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...> manually remove the X1 column from the model matrix. This gives us the > model matrix one would expect if X1 were removed from the model. We then > successfully calculate the covariance matrix. > >> mm = model.matrix(~(X1+X2)^2,data=design) >> mm > (Intercept) X1 X2B X2C X1:X2B X1:X2C > 2 1 -1 0 0 0 0 > 3 1 1 1 0 1 0 > 4 1 -1 1 0 -1 0 > 5 1 1 0 1 0 1 > 6 1 -1 0 1 0 -1 > >> mm = mm[,-2] >> solve(t(mm) %*% mm) >...

Hi, I recently ran into an inconsistency in the way model.matrix.default handles factor encoding for higher level interactions with categorical variables when the full hierarchy of effects is not present. Depending on which lower level interactions are specified, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- >

...rix. This gives us the >> > model matrix one would expect if X1 were removed from the model. We then >> > successfully calculate the covariance matrix. >> > >> >> mm = model.matrix(~(X1+X2)^2,data=design) >> >> mm >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C >> > 2 1 -1 0 0 0 0 >> > 3 1 1 1 0 1 0 >> > 4 1 -1 1 0 -1 0 >> > 5 1 1 0 1 0 1 >> > 6 1 -1 0 1 0 -1 >> > >&g...

...f X1 were removed from the model. We >> >> > then >> >> > successfully calculate the covariance matrix. >> >> > >> >> >> mm = model.matrix(~(X1+X2)^2,data=design) >> >> >> mm >> >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C >> >> > 2 1 -1 0 0 0 0 >> >> > 3 1 1 1 0 1 0 >> >> > 4 1 -1 1 0 -1 0 >> >> > 5 1 1 0 1 0 1 >> >> > 6 1...

...olumn from the model matrix. This gives us the > > model matrix one would expect if X1 were removed from the model. We then > > successfully calculate the covariance matrix. > > > >> mm = model.matrix(~(X1+X2)^2,data=design) > >> mm > > (Intercept) X1 X2B X2C X1:X2B X1:X2C > > 2 1 -1 0 0 0 0 > > 3 1 1 1 0 1 0 > > 4 1 -1 1 0 -1 0 > > 5 1 1 0 1 0 1 > > 6 1 -1 0 1 0 -1 > > > >> mm = mm[,-2] &gt...

...>> > then >> >> >> > successfully calculate the covariance matrix. >> >> >> > >> >> >> >> mm = model.matrix(~(X1+X2)^2,data=design) >> >> >> >> mm >> >> >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C >> >> >> > 2 1 -1 0 0 0 0 >> >> >> > 3 1 1 1 0 1 0 >> >> >> > 4 1 -1 1 0 -1 0 >> >> >> > 5 1 1 0 1 0 1 &...

...; > model matrix one would expect if X1 were removed from the model. We > then > >> > successfully calculate the covariance matrix. > >> > > >> >> mm = model.matrix(~(X1+X2)^2,data=design) > >> >> mm > >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C > >> > 2 1 -1 0 0 0 0 > >> > 3 1 1 1 0 1 0 > >> > 4 1 -1 1 0 -1 0 > >> > 5 1 1 0 1 0 1 > >> > 6 1 -1 0 1 0...

...el. We > >> >> > then > >> >> > successfully calculate the covariance matrix. > >> >> > > >> >> >> mm = model.matrix(~(X1+X2)^2,data=design) > >> >> >> mm > >> >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C > >> >> > 2 1 -1 0 0 0 0 > >> >> > 3 1 1 1 0 1 0 > >> >> > 4 1 -1 1 0 -1 0 > >> >> > 5 1 1 0 1 0 1 > >> &gt...

...>> >> >> > successfully calculate the covariance matrix. > >> >> >> > > >> >> >> >> mm = model.matrix(~(X1+X2)^2,data=design) > >> >> >> >> mm > >> >> >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C > >> >> >> > 2 1 -1 0 0 0 0 > >> >> >> > 3 1 1 1 0 1 0 > >> >> >> > 4 1 -1 1 0 -1 0 > >> >> >> > 5 1 1 0...

...ch being a factor: df <- expand.grid(X1=c("p","q"), X2=c("A","B","C")) print(model.matrix(~(X1+X2)^2 ,data=df)) print(model.matrix(~(X1+X2)^2 -X1,data=df)) print(model.matrix(~(X1+X2)^2 -X2,data=df)) The result is: (Intercept) X1q X2B X2C X1q:X2B X1q:X2C 1 1 0 0 0 0 0 2 1 1 0 0 0 0 3 1 0 1 0 0 0 4 1 1 1 0 1 0 5 1 0 0 1 0 0 6 1 1 0 1 0 1 (Intercept) X2B X2C X1q:X2A...

...;c") suffixX(newFmla, "x1", "c") The last few lines of the output. See how the update misses x1 inside log(x1) or in the interaction? > newFmla <- formula(m2) > newFmla y ~ log(x1) + x2 * x3 > suffixX(newFmla, "x2", "c") y ~ log(x1) + x3 + x2c + x2:x3 > suffixX(newFmla, "x1", "c") y ~ log(x1) + x2 + x3 + x1c + x2:x3 It gets the target if the target is all by itself, but not otherwise. After messing with this for quite a while, I conclude that update was the wrong way to go because it is geared to replacement of i...

Hello, All: What's the simplest way to convert a data.frame into a model.matrix? One way is given by the following example, modified from the examples in help(model.matrix): dd <- data.frame(a = gl(3,4), b = gl(4,1,12)) ab <- model.matrix(~ a + b, dd) ab0 <- model.matrix(~., dd) all.equal(ab, ab0) What do you think about replacing "model.matrix(~ a +

Hi All, I am working on an example where the electric utility is investigating the effect of size of household and the type of air conditioning on electricity consumption. I fit a multiple linear regression Electricity consumption=size of the house hold + air conditioning type There are 3 air conditioning types so I modeled them as a dummy variable Type A Type B Type C Where type A is the

I may be making a simple error, but I've looked at the str() of the resulting objects and I can't see any obvious reason I'm having the problem I am having, so I am reaching out to the R-help group. ?I am generating a string in my code. ?When I make a slight modification (add a comma at the end using my "lastcomma" function), I can no longer successfully write that string to

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