This may be an easy question, but I can't think of the answer. I've built a regression model (predicting Y) with two independent variables (X and Z). I want to compute R^2. Is that built into the model somehow? If not, any ideas on how to compute it? Here's the model I have:

multiRegModel <- mxModel("Multiple Regression, All Variables",

type="RAM",

manifestVars=c("x", "y", "z"),

# variance paths

mxPath(

from=c("x", "y", "z"),

arrows=2,

free=TRUE,

values = c(.5, .5, .5),

labels=c("varx", "residual", "varz")

),

# covariance of x and z

mxPath(

from="x",

to="z",

arrows=2,

free=TRUE,

values=0.2,

labels="covxz"

),

# regression weights

mxPath(

from=c("x","z"),

to="y",

arrows=1,

free=TRUE,

values=.5,

labels=c("betax","betaz")

),

# means and intercepts

mxPath(

from="one",

to=c("x", "y", "z"),

arrows=1,

free=TRUE,

values=c(.5, .5),

labels=c("meanx", "beta0", "meanz")

)

) # close model

I know "residual" is the residual variance of Y, but I would think I'd need an estimate of the total variance of Y to compute it from that. Any help would be appreciated. Thanks!