使用flexmix混合线性回归模型

Question

I have a data set with response variable ADA, and independent variables LEV, ROA, and ROAL. The data is called dt. I used the following code to get coefficients for latent classes.

m1 <- stepFlexmix(ADA ~ LEV+ROA+ROAL,data=dt,control= list(verbose=0), 
k=1:5,nrep= 10);  

m1 <- getModel(m1, "BIC");

All was fine until I read the following from http://rss.acs.unt.edu/Rdoc/library/flexmix/html/flexmix.html

model Object of FLXM of list of FLXM objects. Default is the object returned by calling FLXMRglm().

Which I think says that default model call is generalized linear model, while I am interested in linear model. How can I use linear model rather than GLM? I searched for it for quite a while, bit could't get it except this example from http://www.inside-r.org/packages/cran/flexmix/docs/flexmix , which I couldn't make sense of:

data("NPreg", package = "flexmix")

## mixture of two linear regression models. Note that control parameters
## can be specified as named list and abbreviated if unique.
ex1 <- flexmix(yn~x+I(x^2), data=NPreg, k=2,
                   control=list(verb=5, iter=100))

ex1
summary(ex1)
plot(ex1)

## now we fit a model with one Gaussian response and one Poisson
## response. Note that the formulas inside the call to FLXMRglm are
## relative to the overall model formula.
ex2 <- flexmix(yn~x, data=NPreg, k=2,
               model=list(FLXMRglm(yn~.+I(x^2)), 
                          FLXMRglm(yp~., family="poisson")))
plot(ex2)

Someone please let me know how to use linear regression instead of GLM. Or am I already using LM and just got confused because of the "default model line"? Please explain. Thanks.

Answer 1

I did a numerical analysis to understand if

m1 <- stepFlexmix(ADA ~ LEV+ROA+ROAL,data=dt,control= list(verbose=0)

does produce results from linear regression. To do the experiment, I ran the following code and found that yes the estimated parameters are indeed from linear regression. Experiment helped me to allay my reservations.

  x1 <- c(1:200);
  x2 <- x1*x1;
  x3 <- x1*x2;
  e1 <- rnorm(200,0,1);
  e2 <- rnorm(200,0,1);
  y1 <- 5+12*x1+20*x2+30*x3+e1;
  y2 <- 18+5*x1+10*x2+15*x3+e2;
  y <- c(y1,y2)
  x11 <- c(x1,x1)
  x22 <- c(x2,x2)
  x33 <- c(x3,x3)
  d <- data.frame(y,x11,x22,x33)

  m <- stepFlexmix(y ~ x11+x22+x33, data =d, control = list(verbose=0), k=1:5, nrep = 10);
  m <- getModel(m, "BIC");
  parameters(m);
  plotEll(m, data = d)
  m.refit <- refit(m);
  summary(m.refit)