Regression in R using a function - r

Question

I am trying to smooth out my data for each variable in the data frame. Lets say it looks like this:
data <- data.frame(v1 = c(0.5,1.1,2.9,3.4,4.1,5.7,6.3,7.4,6.9,8.5,9.1),
v2 = c(0.1,0.8,0.5,1.1,1.9,2.4,0.8,3.4,2.9,3.1,4.2),
v3 = c(1.3,2.1,0.8,4.1,5.9,8.1,4.3,9.1,9.2,8.4,7.4))
data$x <- 1:nrow(data)
I then specify my x and y variables as:
x <- data$x
y <- data$v1
I can fit the predicted line I want (and I am happy with the process):
f <- function (x,a,b,d) {(a*x^2) + (b*x) + d}
order_two <- nls(y ~ f(x,a,b,d), start = c(a=1, b=1, d=1))
co2 <- coef(order_two)
data$order_two_predicted_v1 <- (co2[1] * (data$x)^2) + (co2[2] * data$x) + co2[3]
I therefore end up with an appropriately titled new variable (the predicted values for v1). I now want to do this for each of the other 100 variables in my data frame (v2 and v3 in this example).
I tried using a function to do this but can't get it to work as intended. Here is my attempt:
myfunction <- function(xaxis,yaxis){
# Specfiy my "y" and "x"
x <- data$xaxis
y <- data$yaxis
f <- function (x,a,b,d) {(a*x^2) + (b*x) + d}
order_two <- nls(y ~ f(x,a,b,d), start = c(a=1, b=1, d=1))
co2 <- coef(order_two)
data$order_two_predicted_yaxis <- (co2[1] * (data$x)^2) + (co2[2] * data$x) + co2[3]
}
myfunction(x,v1)
myfunction(x,v2)
myfunction(x,v3)
Not only does the function not work as intended, I would like to avoid calling the function 100 times for each variable and instead somehow loop through it.
This is really simple to do in SAS using macros but I am struggling to get this to work in R.

Answer 1

You can model your data directly with the lm() function:
data <- data.frame(v1 = c(0.5,1.1,2.9,3.4,4.1,5.7,6.3,7.4,6.9,8.5,9.1),
v2 = c(0.1,0.8,0.5,1.1,1.9,2.4,0.8,3.4,2.9,3.1,4.2),
v3 = c(1.3,2.1,0.8,4.1,5.9,8.1,4.3,9.1,9.2,8.4,7.4))
x <- 1:nrow(data)
# initialize a list to store the models
models = vector("list", length = (ncol(data)))
# create a loop running over the columns of data
for (i in 1:(ncol(data))){
models[[i]] = lm(data[,i] ~ poly(x,2, raw = TRUE))}
You can also use lapply instead of the for-loop, as stated in the comments.
Use predict() to get the values of the models:
smoothed_v1 = predict(model[[1]], newdata=data.frame(x = x))
Edit:
Regarding your comment - you can store the new values in data with:
for (i in (length(models):1)){
data <- cbind(predict(models[[i]], newdata=data.frame(x = x)), data)
# set the name for the new column
names(data)[1] = paste("pred_v",i, sep ="")}