x wrote:
Hi,
My code, output, error message, and sample data are all below. As always, all
help is appreciated.
Code:
====
library(Design); library(lattice)
df = read.table("./data_cub4.txt", header=TRUE, nrows=100)
attach(df); dd = datadist(df); options(datadist = 'dd'); describe(df);
m = (y1 ~ ( rcs(x1,3) + rcs(x2,3) ) )
f = ols(m, data=df)
print(f)
print( Function(f) )
detach(df)
Output:
======
Linear Regression Model ...
n Model L.R. d.f. R2 Sigma
100 720.7 4 0.9993 82.44
Residuals:
Min 1Q Median 3Q Max
-113.21 -70.46 -20.09 65.77 214.77
Coefficients:
Value Std. Error t Pr(>|t|)
Intercept 757.85 1.647e+17 4.601e-15 1
x1 35.58 9.080e+14 3.919e-14 1
x1' 85.92 6.475e+14 1.327e-13 1
x2 NA 1.797e+14 NA NA
x2' NA 6.475e+14 NA NA
Residual standard error: 82.44 on 95 degrees of freedom
Adjusted R-Squared: 0.9992
Error in if (coef[i] > 0 & (i > 2 | coef[1] != 0 | Intc != 0)) "+" else NULL :
missing value where TRUE/FALSE needed
Sample data:
=================
config benchmark y1 x1 x2 noise
1 verify2 1008.2 1 1000 0.72
2 verify2 1019 2 999 1.6
It just appears that you have perfect prediction, so you have quite an
unusual dataset to be doing inference on.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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