Automated Fixed Order Stepwise Regression Function

Greetings,
 
I am interested in creating a stepwise fixed order regression function. 
There's a function for this already called add1( ).  The F statistics are
calculated using type 2 anova (the SS and the F changes don't match
SPSS's).  You can see my use of this at the very end of the email.
 
What I want: a function to make an anova table with f changes and delt
R^2.  
 
I ran into 10 snags to making this a fully automated function using the
full linear model (order matters here).  Each snag is marked with a
Comment #.  Some snags are repeated because I couldn't do the first time
and certainly couldn't do it after that. Help with any/all snags would be
very appreciated. 
 
I'm a 2 1/2 month [R] user who's reading everything online (incl. manuals)
& ordering every book I can (looking at Dalgaard, Crawly's and Teetor's
very helpful books right now).  Loops and their usage is a foreign thing
to me, despite studying them, and unfortunately I think that my function
may call for them.  I also realize that beyond 10 predictors may make this
function way to bulky.
 
I'm running the latest version of R (2.12.2)on a windows 7 machine.

DATASET
 
mtcars
full.model<-lm(mpg~cyl+disp+hp+drat, data=mtcars)
 
CODE

stepFO<-function(model)
{
m<-data.frame(model.frame(model))
num.of.var<-length(colnames(m))
mod1<-lm(m[,1]~m[,2])
mod2<-lm(m[,1]~m[,2]+m[,3])
mod3<-lm(m[,1]~m[,2]+m[,3]+m[,4])
mod4<-lm(m[,1]~m[,2]+m[,3]+m[,4]+m[,5])
#Comment 1--I don't know how to automated this process(above) of adding 
#...additional variables.  Probably a loop is needed but I don't
understand 
#...how to apply it here.  Maybe update.model [1:num.ofvar]?
a1<-anova(mod1)
a2<-anova(mod2)
a3<-anova(mod3)
a4<-anova(mod4)
#Comment 2--SAME AS COMMENT 1 except applied to the anova tables.  How do
I make
#...[R] add a5, a6...an   as necessary?

rb<-rbind(a1,a2,a3,a4)
#Comment 3--again I can't automate this to make the  addition of a's
automated

anova1<-rbind(rb[1,],rb[4,],rb[8,],rb[13,],rb[14,])
#Comment 4--the rb's follow a pattern of 1+3+4+5...+n variables
#then I row bind these starting with 1 and rowbind one more after the last 
#...rb to include the bottom piece of the anova table that tells 
#...about residuals (how do I aoutomate this?)

anova<-anova1[,1:num.of.var]
anova.table<-data.frame(anova)
#Comment 5--Something that bugs me here is that I have to turn it into a
dataframe to 
#...add the totals row and the delta R^2 (tried playing w/ tkinsert to no
avail)
#...I miss the stuff that's at the bottom of the anova table (sig values)
#Comment 6--I'd love to turn the place value to round to 2 after the
decimal.
#...I've worked this many ways including changing my options but this does 
#...not seem to apply to a data frame

Total<-c(sum(anova[,1]),sum(anova[,2])," ", " ", " ")
anova.table<-rbind(anova.table,Total)
R1<-summary(mod1)[[8]][[1]]
R2<-summary(mod2)[[8]][[1]]
R3<-summary(mod3)[[8]][[1]]
R4<-summary(mod4)[[8]][[1]]
#Comment 7--SAME AS COMMENT 2.  How do I make
#...[R] add R5, R6...Rn   as necessary?

deltaR.1<-R1
deltaR.2<-R2-R1
deltaR.3<-R3-R2
deltaR.4<-R4-R3
#Comment 8--SAME AS COMMENT 7.  How would I aoutomate this process?

Delta.R.Squared<-c(deltaR.1,deltaR.2,deltaR.3,deltaR.4," ","")
#Comment 9--I need a way to add as many deltaR's as 
#...necessary(n of R = n of predictors)

anova.table<-cbind(anova.table, Delta.R.Squared)
colnames(anova.table)<-c("df","Sum Sq","Mean Sq","F-change",
"P-value","Delta.R.Squared")
rownames(anova.table)<-c("X1", "X2 elminating for X1", 
"X3 eliminating for X2 & X3", "X4 eliminating for X1,X2, &
X3","Residuals",
 "Total")
anova.table
}
#Comment 10--Again I would need [R] to automate the list for row names as
we 
#...add more predictors.
#See the final product below I'm after (with two places after the decimal)

anova.table

                                df           Sum Sq          Mean Sq        
F-change            P-value     Delta.R.Squared
X1                               1 817.712952354614 817.712952354614
79.5610275293349 6.112687142581e-10   0.726180005093805
X2 elminating for X1             1 37.5939529324851 37.5939529324851 
4.0268283172755 0.0541857201845285  0.0333857704630917
X3 eliminating for X2 & X3       1 9.37092926438942 9.37092926438942
1.00388976918267  0.324951851250774 0.00832196853596723
X4 eliminating for X1,Xx2, & X3  1 16.4674349222492 16.4674349222492
1.81550535203668  0.189048514740917  0.0146241073243205
Residuals                       27 244.901918026262 9.07044140838007                                                
Total                           31     1126.0471875  
 
 
USING THE ADD1() FUNCTION>  NOT WHAT I WANT> 
 
mod1<-lm(mpg~cyl, data=mtcars)
add1(mod1,~cyl+disp+hp+drat, data=mtcars, test="F")

Model:
mpg ~ cyl
       Df Sum of Sq    RSS    AIC F value   Pr(F)  
              308.33 76.494                  
disp    1    37.594 270.74 74.334  4.0268 0.05419 .
hp      1    16.360 291.98 76.750  1.6249 0.21253  
drat    1    15.841 292.49 76.807  1.5706 0.22012  
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 


 		 	   		  
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