Viitama - 1 year ago 53

R Question

I have been able to run regression with some coefficients constrained to positive territory, but I'm doing alot of rolling regressions where I face the problem. Here is my sample code:

`library(penalized)`

set.seed(1)

x1=rnorm(100)*10

x2=rnorm(100)*10

x3=rnorm(100)*10

y=sin(x1)+cos(x2)-x3+rnorm(100)

data <- data.frame(y, x1, x2, x3)

win <- 10

coefs <- matrix(NA, ncol=5, nrow=length(y))

for(i in 1:(length(y)-win)) {

d <- data[(1+i):(win+i),]

p <- win+i

# Linear Regression

coefs[p,] <- as.vector(coef(penalized(y, ~ x1 + x2 + x3, ~1,

lambda1=0, lambda2=0, positive = c(F, F, T), data=data)))}

This is how I usually populate matrix with coefs from rolling regression and now I receive error:

`Error in coefs[p, ] <- as.vector(coef(penalized(y, ~x1 + x2 + x3, ~1, :`

number of items to replace is not a multiple of replacement length

I assume that this error is produced because there is not always Intercept + 3 coefficients coming out of that penalized regression function. Is there away to get

`penalized`

Answer Source

You at most have 4 coefficients. *(Seems like you know this by saying "intercept + 3 coefficients" but not sure why you in your code set up a matrix of 5 columns.)*

Also, perhaps you are unaware of the `which`

argument for `coef`

for "penfit" object. Have a look at:

```
getMethod(coef, "penfit")
#function (object, ...)
#{
# .local <- function (object, which = c("nonzero", "all", "penalized",
# "unpenalized"), standardize = FALSE)
# {
# coefficients(object, which, standardize)
# }
# .local(object, ...)
#}
#<environment: namespace:penalized>
```

We can set `which = "all"`

to report all coefficients. The default is `which = "nonzero"`

, which is causing the "replacement length differs" issue.

The following works:

```
library(penalized)
set.seed(1)
x1 = rnorm(100)*10
x2 = rnorm(100)*10
x3 = rnorm(100)*10
y = sin(x1) + cos(x2) - x3 + rnorm(100)
data <- data.frame(y, x1, x2, x3)
win <- 10
coefs <- matrix(NA, ncol=4, nrow=length(y))
for(i in 1:(length(y)-win)) {
d <- data[(1+i):(win+i),]
p <- win + i
pen <- penalized(y, ~ x1 + x2 + x3, ~1, lambda1 = 0, lambda2 = 0,
positive = c(F, F, T), data = data)
beta <- coef(pen, which = "all")
coefs[p,] <- unname(beta)
}
```