Displaying 4 results from an estimated 4 matches for "outest".
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2008 Nov 05
2
how can I save the estimates of a regression model in a file?
...---------------
: 2
Call:
lm(formula = logmilk ~ logdays + days, data = .data2)
Coefficients:
(Intercept) logdays days
3.2276114 0.1223412 -0.0006836
and so on:
5) THE QUESTION is:
there is a way in R to get an output file as you get in SAS when you use:
prog reg data=xxx outest=estimate;
I would need an output that looks like this:
individual intercept logdays days etc
1 3.414105 0.069387 -0.0006836
2 3.2276114 0.1223412 -0.0006836
n ........ .......... .........
THANKS FOR YOUR HELP...
2008 Jun 14
1
restricted coefficient and factor in linear regression.
..., l, e, k).
I would like to estimate Lee and Schmidts (1993, OUP) model in R.
My colleague wrote SAS code as follows:
** procedures for creating dummy variables are omitted **
** di# and dt# are dummy variables for industry and time **
data a2; merge a1 a2 a; by id yr;
proc sysnlin maxit=100 outest=beta2;
endogenous y;
exogenous l e k di1-di12 dt2-dt10;
parms a0 0.94 al -0.14 ae 1.8 ak -0.9
b1 0 b2 0 b3 0 b4 0 b5 0 b6 0 b7 0 b8 0 b9 0 b10 0 b11 0
b12 0 c2 0 c3 0 c4 0 c5 0 c6 0 c7 0 c8 0 c9 0 c10 0;
y=a0+al*l+ae*e+ak*k
+(b1*di1+b2*di2+b3*di3+b4*di4+b5*di...
2008 Jun 13
0
restricted coefficient and factor for linear regression.
...l, e, k).
I would like to estimate Lee and Schmidts (1993, OUP) model in R.
My colleague wrote SAS code as follows:
** procedures for creating dummy variables are omitted **
** di# and dt# are dummy variables for industry and time **
data a2; merge a1 a2 a; by id yr;
proc sysnlin maxit=100 outest=beta2;
endogenous y;
exogenous l e k di1-di12 dt2-dt10;
parms a0 0.94 al -0.14 ae 1.8 ak -0.9
b1 0 b2 0 b3 0 b4 0 b5 0 b6 0 b7 0 b8 0 b9 0 b10 0 b11 0
b12 0 c2 0 c3 0 c4 0 c5 0 c6 0 c7 0 c8 0 c9 0 c10 0;
y=a0+al*l+ae*e+ak*k
+(b1*di1+b2*di2+b3*di3+b4*di...
2010 Sep 05
0
cov.unscaled in NLS - how to define cov.scaled to make comparable to SAS proc NLIN output - and theoretically WHY are they different
...efault Gauss-Newton
method of nls.
initialValues.L = list(b=4,d=0.04,t=180);
fit.nls.L = nls(
myModel.nlm ,
fData.L,
start = initialValues.L,
control = nls.control(warnOnly = TRUE),
trace=T
);
summary.nls.L = summary(fit.nls.L);
I run the same analysis in SAS proc NLIN.
proc nlin data=apples outest=a;
parms b=4 d=.04 t=180;
model Y = b/(1+exp(-d*(X-t)));
output out=b p=yhat r=yresid ;
run;
proc print data=a;
run;
A summary of the outputs:
- Same coefficients (this is a good thing)
- Same standard errors of the coefficients (also good)
- Different covariance matrices (WHY?)
I conv...