A simple command to grab coefficients, t-stats, p-values, f-stats, etc from a regression and export them as an easy to use spreadsheet.

lmOut

lmOut <- function(res, file="test.csv", ndigit=3, writecsv=T) {
  # If summary has not been run on the model then run summary
  if (length(grep("summary", class(res)))==0) res <- summary(res)
  
  co <- res$coefficients
  nvar <- nrow(co)
  ncol <- ncol(co)
  f <- res$fstatistic
  formatter <- function(x) format(round(x,ndigit),nsmall=ndigit)
  
  # This sets the number of rows before we start recording the coefficients
  nstats <- 4
  
  # G matrix stores data for output
  G <- matrix("", nrow=nvar+nstats, ncol=ncol+1)
    
  G[1,1] <- toString(res$call)
  
  # Save rownames and colnames
  G[(nstats+1):(nvar+nstats),1] <- rownames(co)
  G[nstats, 2:(ncoll+1)] <- colnames(co)
  
  # Save Coefficients
  G[(nstats+1):(nvar+nstats), 2:(ncol+1)] <- formatter(co)
  
  # Save F-stat
  G[1,2] <- paste0("F(",f[2],",",f[3],")")
  G[2,2] <- formatter(f[1])
  
  # Save F-p value
  G[1,3] <- "Prob > P"
  G[2,3] <- formatter(1-pf(f[1],f[2],f[3]))
  
  # Save R2
  G[1,4] <- "R-Squared"
  G[2,4] <- formatter(res$r.squared)
  
  # Save Adj-R2
  G[1,5] <- "Adj-R2"
  G[2,5] <- formatter(res$adj.r.squared)
  
  print(G)
  if (writecsv) write.csv(G, file=file, row.names=F)
}

lmOut(res)

# First let's generate some fake binary response data (from yesterday's post).
  Nobs <- 10^4
  X <- cbind(cons=1, X1=rnorm(Nobs),X2=rnorm(Nobs),X3=rnorm(Nobs),u=rnorm(Nobs))
  B <- c(B0=-.2, B1=-.1,B2=0,B3=-.2,u=5)
  Y <- X%*%B
  SData <- as.data.frame(cbind(Y, X))

# Great, we have generated our data.  
myres <- lm(Y ~ X1 + X2 + X3, data=SData)

lmOut(myres, file="my-results.csv")

Hello, many thanks for this :) I'm trying to run it but I'm getting the following error:

Error in grep("summary", class(res)) : object 'res' not found

Apologies if this is something basic, I am very, very new to R!
Thanks

Hi, really helpful! is there any version of this function that works with logistic regressions?

To make it work with plm models, modify code this way:

• change f[1] to f[2]$statistic
• change f[2] to f[3]$parameter["df1"]
• change f[3] to f[3]$parameter["df2"]
• change res$r.squared to res$r.squared["rsq"]
• change res$adj.r.squared to res$r.squared["adjrsq"]

Hi, thanks for writing this helpful function. I'm curious that is there any way to use it when I do the regression under glm function?