.matrix, pilot = "samse", pre = "scale")
fhat <- kde(x = my.matrix, compute.cont = TRUE, h = gps_hpi,
xmin = c(min(my.data$x), min(my.data$y)),
xmax = c(max(my.data$x), max(my.data$y)),
bgridsize = c(100, 100))
contours <- c(75)
hts <- contourLevels(fhat, cont = contours)
contourSizes(fhat, cont = contours, approx = TRUE)
col.fun <- function(n) {rgb(255, 0, 0, 127, maxColorValue=255)}
col.fun(1)
plot(fhat, lwd = 3, display = "filled.contour", cont = contours, col.fun = col.fun(1), drawpoints=TRUE)
png(file="transparent_polygon_June22_2021.png")
plot(fhat, lwd = 3, display = "filled.contour", cont = contours, col.fun = col.fun(1), drawpoints=TRUE)
dev.off()

Related

Adjust nomogram ticks with (splines) transformation, rms package [R]

I'm using a Cox regression model considering my variable trough splines transformation. All is working nice until the subsequent nomogram... as expected, the scale of my variable is also transformed but I'd like to add some custom ticks inside the region between values 0 and 2 (I guess is the transformed one). Any idea, if you please?
Here's my code...
data <- source("https://pastebin.com/raw/rGtUSTLz")$value
ddist <- datadist(data)
options(datadist = "ddist")
fit <- cph(Surv(time, event) ~ rcs(var, 3), data = data, surv = T, x = T, y = T)
surv <- Survival(fit)
plot(nomogram(fit,
fun = list(function(x) surv(times = 10, lp = x),
function(x) surv(times = 30, lp = x),
function(x) surv(times = 60, lp = x)),
funlabel = paste("c", 1:3), lp = T))
... and these are the real and the desired outputs.
Thanks in advance for your help!
I have had this issue too. My answer is a work around using another package, regplot. Alternatively, if you know what the point values are at the tick marks you want plotted, then you can supply those instead of using the output from regplot. Basically, you need to modify the tick marks and points that are output from the nomogram function and supplied to plot the nomogram.
This method also provides a way to remove points / tick marks by editing the nomogram output.
data <- source("https://pastebin.com/raw/rGtUSTLz")$value
ddist <- datadist(data)
options(datadist = "ddist")
fit <- cph(Surv(time, event) ~ rcs(var, 3), data = data, surv = T, x = T, y = T)
surv <- Survival(fit)
nom1 <- nomogram(fit, fun = list(function(x) surv(times = 10, lp = x),
function(x) surv(times = 30, lp = x),
function(x) surv(times = 60, lp = x)),
funlabel = paste("c", 1:3), lp = T)
library(regplot)
# call regplot with points = TRUE to get output
regplot(fit, fun = list(function(x) surv(times = 10, lp = x),
function(x) surv(times = 30, lp = x),
function(x) surv(times = 60, lp = x)),
funlabel = paste("c", 1:3), points = TRUE)
# look at the points supplied through regplot and take those.
nom1_edit <- nom1
# now we edit the ticks supplied for var and their corresponding point value
nom1_edit[[1]][1] <- list(c(0, 0.06, 0.15, 0.3, 2,4,6,8,10,12,14,16))
nom1_edit[[1]][2] <- list(c(0, 10, 21, 32, 42.41191, 50.63878, 58.86565,
67.09252, 75.31939, 83.54626, 91.77313, 100.00000))
nom1_edit$var$points <- c(0, 10, 21, 32, 42.41191, 50.63878, 58.86565,
67.09252, 75.31939, 83.54626, 91.77313, 100.00000)
# plot the edited nomogram with new points
plot(nom1_edit)

How to set showing space in plotly

Is there any way to set the view and axes so that in my plotly animated 3D graph I can see a specific space and in this space the graph is moving? I'm trying to do an animation which shows how a detector works but right now when i play my animation the view and axes change along with the graph. I know that I can change the range of the axis but somehow it didn't change anything in my output or maybe i was doing something wrong.
Here is my code:
library(plotly)
Sx <- matrix()
Sy <- matrix()
Sz <- matrix()
N <- 360
u = seq(0, pi/2, length.out = 30)
w = seq(0, 2*pi, length.out = N)
datalist = list()
for (i in 1:N) {
Sx = cos(u) * cos(w[i])
Sy = cos(u) * sin(w[i])
Sz = sin(u)
df <- data.frame(Sx, Sy, Sz, t=i)
datalist[[i]] <- df
}
data = do.call(rbind, datalist)
plot_ly(data, x=~Sx, y =~Sy, z=~Sz, frame=~t, type = 'scatter3d', mode = 'lines')
Welcome to stackoverflow!
I'm not sure if I correctly understand your question. However, I think you are looking for a combination of range and aspectratio.
Please check the following:
library(plotly)
Sx <- matrix()
Sy <- matrix()
Sz <- matrix()
N <- 360
u = seq(0, pi/2, length.out = 30)
w = seq(0, 2*pi, length.out = N)
datalist = list()
for (i in 1:N) {
Sx = cos(u) * cos(w[i])
Sy = cos(u) * sin(w[i])
Sz = sin(u)
df <- data.frame(Sx, Sy, Sz, t=i)
datalist[[i]] <- df
}
data = do.call(rbind, datalist)
plot_ly(data, x=~Sx, y =~Sy, z=~Sz, frame=~t, type = 'scatter3d', mode = 'lines') %>%
layout(scene = list(xaxis = list(nticks = 5, range = c(-1, 1)),
yaxis = list(nticks = 5, range = c(-1, 1)),
zaxis = list(nticks = 5, range = c(-1, 1)),
aspectmode='manual',
aspectratio = list(x=1, y=1, z=1)
)) %>% animation_opts(frame = 100)
For further informarion please see this.

Graphical output of density for the function gammamixEM (package mixtools)

I'm using the function gammamixEM from the package mixtools. How can I return the graphical output of density as in the function normalmixEM (i.e., the second plot in plot(...,which=2)) ?
Update:
Here is a reproducible example for the function gammamixEM:
x <- c(rgamma(200, shape = 0.2, scale = 14), rgamma(200,
shape = 32, scale = 10), rgamma(200, shape = 5, scale = 6))
out <- gammamixEM(x, lambda = c(1, 1, 1)/3, verb = TRUE)
Here is a reproducible example for the function normalmixEM:
data(faithful)
attach(faithful)
out <- normalmixEM(waiting, arbvar = FALSE, epsilon = 1e-03)
plot(out, which=2)
I would like to obtain this graphical output of density from the function gammamixEM.
Here you go.
out <- normalmixEM(waiting, arbvar = FALSE, epsilon = 1e-03)
x <- out
whichplots <- 2
density = 2 %in% whichplots
loglik = 1 %in% whichplots
def.par <- par(ask=(loglik + density > 1), "mar") # only ask and mar are changed
mix.object <- x
k <- ncol(mix.object$posterior)
x <- sort(mix.object$x)
a <- hist(x, plot = FALSE)
maxy <- max(max(a$density), .3989*mix.object$lambda/mix.object$sigma)
I just had to dig into the source code of plot.mixEM
So, now to do this with gammamixEM:
x <- c(rgamma(200, shape = 0.2, scale = 14), rgamma(200,
shape = 32, scale = 10), rgamma(200, shape = 5, scale = 6))
gammamixEM.out <- gammamixEM(x, lambda = c(1, 1, 1)/3, verb = TRUE)
mix.object <- gammamixEM.out
k <- ncol(mix.object$posterior)
x <- sort(mix.object$x)
a <- hist(x, plot = FALSE)
maxy <- max(max(a$density), .3989*mix.object$lambda/mix.object$sigma)
main2 <- "Density Curves"
xlab2 <- "Data"
col2 <- 2:(k+1)
hist(x, prob = TRUE, main = main2, xlab = xlab2,
ylim = c(0,maxy))
for (i in 1:k) {
lines(x, mix.object$lambda[i] *
dnorm(x,
sd = sd(x)))
}
I believe it should be pretty straight forward to continue this example a bit, if you want to add the labels, smooth lines, etc. Here's the source of the plot.mixEM function.

bivariate raster plots in R

I have challenge in plotting a bivariate raster data in one plot with one legend for both variables. my first layer is a continuous variable ranging between -2 and 2 while the second layer is a categorical variable (in years form 1980 to 2011). I need help in ploting the data as one rastr plot with a color scheme and legend which shows both variables as shown here. I appreciate your help.
r <- raster(ncols=100, nrows=100)
r[] <- runif(ncell(r))
crs(r) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
r1 <- raster(ncols=100, nrows=100)
r1[] <- 1980:2011
crs(r1) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
dta=stack(r,r1)
See ?raster::plot for examples, or do spplot(dta)
I successfully applied the code from the site you mentioned.
kpacks <- c("classInt", 'raster', 'rgdal',
'dismo', 'XML', 'maps', 'sp')
new.packs <- kpacks[!(kpacks %in% installed.packages()[, "Package"])]
if (length(new.packs))
install.packages(new.packs)
lapply(kpacks, require, character.only = T)
remove(kpacks, new.packs)
r <- raster(ncols = 100, nrows = 100)
r[] <- runif(ncell(r))
crs(r) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
r1 <- raster(ncols = 100, nrows = 100)
r1[] <- sample(1980:2011, 10000, replace = T)
crs(r1) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
dta = stack(r, r1)
plot(dta)
colmat <-
function(nquantiles = 10,
upperleft = rgb(0, 150, 235, maxColorValue = 255),
upperright = rgb(130, 0, 80, maxColorValue = 255),
bottomleft = "grey",
bottomright = rgb(255, 230, 15, maxColorValue = 255),
xlab = "x label",
ylab = "y label") {
my.data <- seq(0, 1, .01)
my.class <- classIntervals(my.data, n = nquantiles, style = "quantile")
my.pal.1 <- findColours(my.class, c(upperleft, bottomleft))
my.pal.2 <- findColours(my.class, c(upperright, bottomright))
col.matrix <- matrix(nrow = 101, ncol = 101, NA)
for (i in 1:101) {
my.col <- c(paste(my.pal.1[i]), paste(my.pal.2[i]))
col.matrix[102 - i, ] <- findColours(my.class, my.col)
}
plot(
c(1, 1),
pch = 19,
col = my.pal.1,
cex = 0.5,
xlim = c(0, 1),
ylim = c(0, 1),
frame.plot = F,
xlab = xlab,
ylab = ylab,
cex.lab = 1.3
)
for (i in 1:101) {
col.temp <- col.matrix[i - 1, ]
points(
my.data,
rep((i - 1) / 100, 101),
pch = 15,
col = col.temp,
cex = 1
)
}
seqs <- seq(0, 100, (100 / nquantiles))
seqs[1] <- 1
col.matrix <- col.matrix[c(seqs), c(seqs)]
}
col.matrix <-
colmat(
nquantiles = 10,
upperleft = "blue",
upperright = "yellow",
bottomleft = "green",
bottomright = "red",
xlab = "My x label",
ylab = "My y label"
)
bivariate.map <-
function(rasterx,
rastery,
colormatrix = col.matrix,
nquantiles = 10) {
quanmean <- getValues(rasterx)
temp <- data.frame(quanmean, quantile = rep(NA, length(quanmean)))
brks <-
with(temp, quantile(temp, na.rm = TRUE, probs = c(seq(0, 1, 1 / nquantiles))))
r1 <-
within(
temp,
quantile <-
cut(
quanmean,
breaks = brks,
labels = 2:length(brks),
include.lowest = TRUE
)
)
quantr <- data.frame(r1[, 2])
quanvar <- getValues(rastery)
temp <- data.frame(quanvar, quantile = rep(NA, length(quanvar)))
brks <-
with(temp, quantile(temp, na.rm = TRUE, probs = c(seq(0, 1, 1 / nquantiles))))
r2 <-
within(temp,
quantile <-
cut(
quanvar,
breaks = brks,
labels = 2:length(brks),
include.lowest = TRUE
))
quantr2 <- data.frame(r2[, 2])
as.numeric.factor <- function(x) {
as.numeric(levels(x))[x]
}
col.matrix2 <- colormatrix
cn <- unique(colormatrix)
for (i in 1:length(col.matrix2)) {
ifelse(is.na(col.matrix2[i]),
col.matrix2[i] <- 1,
col.matrix2[i] <- which(col.matrix2[i] == cn)[1])
}
cols <- numeric(length(quantr[, 1]))
for (i in 1:length(quantr[, 1])) {
a <- as.numeric.factor(quantr[i, 1])
b <- as.numeric.factor(quantr2[i, 1])
cols[i] <- as.numeric(col.matrix2[b, a])
}
r <- rasterx
r[1:length(r)] <- cols
return(r)
}
my.colors = colorRampPalette(c("white", "lightblue", "yellow", "orangered", "red"))
plot(
r,
frame.plot = F,
axes = F,
box = F,
add = F,
legend.width = 1,
legend.shrink = 1,
col = my.colors(255)
)
map(interior = T, add = T)
bivmap <- bivariate.map(r, r1, colormatrix = col.matrix, nquantiles = 10)
# Plot the bivariate map:
plot(
bivmap,
frame.plot = F,
axes = F,
box = F,
add = F,
legend = F,
col = as.vector(col.matrix)
)
col.matrix

R Package Deepnet: Why sae_dnn_train does not work with large data sets

I am trying sae.dnn.train() with 5000 cases, 55-inputs and 3 hidden layers.
Why function nn.predict returns NaN? (vector)
I am using the following command
Nrow <-5000
Ncol <- 55
v <- c(rnorm(Nrow*Ncol,1, 0.5))
x <- matrix(v, nrow=Nrow, ncol=Ncol)
y <- c(rep(1, Nrow/2), rep(0, Nrow/2))
dnn <- sae.dnn.train(x, y, hidden = c(100,90,80),activationfun = "tanh", learningrate = 0.6, momentum = 0.5, learningrate_scale = 1.0,output = "sigm", sae_output = "linear", numepochs = 10, batchsize = 100, hidden_dropout = 0, visible_dropout = 0)
yy <- nn.predict(dnn, x)

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