how to solve error non-numeric argument to binary operation - r

I want to calculate the stock return but I got error in
return=function(x)
{
n=length(x)
x=matrix(x,nrow=n,ncol=1)
return_data=matrix(nrow=n-1,ncol=1)
for(i in 1:n-1)
{
return_data[i]=log(x[i+1,]/x[i,])
}
return_data
}
R_JSMR=return(JSMR)
Error in x[i + 1, ]/x[i, ] : non-numeric argument to binary operator
how do I solve this? please help me, thank you very much :)


The error is to have the for loop in 1:n-1 instead of 1:(n-1). Here is the function corrected and a vectorized version of it.
log_returns <- function(x) {
n <- length(x)
return_data <- matrix(nrow = n - 1, ncol = 1)
for(i in 1:(n-1)) {
return_data[i] <- log(x[i+1]/x[i])
}
return_data
}
log_returns2 <- function(x) {
return_data <- log(x[-1]/x[-length(x)])
matrix(return_data, ncol = 1)
}
JSMR <- 1:10
R_JSMR <- log_returns(JSMR)
R_JSMR2 <- log_returns2(JSMR)
identical(R_JSMR,R_JSMR2)
#[1] TRUE
If you don't need to return a matrix, here are two one-liners.
log_returns3 <- function(x) log(x[-1]/x[-length(x)])
log_returns4 <- function(x) diff(log(x))

Related

R median function from scratch

I am a R beginner and I tried to make a median function from scratch.
Here is my code:
mymedian <- function(x) {
len <- length(x)
sorted <- sort(x)
if (len %% 2 == 0) {
med1 <- sorted[len / 2]
med2 <- sorted[(len + 1) %/% 2]
result <- sorted[med1 + med2 / 2]
return(result)
} else {
result <- sorted[(len + 1)/2]
return(result)
}
}
Im getting "NA" output. I couldn't find where the problem is.

Main issue is you're trying to index your sorted vector with a non-integer (e.g., 168.5). Compare your function to this:
mymedian <- function(x){
len <- length(x)
sorted <-sort(x)
if(len%%2==0){
i <- len/2
med1<-sorted[i]
med2 <- sorted[i+1]
result <- sum(med1,med2)/2
return(result)
}else{
result<-sorted[(len+1)/2]
return(result)
}
}

Unknown usages of functions in source code of predict.naiveBayes method in e1071 package

I don't understand, how method predict.naiveBayes works, if there are two misspelled usages of functions, i.e., isnumeric[attribs[v]] and islogical[attribs[v]].
In my opinion, there should be is.numeric(attribs[v]) and is.logical(attribs[v]), respectively.
Code below:
...
L <- sapply(1:nrow(newdata), function(i) {
ndata <- newdata[i, ]
L <- log(object$apriori) + apply(log(sapply(seq_along(attribs),
function(v) {
nd <- ndata[attribs[v]]
if (is.na(nd)) rep(1, length(object$apriori)) else {
prob <- if (isnumeric[attribs[v]]) {
msd <- object$tables[[v]]
msd[, 2][msd[, 2] <= eps] <- threshold
dnorm(nd, msd[, 1], msd[, 2])
} else object$tables[[v]][, nd + islogical[attribs[v]]]
prob[prob <= eps] <- threshold
prob
}
})), 1, sum)
if (type == "class")
L
else {
## Numerically unstable:
## L <- exp(L)
## L / sum(L)
## instead, we use:
sapply(L, function(lp) {
1/sum(exp(L - lp))
})
}
})
...
Everything works fine, when I am using naive Bayes classifier from package, but it is rather strange, due to these inconsistencies. Can anyone explain me my doubts?

Just two lines above your code excerpt there is basically what you expect:
isnumeric <- sapply(newdata, is.numeric)
islogical <- sapply(newdata, is.logical)
That is, isnumeric and islogical are not functions, they are validly defined logical vectors.

How to add two functions to a new function in R

I met a problem adding two functions together to a new function in R. For example, fun_1<-function(w)... fun_2<-function(w)... now I need to get a function fun(w)=fun_1(w)+fun_2(w) how could I do it?

Do you mean this ?
fun_1 <- function(x){
x ^ 2
}
fun_2 <- function(x){
x + 1
}
fun_3 <- function(x){
fun_1(x) + fun_2(x)
}
fun_3(1)
returns 3
k <- NA
fun <- function(w){
for (i in 1:100){
k[i] <- (-i/100)^2 + exp(w)
}
sum(k)
}
fun(1)
returns 305.6632

You can use get with envir = parent.frame() and just use paste to specify the function name.
# define functions
for(i in 1:100) assign(paste0('fun',i), function(w) (-i/100)^2+exp(w) )
# sum them
sum.fun <- function(x){
out <- 0
for(i in 1:100){
fun <- get(paste0('fun',i), envir = parent.frame())
out <- out + fun(x)
}
out
}
sum.fun(2)

R for loop to populate new variable

Dear Stackoverflow community,
I am at a loss with my for loop and I'm pretty sure it is a minor problem. I did browse through the loop questions that have already been asked...
Here is what I have so far:
xvals <- as.matrix(seq(1,10, 1))
I would like to create ovals based on ovals
yvals <- for (i in 1:nrow(xvals)){
p <- 2.69/(1+2.69)
if (i == 1){
yvals[i,] <- round(p, 2)}
else {
yvals[i,] <- round(1-(p)^i, 2)}
}
Unfortunately, this thing keeps throwing an error
Error in 1:nrow(xvals) : argument of length 0
When I change xvals to matrix, I get a different error:
Error in yvals[i, ] <- round(p, 2) :
incorrect number of subscripts on matrix

Without the loop
xvals <- 1:10
p <- 2.69/(1+2.69)
yvals <- 1-p^xvals
yvals[1] <- p
yvals <- round(yvals, 2)

I think you should first create empty matrix to achieve loop to work. Try this, maybe it helps you
xvals <- as.matrix(seq(1,10, 1))
xvals
yvals <- matrix(0, nrow=nrow(xvals),ncol=ncol(xvals))
yvals
for (i in 1:nrow(xvals)){
p <- 2.69/(1+2.69)
if (i == 1){
yvals[i,] <- round(p, 2)}
else {
yvals[i,] <- round(1-(p)^i, 2)}
}
yvals

Do you actually want a matrix or will a vector do?
yvals <- replicate(10,0)
p <- 2.69/(1+2.69)
for (i in 1:length(yvals)){
if (i == 1){
yvals[i] <- round(p, 2)
}
else {
yvals[i] <- round(1-(p)^i, 2)}
}
xvals <- 1:10
plot(xvals, yvals)

If error in loop create vector of "n" and continue

I have a loop in R which tests every possible combination of ARIMA with specific conditions and tests the lags. However during the loop there is an error
Error in optim(init[mask], armafn, method = optim.method, hessian = TRUE, :
non-finite finite-difference value [1]
When this error occurs I want it to create a vector of "n" which will be put into a matrix with the rest of the models. I have tried tryCatch but this for some reason stops the rest of the iterations from happening.
Here is my code:
N<- c(155782.7, 159463.7, 172741.1, 204547.2, 126049.3, 139881.9, 140747.3, 251963.0, 182444.3, 207780.8, 189251.2, 318053.7, 230569.2, 247826.8, 237019.6, 383909.5, 265145.5, 264816.4, 239607.0, 436403.1, 276767.7, 286337.9, 270022.7, 444672.9, 263717.2, 343143.9, 271701.7)
aslog<-"n"
library(gtools)
library(forecast)
a<-permutations(n=3,r=6,v=c(0:2),repeats.allowed=TRUE)
a<-a[ifelse((a[,1]+a[,4]>2|a[,2]+a[,5]>2|a[,3]+a[,6]>2),FALSE,TRUE),]
namWA<-matrix(0,ncol=1,nrow=length(a[,1]))
namWS<-matrix(0,ncol=1,nrow=length(a[,1]))
Arimafit<-matrix(0,ncol=length(N),nrow=length(a[,1]),byrow=TRUE)
tota<-matrix(0,ncol=1,nrow=length(a[,1]))
totb<-matrix(0,ncol=1,nrow=length(a[,1]))
for(i in 1:length(a[,1])){
namWA[i]<-paste("orderWA",i,sep=".")
assign(namWA[i],a[i,c(1:3)])
namWS[i]<-paste("orderWS",i,sep=".")
assign(namWS[i],a[i,c(4:6)])
ArimaW1 <- Arima(N, order= a[i,c(1:3)], seasonal=list(order=a[i,c(4:6)]),method="ML")
if(aslog=="y"){Arimafit[i,]<-c(exp(fitted(ArimaW1)))}else{Arimafit[i,]<-c(fitted(ArimaW1))}
nnn<-c(N)
arimab<-c(Arimafit[i,])
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
tota[i]<-"n" else{
tota[i]<-sum(abs(v$acf[2:7]))
totb[i]<-sum(abs(w$acf[1:6]))}
}
I tried doing
ArimaW1<-tryCatch(Arima(N, order= a[i,c(1:3)], seasonal=list(order=a[i,c(4:6)]),method="ML"),error=function(e) NULL)
and this gave another error
Error in Arimafit[i, ] <- c(fitted(ArimaW1)) :
number of items to replace is not a multiple of replacement length
then i tried:
ArimaW1<-tryCatch(Arima(N, order= a[i,c(1:3)], seasonal=list(order=a[i,c(4:6)]),method="ML"),error=function(e) matrix("n",ncol=length(Arimafit[1,])))
but this gave an error:
Error: $ operator is invalid for atomic vectors
and also gave a matrix with all the fitted ARIMA values up to iteration 68, after that it gives everything as 0.0
is there a way to get the loop to continue the iterations, filling a vector with a value which goes into the matrix Arimafit like the iterations that do work so that i can carry on with the code?

I just found out the way to do what i wanted to do. This may help other people so I wont delete it, ill just post the solution :)
library(gtools)
a<-permutations(n=3,r=6,v=c(0:2),repeats.allowed=TRUE)
a<-a[ifelse((a[,1]+a[,4]>2|a[,2]+a[,5]>2|a[,3]+a[,6]>2),FALSE,TRUE),]
namWA<-matrix(0,ncol=1,nrow=length(a[,1]))
namWS<-matrix(0,ncol=1,nrow=length(a[,1]))
Arimafit<-matrix(0,ncol=length(N),nrow=length(a[,1]),byrow=TRUE)
tota<-matrix(0,ncol=1,nrow=length(a[,1]))
totb<-matrix(0,ncol=1,nrow=length(a[,1]))
arimaerror<-matrix(0,ncol=length(N),nrow=1)
for(i in 1:length(a[,1])){
namWA[i]<-paste("orderWA",i,sep=".")
assign(namWA[i],a[i,c(1:3)])
namWS[i]<-paste("orderWS",i,sep=".")
assign(namWS[i],a[i,c(4:6)])
ArimaW1 <- try(Arima(N, order= a[i,c(1:3)], seasonal=list(order=a[i,c(4:6)]),method="ML"))
if(is(ArimaW1,"try-error"))
ArimaW1<-arimaerror else
ArimaW1<-ArimaW1
arimafitted<-try(fitted(ArimaW1))
if(is(arimafitted,"try-error"))
fitarima<-arimaerror else
fitarima<-arimafitted
if(aslog=="y"){Arimafit[i,]<-c(exp(fitarima))}else{Arimafit[i,]<-c(fitarima)}
nnn<-c(N)
arimab<-c(Arimafit[i,])
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
tota[i]<-"n" else{
tota[i]<-sum(abs(v$acf[2:7]))
totb[i]<-sum(abs(w$acf[1:6]))}
}

Here is a further adaption to what i wanted to achieve
a <- permutations(n = 3, r = 6, v = c(0:2), repeats.allowed = TRUE)
a <- a[ifelse((a[, 1] + a[, 4] > 2 | a[, 2] + a[, 5] > 2 | a[, 3] + a[, 6] > 2),
FALSE, TRUE), ]
Arimafit <- matrix(0,
ncol = length(Data.new),
nrow = length(a[, 1]),
byrow = TRUE)
totb <- matrix(0, ncol = 1, nrow = length(a[, 1]))
arimaerror <- matrix(0, ncol = length(Data.new), nrow = 1)
for (i in 1:length(a[, 1])){
ArimaData.new <- try(Arima(Data.new,
order = a[i, c(1:3)],
seasonal = list(order = a[i, c(4:6)]),
method = "ML"),
silent = TRUE)
if (is(ArimaData.new, "try-error")){
ArimaData.new <- arimaerror
} else {
ArimaData.new <- ArimaData.new
}
arimafitted <- try(fitted(ArimaData.new), silent = TRUE)
if (is(arimafitted, "try-error")){
fitarima <- arimaerror
} else {
fitarima <- arimafitted
}
if (as.log == "log"){
Arimafit[i, ] <- c(exp(fitarima))
Datanew <- c(exp(Data.new))
} else {
if (as.log == "sqrt"){
Arimafit[i, ] <- c((fitarima)^2)
Datanew <- c((Data.new)^2)
} else {
Arimafit[i, ] <- c(fitarima)
Datanew <- c(Data.new)
}
}
data <- c(Datanew)
arima.fits <- c(Arimafit[i, ])
fullres <- data - arima.fits
v <- acf(fullres, plot = FALSE)
w <- pacf(fullres, plot = FALSE)
if (v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4)){
totb[i] <- "n"
} else {
totb[i] <- sum(abs(w$acf[1:4]))
}
j <- match(min(totb), totb)
order.arima <- a[j, c(1:3)]
order.seasonal.arima <- a[j, c(4:6)]
}

Resources