Plot normal probability for effect estimates in factorial design in R I have 2 level  Design (DOE) with 4 factors (A,B,C,D). I've already calculated the estimates for each main effect and all the interaction effects.
How can I construct the normal probability plot to see which effects are significant? I have looked at many packages in R for Design of Experiment but cannot find a package that produces the plot.
How can I construct the plot with all the estimates I have by code in R
Thank you.
 A: In R you can just pass the vector of effects to the qqnorm function.  If you want the points labeled then save the result of qqnorm and use that with the text or identify functions.
mydata <- expand.grid( A=c(-1,1), B=c(-1,1), C=c(-1,1), D=c(-1,1) )
mydata$y <- rnorm( nrow(mydata) )

fit <- lm( y ~ .^4, data=mydata )

tmp <- qqnorm( coef(fit) )
qqline( coef(fit) )
text( tmp$x, tmp$y, names(coef(fit)), pos=3 )
# or
identify( tmp$x, tmp$y, names(coef(fit)))

A: First order the data (effects and interactions), then calculate the probability of each data with this formula: $P=i/(n+1)$ where $n$ is the total data (16 in your case) and $i$ the order (1, 2, 3 and so on). After that calculate the inverse probability function (I think is called a z-score). I think the Excel function to do this is NORMINV with the probability as an argument (inverse probability: NORMINV(P)). Finally graph the data versus the inverse probability (i.e., z-score). Where the data is on the X axis and the z-score is on the Y axis.    
