non-numeric argument to binary operator, AR(1) model - r

I have an exercise to do where I have to run the following AR(1) model:
xi =c+φxi−1+ηi (i=1,...,T)
I know that ni ~ N(0,1) ; x0 ~ N(c/(1-φ),1/(1-φˆ2)); c= 2 ; φ = 0.6
I am trying to do a for loop. My code is the following:
n <- rnorm(T, 0, 1)
c <- 2
phi <- 0.6
x_0 <- rnorm(1,c/(1-phi), 1/(1-phi**2))
v <- vector("numeric", 0)
#for (i in 2:T){
name <- paste("x", i, sep="_")
v <- c(v,name)
v[1] <- c + phi*x_0 + n[1]
v[i] <- c + phi*v[i-1] + n[i]
}
However, I keep getting this error:
Error in phi * v[i - 1] : non-numeric argument to binary operator
I understand what this error is, but I can't find any solutions to solve it. Could someone please enlighten me? How could I assign numeric values to the name vector?
Thank you!

You're defining v as a numeric vector, but then v <- c(v, name) turns v into a character vector since name is character. That's what's causing the error.
If I'm not mistaken, your intent is to assign names to the values in a numeric vector. That's fine, you just need a different approach.
n <- rnorm(t)
c <- 2
phi <- 0.6
x_0 <- rnorm(1, c/(1-phi), 1/(1-phi^2))
v <- c + phi*x_0 + n[1]
for (i in 2:t) {
v[i] <- c + phi*v[i-1] + n[i]
}
names(v) <- paste("x", 1:t, sep="_")
Vectors in R don't have a static size; they're dynamically resized as needed. So even though we're initializing v with a scalar value, it grows to fit each new value in the loop.
The final step is to give v a list of names. This can be accomplished using names(v) <-. Take a look at v now--it has names!
And as an aside, since T is a synonym for TRUE in R, it's best not to use T as a variable name. Thus I've used t here instead.

I guess you seem to need the following. It'll produces 11 elements including the initial x value. You may exclude it later.
set.seed(1237)
t <- 10
n <- rnorm(t, 0, 1)
c <- 2
phi <- 0.6
x0 <- rnorm(1, c/(1-phi), 1/(1-phi**2))
v <- c(x0, rep(0, t))
for(i in 2:length(v)) {
v[i] <- c + phi * v[i-1] + n[i-1]
}
v
[1] 4.967833 4.535847 2.748292 2.792992 5.389548 6.173001 4.526824 3.790483 4.307981 5.442913 4.958193

Related

Remove a vector from another vector

I would like to remove from the vector wine below the vector b=c(1,0).
The result should be d=c(1,1,0).
library(gtools)
wine=c(1,1,1,0,0)
x=combinations(5,2,v=wine,set=FALSE,repeats.allowed=FALSE)
y=matrix(NA,nrow(x),3)
I want to find the complementary matrix y of x.
Thanks for your time.
The following uses a function I have posted here. The function finds where in y the vector x occurs returning an index vector into y.
First, get where b occurs in wine. Then the location is used to remove the found vector.
occurs <- function(x, y) {
m <- length(x)
n <- length(y)
candidate <- seq.int(length = n - m + 1L)
for (i in seq.int(length = m)) {
candidate <- candidate[x[i] == y[candidate + i - 1L]]
}
candidate
}
wine <- c(1,1,1,0,0)
b <- c(1,0)
i <- occurs(b, wine)
d <- wine[-(i + seq(b) - 1L)]
d
#[1] 1 1 0

Creating an Equation with Summation and Constants in R

I was hoping someone could evaluate this code in which I am seeking to create a Random Variable Z using a Uniform Variable U and numerous Constant variables within a summation.
I have U(0,1) and constants a0=2.51..., a2=0.01, and b0=1...b3=0.0013.
and based on if U is > or < than 0.5 we get either Z1 or Z2 in return. My code is below!
w <- (sqrt((-2)*(log(U))))
a[1] <- 2.515517
a[2] <- 0.802853
a[3] <- 0.010328
b[1] <- 1
b[2] <- 1.432788
b[3] <- 0.189269
b[4] <- 0.001308
U <- runif(1)
if(U<=0.5) {
print(ZOne <- ((-w)+((sum(((a[i])*(w^[i])), i=1, 3))/(sum(((b[j])*(w^[j])), j=1, 4)))))
} else {
print(ZTwo <- ((1)-(((-w)+((sum(((a[i])*(w^[i])), i=1, 3))/(sum(((b[j])*(w^[j])), j=1, 4)))))))
}
Hope this makes sense, just for reference ZOne = , when U=<0.5.
ZTwo is (1-ZOne), when U>=0.5.
If you need any clarification please just let me know. Thank you!
*PS, I somehow need to create 1000 of these variables (Z), and figured I would just use the replicate for that.
a <- numeric(3)
b <- numeric(4)
a[1] <- 2.515517
a[2] <- 0.802853
a[3] <- 0.010328
b[1] <- 1
b[2] <- 1.432788
b[3] <- 0.189269
b[4] <- 0.001308
U <- runif(1)
# You can't use U before it exists!
w <- (sqrt((-2)*(log(U))))
# Here assuming w^2 in your equation is w * w etc.
# if not, remove ^(1 : length(a)) and ^(1 : length(b))
ZOne <- (-w) + sum(a * w^(1 : length(a)))/sum(b * w^(1 : length(b)))
if (U<=0.5) {
print(ZOne)
} else {
print(ZTwo <- 1 - ZOne)
}
This seems to be what you are trying to do:
Z <- function(){
w <- sqrt(-2*log(runif(1)))
a <- c(2.515517, 0.802853, 0.010328)
b <- c(1, 1.432788, 0.189269,0.001308)
ZOne <- -w+sum(a*w^(1:3))/sum(b*w^(1:4))
ZTwo <- 1 - ZOne
if(runif(1)<=0.5) {
Zval <- ZOne
} else {
Zval <- ZTwo
}
Zval
}
R operates on whole vectors. Using indices (i,j) is frequently a sign of poor design. Perhaps you might want to spend a certain amount of time reading a tutorial on R programming. By making it a function, you can use Z() to create random variates at will. E.g. something like replicate(1000,Z()) will create 1000 such values.

Non-comformable arguments in R

I am re-writting an algorithm I did in C++ in R for practice called the Finite Difference Method. I am pretty new with R so I don't know all the rules regarding vector/matrix multiplication. For some reason I am getting a non-conformable arguments error when I do this:
ST_u <- matrix(0,M,1)
ST_l <- matrix(0,M,1)
for(i in 1:M){
Z <- matrix(gaussian_box_muller(i),M,1)
ST_u[i] <- (S0 + delta_S)*exp((r - (sigma*sigma)/(2.0))*T + sigma*sqrt(T)%*%Z)
ST_l[i] <- (S0 - delta_S)*exp((r - (sigma*sigma)/(2.0))*T + sigma*sqrt(T)%*%Z)
}
I get this error:
Error in sqrt(T) %*% Z : non-conformable arguments
Here is my whole code:
gaussian_box_muller <- function(n){
theta <- runif(n, 0, 2 * pi)
rsq <- rexp(n, 0.5)
x <- sqrt(rsq) * cos(theta)
return(x)
}
d_j <- function(j, S, K, r, v,T) {
return ((log(S/K) + (r + (-1^(j-1))*0.5*v*v)*T)/(v*(T^0.5)))
}
call_delta <- function(S,K,r,v,T){
return (S * dnorm(d_j(1, S, K, r, v, T))-K*exp(-r*T) * dnorm(d_j(2, S, K, r, v, T)))
}
Finite_Difference <- function(S0,K,r,sigma,T,M,delta_S){
ST_u <- matrix(0,M,1)
ST_l <- matrix(0,M,1)
for(i in 1:M){
Z <- matrix(gaussian_box_muller(i),M,1)
ST_u[i] <- (S0 + delta_S)*exp((r - (sigma*sigma)/(2.0))*T + sigma*sqrt(T)%*%Z)
ST_l[i] <- (S0 - delta_S)*exp((r - (sigma*sigma)/(2.0))*T + sigma*sqrt(T)%*%Z)
}
Delta <- matrix(0,M,1)
totDelta <- 0
for(i in 1:M){
if(ST_u[i] - K > 0 && ST_l[i] - K > 0){
Delta[i] <- ((ST_u[i] - K) - (ST_l[i] - K))/(2*delta_S)
}else{
Delta <- 0
}
totDelta = totDelta + exp(-r*T)*Delta[i]
}
totDelta <- totDelta * 1/M
Var <- 0
for(i in 1:M){
Var = Var + (Delta[i] - totDelta)^2
}
Var = Var*1/M
cat("The Finite Difference Delta is : ", totDelta)
call_Delta_a <- call_delta(S,K,r,sigma,T)
bias <- abs(call_Delta_a - totDelta)
cat("The bias is: ", bias)
cat("The Variance of the Finite Difference method is: ", Var)
MSE <- bias*bias + Var
cat("The marginal squared error is thus: ", MSE)
}
S0 <- 100.0
delta_S <- 0.001
K <- 100.0
r <- 0.05
sigma <- 0.2
T <- 1.0
M <- 10
result1 <- Finite_Difference(S0,K,r,sigma,T,M,delta_S)
I can't seem to figure out the problem, any suggestions would be greatly appreciated.
In R, the %*% operator is reserved for multiplying two conformable matrices. As one special case, you can also use it to multiply a vector by a matrix (or vice versa), if the vector can be treated as a row or column vector that conforms to the matrix; as a second special case, it can be used to multiply two vectors to calculate their inner product.
However, one thing it cannot do is perform scalar multipliciation. Scalar multiplication of vectors or matrices always uses the plain * operator. Specifically, in the expression sqrt(T) %*% Z, the first term sqrt(T) is a scalar, and the second Z is a matrix. If what you intend to do here is multiply the matrix Z by the scalar sqrt(T), then this should just be written sqrt(T) * Z.
When I made this change, your program still didn't work because of another bug -- S is used but never defined -- but I don't understand your algorithm well enough to attempt a fix.
A few other comments on the program not directly related to your original question:
The first loop in Finite_Difference looks suspicious: guassian_box_muller(i) generates a vector of length i as i varies in the loop from 1 up to M, and forcing these vectors into a column matrix of length M to generate Z is probably not doing what you want. It will "reuse" the values in a cycle to populate the matrix. Try these to see what I mean:
matrix(gaussian_box_muller(1),10,1) # all one value
matrix(gaussian_box_muller(3),10,1) # cycle of three values
You also use loops in many places where R's vector operations would be easier to read and (typically) faster to execute. For example, your definition of Var is equivalent to:
Var <- sum((Delta - totDelta)^2)/M
and the definitions of Delta and totDelta could also be written in this simplified fashion.
I'd suggest Googling for "vector and matrix operations in r" or something similar and reading some tutorials. Vector arithmetic in particular is idiomatic R, and you'll want to learn it early and use it often.
You might find it helpful to consider the rnorm function to generate random Gaussians.
Happy R-ing!

R: Generate matrix from function

In R I'm interested in the general case to generate a matrix from a formula such as:
X = some other matrix
Y(i, j) = X(i, j) + Y(i - 1, j - 1)
Unfortunately I can't find how to account for the matrix self-referencing.
Obviously order of execution and bounds checking are factors here, but I imagine these could be accounted for by the matrix orientation and formula respetively.
Thanks.
This solution assumes that you want Y[1,n] == X[1,n] and Y[n,1] == X[n,1]. If not, you can apply the same solution on the sub-matrix X[-1,-1] to fill in the values of Y[-1,-1]. It also assumes that the input matrix is square.
We use the fact that Y[N,N] = X[N,N] + X[N-1, N-1] + ... + X[1,1] plus similar relations for off-diagonal elements. Note that off-diagonal elements are a diagonal of a particular sub-matrix.
# Example input
X <- matrix(1:16, ncol=4)
Y <- matrix(0, ncol=ncol(X), nrow=nrow(X))
diag(Y) <- cumsum(diag(X))
Y[1,ncol(X)] <- X[1,ncol(X)]
Y[nrow(X),1] <- X[nrow(X),1]
for (i in 1:(nrow(X)-2)) {
ind <- seq(i)
diag(Y[-ind,]) <- cumsum(diag(X[-ind,])) # lower triangle
diag(Y[,-ind]) <- cumsum(diag(X[,-ind])) # upper triangle
}
Well, you can always use a for loop:
Y <- matrix(0, ncol=3, nrow=3)
#boundary values:
Y[1,] <- 1
Y[,1] <- 2
X <- matrix(1:9, ncol=3)
for (i in 2:nrow(Y)) {
for (j in 2:ncol(Y)) {
Y[i, j] <- X[i, j] + Y[i-1, j-1]
}
}
If that is too slow you can translate it to C++ (using Rcpp) easily.

Speeding up this tricky matrix calculation

As of now I am computing some features from a large matrix and doing it all in a for-loop. As expected it's very slow. I have been able to vectorize part of the code, but I'm stuck on one part.
I would greatly appreciate some advice/help!
s1 <- MyMatrix #dim = c(5167,256)
fr <- MyVector #vector of length 256
tw <- 5
fw <- 6
# For each point S(t,f) we need the sub-matrix of points S_hat(i,j),
# i in [t - tw, t + tw], j in [f - fw, f + fw] for the feature vector.
# To avoid edge effects, I pad the original matrix with zeros,
# resulting in a matrix of size nobs+2*tw x nfreqs+2*fw
nobs <- dim(s1)[1] #note: this is 5167
nf <- dim(s1)[2] #note: this is 256
sp <- matrix(0, nobs+2*tw, nf+2*fw)
t1 <- tw+1; tn <- nobs+tw
f1 <- fw+1; fn <- nf+fw
sp[t1:tn, f1:fn] <- s1 # embed the actual matrix into the padding
nfeatures <- 1 + (2*tw+1)*(2*fw+1) + 1
fsp <- array(NaN, c(dim(sp),nfeatures))
for (t in t1:tn){
for (f in f1:fn){
fsp[t,f,1] <- fr[(f - f1 + 1)] #this part I can vectorize
fsp[t,f,2:(nfeatures-1)] <- as.vector(sp[(t-tw):(t+tw),(f-fw):(f+fw)]) #this line is the problem
fsp[t,f,nfeatures] <- var(fsp[t,f,2:(nfeatures-1)])
}
}
fspec[t1:tn, f1:fn, 1] <- t(matrix(rep(fr,(tn-t1+1)),ncol=(tn-t1+1)))
#vectorized version of the first feature ^
return(fsp[t1:tn, f1:fn, ]) #this is the returned matrix
I assume that the var feature will be easy to vectorize after the 2nd feature is vectorized

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