Dear Rusers,
  I am now using  R and SAS to fit the piecewise linear functions, and what
surprised me is that they have a great differrent result. See below.
#R code--Knots for distance are 16.13 and 24, respectively, and Knots for y
are -0.4357 and -0.3202
m.glm<-glm(mark~x+poly(elevation,2)+bs(distance,degree=1,knots=c(16.13,24))
                +bs(y,degree=1,knots=c(-0.4357,-0.3202
)),family=binomial(logit),data=point)
summary(m.glm)

Coefficients:
                                                      Estimate Std. Error z
value Pr(>|z|)
(Intercept)                                             12.104      3.138
3.857 0.000115 ***
x                                                       5.815      1.987
2.926 0.003433 **
poly(elevation, 2)1                                     6.654      4.457
1.493 0.135444
poly(elevation, 2)2                                     -6.755      3.441  -
1.963 0.049645 *
bs(distance, degree = 1, knots = c(16.13, 24))1         -1.291      1.139  -
1.133 0.257220
bs(distance, degree = 1, knots = c(16.13, 24))2        -10.348      2.025  -
5.110 3.22e-07 ***
bs(distance, degree = 1, knots = c(16.13, 24))3        -3.530      3.752  -
0.941 0.346850
bs(y, degree = 1, knots = c(-0.4357, -0.3202))1        -6.828      1.836  -
3.719 0.000200 ***
bs(y, degree = 1, knots = c(-0.4357, -0.3202))2        -4.426      1.614  -
2.742 0.006105 **
bs(y, degree = 1, knots = c(-0.4357, -0.3202))3        -11.216      2.861  -
3.920 8.86e-05 ***

#SAS codes
data b;
 set a;
 if distance > 16.13 then d1=1; else d1=0;
 distance2=d1*(distance - 16.13);
 if distance > 24 then d2=1; else d2=0;
 distance3=d2*(distance - 24);
 if y>-0.4357 then dd1=1; else dd1=0;
 y2=dd1*(y+0.4357);
 if y>-0.3202 then dd2=1; else dd2=0;
 y3=dd2*(y+0.3202);
run;

proc logistic descending data=b;
 model mark =x elevation elevation*elevation distance distance2 distance3 y
y2 y3;
run;


              The LOGISTIC Procedure  Analysis of Maximum Likelihood
Estimates

                                                     Standard          Wald
           Parameter               DF    Estimate       Error
Chi-Square    Pr > ChiSq

           Intercept                1     -2.6148      2.1445
1.4867
0.2227
           x                        1      5.8146      1.9872
8.5615
0.0034
           elevation                1      0.4457      0.1506
8.7545
0.0031
           elevation*elevation      1     -0.0279      0.0142
3.8533
0.0496
           distance                 1     -0.1091      0.0963
1.2836
0.2572
           distance2                1     -1.0418      0.2668
15.2424
<.0001
           distance3                1      2.8633      0.7555
14.3625
0.0002
           y                        1    -16.2032      4.3568
13.8314
0.0002
           y2                       1     36.9974     10.3219
12.8476
0.0003
           y3                       1    -58.4938     14.0279
17.3875
<.0001
Q: What is the problem? which one is correct for the piecewise linear
function?
  Thanks very much.


-- 
With Kind Regards,

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Zhi Jie,Zhang ,PHD
Tel:+86-21-54237149
Dept. of Epidemiology,School of Public Health,Fudan University
Address:No. 138 Yi Xue Yuan Road,Shanghai,China
Postcode:200032
Email:epistat@gmail.com
Website: www.statABC.com
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