Reverse regression returns multiple objects

Question

I am trying to create a moving regression function based on the example here , but in addition to returning the predicted values, I would like to return some moving diagnostic model (i.e. coefficients, t-values ​​and mabye R ^ 2). I would like the results to be returned in discrete objects based on the type of results. The example in the link above successfully creates predictions for slippage, but I need help collecting and writing out a diagnostic rolling model:

In the end, I would like the function to return three (3) objects:

• Forecasts
• Odds
• T values
• R ^ 2

Below is the code:

require(zoo) require(dynlm) ## Create Some Dummy Data set.seed(12345) x <- rnorm(mean=3,sd=2,100) y <- rep(NA,100) y[1] <- x[1] for(i in 2:100) y[i]=1+x[i-1]+0.5*y[i-1]+rnorm(1,0,0.5) int <- 1:100 dummydata <- data.frame(int=int,x=x,y=y) zoodata <- as.zoo(dummydata) rolling.regression <- function(series) { mod <- dynlm(formula = y ~ L(y) + L(x), data = as.zoo(series)) # get model nextOb <- max(series[,'int'])+1 # To get the first row that follows the window if (nextOb<=nrow(zoodata)) { # You won't predict the last one # 1) Make Predictions predicted <- predict(mod,newdata=data.frame(x=zoodata[nextOb,'x'],y=zoodata[nextOb,'y'])) attributes(predicted) <- NULL c(predicted=predicted,square.res <-(predicted-zoodata[nextOb,'y'])^2) # 2) Extract coefficients #coefficients <- coef(mod) # 3) Extract rolling coefficient t values #tvalues <- ????(mod) # 4) Extract rolling R^2 #rsq <- } } rolling.window <- 20 results.z <- rollapply(zoodata, width=rolling.window, FUN=rolling.regression, by.column=F, align='right') 

So, having figured out how to extract the t values ​​from the model (for example, mod), what do I need to do for the function to return three (3) separate objects (i.e. predictions, coefficients and T values)?

I'm new to R, really new to features and unusually new to the zoo, and I'm stuck.

Any help would be greatly appreciated.

Answer 1

I hope I understand you correctly, but here is a small edit to your function:

 rolling.regression <- function(series) { mod <- dynlm(formula = y ~ L(y) + L(x), data = as.zoo(series)) # get model nextOb <- max(series[,'int'])+1 # To get the first row that follows the window if (nextOb<=nrow(zoodata)) { # You won't predict the last one # 1) Make Predictions predicted=predict(mod,newdata=data.frame(x=zoodata[nextOb,'x'],y=zoodata[nextOb,'y'])) attributes(predicted)<-NULL #Solution 1; Quicker to write # c(predicted=predicted, # square.res=(predicted-zoodata[nextOb,'y'])^2, # summary(mod)$coef[, 1], # summary(mod)$coef[, 3], # AdjR = summary(mod)$adj.r.squared) #Solution 2; Get column names right c(predicted=predicted, square.res=(predicted-zoodata[nextOb,'y'])^2, coef_intercept = summary(mod)$coef[1, 1], coef_Ly = summary(mod)$coef[2, 1], coef_Lx = summary(mod)$coef[3, 1], tValue_intercept = summary(mod)$coef[1, 3], tValue_Ly = summary(mod)$coef[2, 3], tValue_Lx = summary(mod)$coef[3, 3], AdjR = summary(mod)$adj.r.squared) } } rolling.window <- 20 results.z <- rollapply(zoodata, width=rolling.window, FUN=rolling.regression, by.column=F, align='right') head(results.z) predicted square.res coef_intercept coef_Ly coef_Lx tValue_intercept tValue_Ly tValue_Lx AdjR 20 10.849344 0.721452 0.26596465 0.5798046 1.049594 0.38309211 7.977627 13.59831 0.9140886 21 12.978791 2.713053 0.26262820 0.5796883 1.039882 0.37741499 7.993014 13.80632 0.9190757 22 9.814676 11.719999 0.08050796 0.5964808 1.073941 0.12523824 8.888657 15.01353 0.9340732 23 5.616781 15.013297 0.05084124 0.5984748 1.077133 0.08964998 9.881614 16.48967 0.9509550 24 3.763645 6.976454 0.26466039 0.5788949 1.068493 0.51810115 11.558724 17.22875 0.9542983 25 9.433157 31.772658 0.38577698 0.5812665 1.034862 0.70969330 10.728395 16.88175 0.9511061 

To see how this works, do a little regression example:

 x <- rnorm(1000); y <- 2*x + rnorm(1000) reg <- lm(y ~ x) summary(reg)$coef Estimate Std. Error t value Pr(>|t|) (Intercept) 0.02694322 0.03035502 0.8876033 0.374968 x 1.97572544 0.03177346 62.1816310 0.000000 

As you can see, calling summary first and then getting its coefficients ( coef(summary(reg)) ) gives you a table with estimates, standard errors and t-values. Thus, the ratings are stored in column 1 of this table, the t-values ​​in column 3. And that I get them in the updated rolling.regression function.

EDIT

I updated my solution; it now also contains adjusted R2. If you just want a normal R2, get rid of .adj .

EDIT 2

Quick and dirty hack, what to call columns:

 rolling.regression <- function(series) { mod <- dynlm(formula = y ~ L(y) + L(x), data = as.zoo(series)) # get model nextOb <- max(series[,'int'])+1 # To get the first row that follows the window if (nextOb<=nrow(zoodata)) { # You won't predict the last one # 1) Make Predictions predicted=predict(mod,newdata=data.frame(x=zoodata[nextOb,'x'],y=zoodata[nextOb,'y'])) attributes(predicted)<-NULL #Get variable names strVar <- c("Intercept", paste0("L", 1:(nrow(summary(mod)$coef)-1))) vec <- c(predicted=predicted, square.res=(predicted-zoodata[nextOb,'y'])^2, AdjR = summary(mod)$adj.r.squared, summary(mod)$coef[, 1], summary(mod)$coef[, 3]) names(vec)[4:length(vec)] <- c(paste0("Coef_", strVar), paste0("tValue_", strVar)) vec } }