Dear All,
I am trying to simulate data for a spline/piecewise regression model. I am
missing something fundamental in my simulation procedure because when I try to
fit my simulated data using the Gauss-Newton method in SAS, I am getting some
wacky parameter estimates. Can anyone please check my simulation code and tell
me what mistake I am making in generating data for spline model?
Thank you very much for your help.
Regards,
Nidhi
Here is my code:
#Set the working directory #
setwd("C:/SPLINE MODEL")
#Data Simulation Routine#
subjects = 30 #Sets the sample of subjects for whom the data is to be generated#
knot = 8 #Specifies the value of knot/change point#
beta0 = 3 #Specifies the value of intercept for x <= knot#
beta1 = 8 #Specifies the value of slope for x <= knot#
gamma0 = 6 #Specifies the value of intercept for x > knot#
gamma1 = 5 #Specifies the value of slope for x > knot#
#The following intializes empty (NA) vectors#
X = rep(NA,subjects)
Y = rep(NA, subjects)
DATA = rep(NA, subjects)
#The following sample x.values from uniform distribution#
X = runif(subjects,1,20)
# For loop for computing y-values by comparing each x-value to the knot#
for (i in 1:30)
{
if (X[i] <= knot)
Y[i] = beta0+beta1*X[i]
else
Y[i] = gamma0+gamma1*X[i]
}
#Binding/combining the x- and y-values in one data set#
DATA = cbind(X, Y)
#This writes the DATA to a separate file#
write.table(DATA, "SplineData.dat")
Dear All,
I am trying to simulate data for a spline/piecewise regression model. I am
missing something fundamental in my simulation procedure because when I try to
fit my simulated data using the Gauss-Newton method in SAS, I am getting some
wacky parameter estimates. Can anyone please check my simulation code and tell
me what mistake I am making in generating data for spline model?
Thank you very much for your help.
Regards,
Nidhi
Here is my code:
#Set the working directory #
setwd("C:/SPLINE MODEL")
#Data Simulation Routine#
subjects = 30 #Sets the sample of subjects for whom the data is to be generated#
knot = 8 #Specifies the value of knot/change point#
beta0 = 3 #Specifies the value of intercept for x <= knot#
beta1 = 8 #Specifies the value of slope for x <= knot#
gamma0 = 6 #Specifies the value of intercept for x > knot#
gamma1 = 5 #Specifies the value of slope for x > knot#
#The following intializes empty (NA) vectors#
X = rep(NA,subjects)
Y = rep(NA, subjects)
DATA = rep(NA, subjects)
#The following sample x.values from uniform distribution#
X = runif(subjects,1,20)
# For loop for computing y-values by comparing each x-value to the knot#
for (i in 1:30)
{
if (X[i] <= knot)
Y[i] = beta0+beta1*X[i]
else
Y[i] = gamma0+gamma1*X[i]
}
#Binding/combining the x- and y-values in one data set#
DATA = cbind(X, Y)
#This writes the DATA to a separate file#
write.table(DATA, "SplineData.dat")