Getting rid of repeated point in plot - r

I am simulating traders and their actions and am trying to get rid of repeated points in the plot statement. What is the best way to do this? In other words, for ninterval >1 I keep getting a plot where points are repeated.
f1 <- function(n,m,priceinitial,delta,mean, sd, ninterval){
traders <- vector(mode="character", length=n)
traderscurrent <- vector(mode="character", length=n)
price <- vector(mode="numeric")
pricecurrent <- vector(mode="numeric")
for(nint in 1:ninterval)
{
L = floor(rnorm(1,mean,sd))
print(L)
x3 <- runif(2,0,1)
v <- c(0, min(x3), max(x3))
for(i in 1:n)
{
traders[i] <- runif(1,0,1)
if(findInterval(traders[i],v) == sample(c(1,3),1))
{
traders[i] <- "B"
}
else if(findInterval(traders[i],v) == 2)
{
traders[i] <- "N"
}
else {
traders[i] <- "S"
}
}
print(table(traders))
for(step in 1:L)
{
for(i in 1:n)
{
b <- sample(traders[-i], m)
print(b)
table(b)
traderscurrent[i] <- sample(b,1)
}
print(table(traderscurrent))
pricecurrent[step] = priceinitial+length(which(traderscurrent == "B"))*delta-length(which(traderscurrent == "S"))*delta
priceinitial = pricecurrent[step]
traders <- traderscurrent
#print(nint)
#print(step)
}
price <- c(price,pricecurrent)
price <- price[-L]
}
print(price)
plot(price)
}
The call to generate the plot is:
f1(10,2,100,5,10,1,5)
The last three dots don't make sense.

I think this is what you wanted, it's kind of hard to tell though without code comments or more context.
When you did c(price, pricecurrent) you did add a repeated point, for instance in my experiment price got set to 90 on step 1 then pricecurrent was 90 80 on step 2 and the result was 90 90 80.
It looked like you were trying to fix this in the next line, but I'm not sure that it made sense to use -L, which was 9 in my experiment and thus had no effect on the repeated point. I think you want to use step there, but again I have no context so I can't be sure of the use case-specific logic.
f1(10,2,100,5,10,1,5)
f1 <- function(n=10,m=2,priceinitial=100,delta=5,mean=10, sd=1, ninterval=5){
cat("I need to comment my code")
traders <- vector(mode="character", length=n)
traderscurrent <- vector(mode="character", length=n)
price <- vector(mode="numeric")
pricecurrent <- vector(mode="numeric")
for(nint in 1:ninterval)
{
L = floor(rnorm(1,mean,sd))
print(L)
x3 <- runif(2,0,1)
v <- c(0, min(x3), max(x3))
for(i in 1:n)
{
traders[i] <- runif(1,0,1)
if(findInterval(traders[i],v) == sample(c(1,3),1))
{
traders[i] <- "B"
}
else if(findInterval(traders[i],v) == 2)
{
traders[i] <- "N"
}
else {
traders[i] <- "S"
}
}
print(table(traders))
for(step in 1:L)
{
for(i in 1:n)
{
b <- sample(traders[-i], m)
print(b)
table(b)
traderscurrent[i] <- sample(b,1)
}
print(table(traderscurrent))
pricecurrent[step] = priceinitial+length(which(traderscurrent == "B"))*delta-length(which(traderscurrent == "S"))*delta
priceinitial = pricecurrent[step]
traders <- traderscurrent
#print(nint)
#print(step)
}
price <- c(price,pricecurrent)
price <- price[-step]
}
print(price)
plot(price)
}
f()

Related

function generating NA in R

I need help, my function not work correctly when i try make any sum with the results.
have a lot of NA values and I don't know why.
Craps <- function(jogadas){
for (i in 1:jogadas){
comeOut <- sample(1:6,1)+ sample(1:6,1)
if(comeOut %in% c(2,3,12)){
result <- F #False
}else if(comeOut %in% c(7,11)){
}
else{
dados <- sample(1:6,1) + sample(1:6,1)
if(dados == 7){
result <- F
}else {
result <- T
}
while (!(dados %in% c(7,comeOut))){
dados <- sample(1:6,1)+ sample(1:6,1)
}
if(dados == 7)
result <- F
else result <- T
}
print(result)
#probability
prob<-NULL
prob[i] <- result
prob2<-sum(prob)/jogadas
print(prob2)
}
}
Craps(1000)
You put prob=NULL inside your loop, so it will become NULL at each iteration of the loop, just create prob before the loop. Also you forgot one line as noticed in the comments :
Craps <- function(jogadas){
prob<-NULL
for (i in 1:jogadas){
comeOut <- sample(1:6,1)+ sample(1:6,1)
if(comeOut %in% c(2,3,12)){
result <- F #False
}else if(comeOut %in% c(7,11)){
result <- T
}
else{
dados <- sample(1:6,1) + sample(1:6,1)
if(dados == 7){
result <- F
}else {
result <- T
}
while (!(dados %in% c(7,comeOut))){
dados <- sample(1:6,1)+ sample(1:6,1)
}
if(dados == 7)
result <- F
else result <- T
}
print(result)
#probability
prob[i] <- result
prob2<-sum(prob)/jogadas
print(prob2)
}
}

How to find the smallest circumcircle of an irregular polygon on R project?

I was wondering about how to find the smallest circumcircle of an irregular polygon. I've worked with spatial polygons in R.
I want to reproduce some of the fragstats metrics in a vector mode because I had hard times with the package 'landscapemetrics' for a huge amount of data. In specific I would like to implement the circle (http://www.umass.edu/landeco/research/fragstats/documents/Metrics/Shape%20Metrics/Metrics/P11%20-%20CIRCLE.htm). So far, I could not find the formula or script for the smallest circumcircle.
All your comments are more than welcome.
Than you
As I mentioned in a comment, I don't know of existing R code for this, but a brute force search should be fast enough if you don't have too many points that need to be in the circle. I just wrote this one. The center() function is based on code from Wikipedia for drawing a circle around a triangle; circumcircle() is the function you want, found by brute force search through all circles that pass through 2 or 3 points in the set. On my laptop it takes about 4 seconds to handle 100 points. If you have somewhat bigger sets, you can probably get tolerable results by translating to C++, but it's an n^4 growth rate, so you'll need a better solution
for a really large set.
center <- function(D) {
if (NROW(D) == 0)
matrix(numeric(), ncol = 2)
else if (NROW(D) == 1)
D
else if (NROW(D) == 2) {
(D[1,] + D[2,])/2
} else if (NROW(D) == 3) {
B <- D[2,] - D[1,]
C <- D[3,] - D[1,]
Dprime <- 2*(B[1]*C[2] - B[2]*C[1])
if (Dprime == 0) {
drop <- which.max(c(sum((B-C)^2), sum(C^2), sum(B^2)))
center(D[-drop,])
} else
c((C[2]*sum(B^2) - B[2]*sum(C^2))/Dprime,
(B[1]*sum(C^2) - C[1]*sum(B^2))/Dprime) + D[1,]
} else
center(circumcircle(D))
}
radius <- function(D, U = center(D))
sqrt(sum((D[1,] - U)^2))
circumcircle <- function(P) {
n <- NROW(P)
if (n < 3)
return(P)
P <- P[sample(n),]
bestset <- NULL
bestrsq <- Inf
# Brute force search
for (i in 1:(n-1)) {
for (j in (i+1):n) {
D <- P[c(i,j),]
U <- center(D)
rsq <- sum((D[1,] - U)^2)
if (rsq >= bestrsq)
next
failed <- FALSE
for (k in (1:n)[-j][-i]) {
Pk <- P[k,,drop = FALSE]
if (sum((Pk - U)^2) > rsq) {
failed <- TRUE
break
}
}
if (!failed) {
bestset <- c(i,j)
bestrsq <- rsq
}
}
}
# Look for the best 3 point set
for (i in 1:(n-2)) {
for (j in (i+1):(n-1)) {
for (l in (j+1):n) {
D <- P[c(i,j,l),]
U <- center(D)
rsq <- sum((D[1,] - U)^2)
if (rsq >= bestrsq)
next
failed <- FALSE
for (k in (1:n)[-l][-j][-i]) {
Pk <- P[k,,drop = FALSE]
if (sum((Pk - U)^2) > rsq) {
failed <- TRUE
break
}
}
if (!failed) {
bestset <- c(i,j,l)
bestrsq <- rsq
}
}
}
}
P[bestset,]
}
showP <- function(P, ...) {
plot(P, asp = 1, type = "n", ...)
text(P, labels = seq_len(nrow(P)))
}
showD <- function(D) {
U <- center(D)
r <- radius(D, U)
theta <- seq(0, 2*pi, len = 100)
lines(U[1] + r*cos(theta), U[2] + r*sin(theta))
}
n <- 100
P <- cbind(rnorm(n), rnorm(n))
D <- circumcircle(P)
showP(P)
showD(D)
This shows the output

For optimx() with method of bobyqa, how to set the initial value

I was trying to maximize my Likelihood with the R package 'optimx'. Here is my code. With the initial value (5,5) and (1,1), I got different Maximized likelihood. I also have tried different method like 'Nelder=Mead', but the estimated log likelihood are different under different methods...
library('optimx')
n=225
X = matrix(runif(225),ncol=1)
e2 = matrix(runif(225,0,2),ncol=1)
set.seed(123)
This is the function to generate some data I will use
get_mls_basis<- function(p){
depth <- ceiling(runif(1)*p)
knot <- matrix(rep(0,depth+1),ncol=1)
lr <- runif(1) > 0.5
x <- matrix(rep(0,n),ncol=1)
not_finished <- 1
while (not_finished == 1) {
data_indx = ceiling(runif(1)*n)
var = matrix(rep(0,depth),ncol=1)
for (j in 1:depth) {
not_ok <- 1
while (not_ok == 1) {
ind <- ceiling(runif(1)*p)
if (!is.element (ind,var[1:j]))
{
var[j] <- ind
not_ok <- 0
}
}
}
x_v <- as.matrix(X[data_indx, var])
knot[1:depth] <- rgamma(depth,1,1)
knot[1:depth] <- knot[1:depth] / sqrt(sum(knot^2))
knot[depth+1] <- -x_v %*% knot[1:depth]
ones <- matrix(rep(1,n),ncol=1)
temp <- as.matrix(cbind(X[,var], ones)) %*% knot
if (lr == 0) {
for (i in 1:n)
{
temp[i] <- max(0,temp[i])
}
}
else {
for (i in 1:n)
{
temp[i] <- min(0,temp[i])
}
}
x <- temp
not_finished <- all(x==0)
}
mx <- mean(x)
stx <- sd(x)
x <- (x-mx)/stx
x
}
This is my log likelihood
Lik1<-function(theta, basis){
theta0=theta[1]
theta1=theta[2]
L=-n/2*log(theta0)-sum(basis/2)*log(theta1)-0.5/theta0*sum(e2/theta1^basis)
return(L)
}
basis1=get_mls_basis(1)
Here I used 5 as initial value
optimx(par=c(5,5), Lik1,
basis=basis1,method='bobyqa',control = list(maximize=TRUE))

Repeating a function multiple times using a for loop

A have code that creates a random graph in the form of a matrix. Now I would like it to create many, say m, random graphs so the output is m matrices. I am trying to do this with a for loop. This would be my preferred method however I am open to other suggestions (apply family?). Here is my code, where n is the number of nodes/vertices the graph has and beta is the amount of preferential attachment (keep this between 0 and 1.5)
multiplerandomgraphs <- function(n, beta, m) {
for(k in 1:m) {
randomgraph <- function(n, beta) {
binfunction <- function(y) {
L <- length(y)
x <- c(0, cumsum(y))
U <- runif(1, min = 0 , max = sum(y))
for(i in 1:L) {
if(x[i] <= U && x[i+1] > U){
return(i)
}
}
}
mat <- matrix(0,n,n)
mat[1,2] <- 1
mat[2,1] <- 1
for(i in 3:n) {
degvect <- colSums(mat[ , (1:(i-1))])
degvect <- degvect^(beta)
j <- binfunction(degvect)
mat[i,j] <- 1
mat[j,i] <- 1
}
return(mat)
}
}
}
You can define your randomgraph function as randomgraph <- function(i, n, beta) {} with the body the same as your definition, leaves the parameter i as a dummy parameter. And then use apply function as listOfMatrix <- lapply(1:m, randomgraph, n, beta) which return a list of matrix.

A lot of variables in lm regression and large list

My task is to do some researches in dummy variables. Here is a R code:
parameters_estimation2 <- function(n)
{
age <- sample(20:40, n, replace=TRUE)
male <- sample(0:1, n, replace=TRUE)
education <- sample(0:6, n, replace=TRUE)
experience <- floor(rexp(n, 0.2))
for(i in 1:n)
{if(experience[i]>15) {
experience[i] <- floor(rexp(1, 0.2))
if(experience[i]>15) { i <- i-1 }
}}
sqexperience <- experience*experience
e <- rnorm(n, 0, 4)
B0 <- -200; B1 <- 15; B2 <- 100; B3 <-10; B4 <- 5; B5 <-20;
wage <- B0 + B1*age + B2*male + B3*education+ B4*experience+ B5*sqexperience+e
#Dummy making
expe1 <- c(rep(0,n)); expe2 <- c(rep(0,n)); expe3 <- c(rep(0,n)); expe4 <- c(rep(0,n));
expe5 <- c(rep(0,n)); expe6 <- c(rep(0,n)); expe7 <- c(rep(0,n)); expe8 <- c(rep(0,n));
expe9 <- c(rep(0,n)); expe10 <- c(rep(0,n)); expe11 <- c(rep(0,n)); expe12 <- c(rep(0,n));
expe13 <- c(rep(0,n)); expe14 <- c(rep(0,n)); expe15 <- c(rep(0,n));
for(i in 1:n)
{
if(experience[i]==1) { expe1[i] <-1
} else if(experience[i]==2) { expe2[i] <-1
} else if(experience[i]==3) { expe3[i] <-1
} else if(experience[i]==4) { expe4[i] <-1
} else if(experience[i]==5) { expe5[i] <-1
} else if(experience[i]==6) { expe6[i] <-1
} else if(experience[i]==7) { expe7[i] <-1
} else if(experience[i]==8) { expe8[i] <-1
} else if(experience[i]==9) { expe9[i] <-1
} else if(experience[i]==10) { expe10[i] <-1
} else if(experience[i]==11) { expe11[i] <-1
} else if(experience[i]==12) { expe12[i] <-1
} else if(experience[i]==13) { expe13[i] <-1
} else if(experience[i]==14) { expe14[i] <-1
} else if(experience[i]==15) { expe15[i] <-1
}}
regression<-lm(wage~age+male+education+expe1+expe2+expe3+expe4+expe5+expe6+expe7+expe8+expe9+expe10+expe11+expe12+expe13+expe14+expe15)
return(summary(regression)$coefficients[,"Estimate"])
}
times <- 1000
size <- rep(200, times)
koeficientai1 <-mapply(parameters_estimation2, size)
blah <- as.data.table(koeficientai1)
beta0sample200d <- mean(koeficientai1[,"(Intercept)"])
And the problem is that in last line I get:
Error in koeficientai1[, "(Intercept)"] : incorrect number of dimensions
I think the problem is that koeficientai1 is large list. But then I'm trying another lm regression with just 5 variables, the code is working and I get simple data frame.
Try replacing the last line by
beta0sample200d <- mean(sapply(koeficientai1, function(x) x["(Intercept)"]))
koeficientai1 is a list, but you try to access it as a data.frame, hence the error message.
sapply extracts the element named (Intercept) from each list element in koeficientai1(in your case each list element is a named vector) and returns a vector that contains the results.

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