[RsR] robustbase::lmrob() [bis]
Kaveh Vakili
k@veh@v@k||| @end|ng |rom w|@@ku|euven@be
Sun Jan 20 15:08:37 CET 2013
Dear all,
I'm trying to estimate a model
using robustbase::lmrobn using
the non-singular sub-sampling
scheme. I get:
Error in lmrob.S(x, y, control = control) :
C function R_lmrob_S() exited prematurely
In addition: Warning message:
In lmrob.S(x, y, control = control) :
subsample: could not find non-singular subsample.
It is not clear to me what parameter
of the lmrob() routine I should modify
to address this warning...below, i join
a reproducible example.
Best regards,
x0<-structure(list(y = c(4.16, 0.99, 0.61, 2.99, 1.75, -0.3, 2.75,
1.03, 3.13, 2.61, 3.73, 1.12, 3.52, 4.86, 4.53, -2.43, 3.78,
1.01, 1.67, 5.82, 2.77, 4.56, 0.11, 1.78, 4.65, 2.41, 2.99, 3.29,
0.1, 4.09, 2.38, 2.93, 5.7, 2.03, 0.68, 3.36, -1.63, 4.26, 7.44,
2.54, -0.36, 4.03, 3.75, 2.93, 0.79, -2.48, 0.17, 0.59, 4.71,
6.87, 1.42, 1.71, 6.68, 3.24, 0.09, 3.66, 2.06, 4.12, -1.11,
7.84, 1.2, 3.24, 4.26, 0.19, 2.66, 2.87, 2.31, 2.46, 5.69, 1.62,
3.54, 3.26, 3.49, -0.57, -0.36, 1.16, 3.07, 3.26, 0.43, 2.46,
-2.28, -0.39, 2.4, 4.22, 1.77, 3.43, -0.18, 2.51, 4.87, 3.27,
1.86, 2.45, 0.56, -0.83, 2.68, 3.02, 0.48, -1.37, 2.02, 3.25,
-1.08, 3.11, 2.24, -1.34, 7.55, 2.27, 1.16, 6.31, -0.78, 5.42,
-0.32, -3.49, 3.5, 1.92, 5.36, -2.17, 0.69, 1.1, 3.28, 4.71,
9.68, 14.08, 12.07, 11.61, 11.06, 9.4, 10.25, 13.29, 10.63, 7.51,
12.27, 11.34, 9.91, 11.44, 13.49, 12.78, 11.03, 13.06, 11.2,
10.64, 15.03, 11.07, 9.21, 15.03, 8.34, 8.86, 12.21, 8.59, 16.46,
11.18, 14.64, 11.05, 10.29, 15.68, 12.78, 9.36, 10.67, 13.05,
13.42, 12.21, 9.09, 8.04, 11.27, 13.61, 11.3, 8.27, 10.81, 8.17,
7.13, 13.84, 11.63, 12.56, 9.92, 10.45, 11.61, 12.41, 12.07,
9.03, 12.72, 13.68, 8.38, 9.1, 5.56, 13.65, 13.17, 7.84, 10.43,
12.94, 10.96, 11.28, 9.72, 15.08, 9.1, 9.86, 12.96, 12.27, 12.64,
9.04, 14.85, 12.15), C1 = c(2.04, 0.15, -0.2, 0.73, 0.67, 0.15,
1.02, -0.1, -0.12, 0.28, 0.65, 1.17, -0.3, 1.88, 0.34, -1.72,
0.39, 1.09, 0.48, -0.16, -1.09, 0.04, -1.22, -0.62, -0.35, -0.82,
-0.48, 1.66, 0.45, -0.58, -0.97, 1.19, 2.57, 0.23, -0.21, 0.07,
-0.62, 0.37, 1.31, 0.9, 2.04, 0.4, -0.37, -0.4, -1.72, -0.98,
-0.93, 0.7, 1.08, 1.6, 0.88, -0.45, 0.05, 0.61, 0.13, -0.29,
1.61, 0.22, 0.09, 1.53, 0.26, 0.43, 1.34, -0.89, 0.25, 0.22,
-1.21, -0.56, 1.56, -0.93, 0.45, 0.84, -0.93, 0.23, -0.1, -0.78,
0.24, 0.22, -0.42, -0.08, -0.45, -0.73, -1.2, 0.5, -0.03, 2.5,
-1.25, 1.44, 1.29, 0.76, 1.02, -0.23, -0.66, -0.95, -0.4, -0.25,
0.46, -0.25, -1.53, 0.47, -0.02, 0.09, -0.08, 0.25, 0.54, -0.12,
-1.17, -0.98, -1.14, 1.09, -0.8, -1.1, 2.02, 0.53, -0.12, -1.04,
-0.9, 1.11, -0.25, 0.48, 0.98, -0.01, -0.76, -0.18, -0.46, 0.06,
0.13, 2.08, -0.91, -0.31, 0.26, 0.06, -1.57, -0.1, -0.2, 0.06,
-0.18, 1.03, -0.37, -1.19, 0.78, -0.33, -0.94, 0.54, -0.27, -0.37,
0.25, -0.55, 2.58, -0.38, 0.68, 0.89, -0.61, 1.62, -0.3, -0.12,
-1.54, 1.34, -0.36, -0.81, -0.78, -0.43, -1.68, 1.39, 0.27, -0.69,
-0.35, 0.07, -0.76, 1.06, 0.47, 2.2, -1.29, 0.36, 0.62, -0.87,
2.25, -1.22, -0.06, 1.58, 1.28, -1.17, -1.04, -1.75, 0.87, -0.7,
0.55, 1.46, -0.19, 0.59, -0.22, 0.68, 0.59, 0.46, 0.53, 0.99,
0.93, -1.72, 1, 0.37), C2 = c(0.63, -1.01, 0.51, 1.39, -0.03,
-0.68, 1.22, 0.22, 0.35, 0.27, -1.15, 0.14, -0.2, 0.67, 0.35,
0.5, -1.12, -0.01, 1.48, 0.82, 0.68, -0.52, 0.09, 1.58, 1.25,
0.36, 0.64, 0.03, -0.79, 0.49, 0.31, 1.23, -0.12, 0.07, 1.25,
-0.4, -0.44, 0.55, 0.17, -0.05, -1.58, -0.62, 0.3, 0.1, -0.24,
0.16, -0.55, -0.78, -0.08, -0.64, -0.23, -1.13, 1.91, 0.24, -1.3,
0.65, 2.38, 1.47, -1.5, 1.01, -0.86, 1.67, 0.16, -1.43, 0.21,
1.01, 1.28, 0.13, 1.06, -0.01, -0.28, -1.63, 1.63, 0.55, -0.64,
0.41, 1.04, -0.8, -1.44, 0.14, 0.2, 0.73, -0.51, -0.59, 0.67,
-0.11, -2.83, -0.9, 0.46, 0.57, -0.93, 1.18, -0.64, 1.02, -0.15,
0.22, -0.36, -0.82, 0.43, 1.64, -0.21, -0.75, -0.55, 0.22, 2.09,
-0.2, 1.49, 2.2, -0.79, 0.49, -0.06, -2.2, -0.05, 0.95, 1.11,
-1.24, 0.36, -0.24, 1.01, 1.04, -2.62, -0.08, 0.9, -0.83, -1.55,
1.36, 1.75, -0.6, 2.21, -0.26, 0.86, -0.4, -1.81, 0.08, 0.28,
-0.89, 1.07, 0.76, 0.75, 1.5, 0.52, 0.79, 0.1, 0.55, -2.14, -0.99,
1.51, -0.2, 0.32, 0.11, 0.1, 0.2, -1.59, 0.66, 0.25, -0.49, -1.02,
-0.08, 1.45, 1.13, -0.17, -1.08, 1.01, 0.46, -0.01, -1.78, 0.36,
-0.11, -1.65, -1, -0.51, 0.04, 0.28, 0.31, 0.99, 0.29, -0.57,
-0.46, 0.97, -0.1, -0.91, -0.7, -0.73, 1.06, 1.66, -1.96, -0.13,
0.1, -1.71, -1.95, -1.17, 0.66, -0.09, -1.69, -0.05, 0.18, -0.26,
-2.13, 0.36, 1.19), C3 = c(0.05, -0.28, 1.67, -1.17, -0.57, -2.2,
0.98, -0.57, -0.62, -0.12, 0.65, -1.27, 0.41, -0.37, 0.42, -0.51,
0.51, -1.21, -1.69, 0.95, 0.3, 1.39, 0.79, -0.16, 0.79, 0.19,
0.69, -1.28, -0.6, 1.45, 0.27, -0.63, -1.93, 0.85, -0.38, -0.24,
0.15, 1.23, 0.4, -0.4, 0.69, 0.35, 0.18, 1.11, -0.6, -0.84, -0.16,
-0.85, 0.05, 0.75, -0.48, -0.12, -0.22, -0.46, -0.7, 0.31, -1.1,
-0.44, -1.4, 0.95, -0.34, 0.83, 0.33, -0.91, 0.56, 0.52, -1.92,
0.98, 0.98, -0.22, 0.24, 0.72, 1.84, -0.44, 0.93, -0.05, 0.11,
1.48, -0.29, 0.02, -2.65, 0.42, 1.91, 0.58, -1.72, -0.3, -0.43,
1.1, 0.56, 0.89, -0.39, 0.19, 0.67, -0.77, 1.2, 1.07, 0.43, -0.98,
-0.81, 0.42, 1.22, 0.38, 0.97, -0.42, 1.91, 0.93, -0.77, 1.89,
-0.36, 0.36, -0.38, -1.23, -0.45, 0.01, 0.52, -1.31, -1.44, 1.08,
1.04, 1.12, -0.8, 1.49, 0.17, -0.5, 0.16, -1.07, -2.23, 0.81,
0.9, 0.53, -0.35, 1.11, 1.16, 0.99, 1.22, 0.72, -1.55, -1.19,
0.51, -0.32, 0.64, -0.49, -0.82, 2.13, -0.97, -0.59, 0.82, -0.03,
1.7, -0.16, -0.03, -0.35, -0.19, -0.03, 0.38, 0.16, 1.73, -0.2,
-0.17, 0.74, -1.06, -1.55, -0.61, -1.19, -0.57, 0.15, -0.84,
-2.43, -0.86, 0.99, 0.25, 0.08, -1.32, -0.68, 0.17, 1, -1.22,
-0.28, 0.3, 0.6, -2.13, 0.61, -1.61, 1.12, -0.84, -0.53, -0.04,
0.01, 1.37, 1.48, 0.18, 0.09, -1.2, -0.03, 0.7, -0.12, 1.4, 1.13,
1.88, -1.03), C4 = c(-0.89, 1.05, -2.49, 0.07, 0.26, 0.45, -0.39,
-0.8, 1.13, -0.31, 1.38, 0.15, 0.65, 0.28, 0.87, -0.9, 0.62,
1.43, -0.21, 1.72, 0.23, 1.45, -0.76, 0.01, -0.07, 1.13, -0.23,
0.28, -0.11, 0.19, 0.35, 0.19, 1.92, -1.88, -0.48, 0.49, -1.47,
0, 1.96, 0.44, -1.13, 0.93, 1.57, 1.24, 1.29, -0.48, -1.6, 1.15,
0.91, 0.22, 0.27, 0.6, 1.51, 1.22, 1.02, 0.22, -0.96, -0.3, 0.59,
1.79, 0.11, -0.69, -0.04, -0.02, -0.07, -1.05, 1.98, 0.47, -0.55,
1.74, 1.19, 0.72, -0.46, -3.45, -1.47, -1.18, -0.59, 0.63, 0.82,
0.1, -0.34, -2.47, -0.23, 0.53, 0.04, 1.03, 0.82, -0.97, -0.27,
0.76, 0.54, -0.44, 0.32, -0.93, -0.21, 0.06, -0.65, -0.81, 1.4,
-0.61, -1.15, 0.91, -1.82, -1.95, 0.07, -0.13, -0.04, 0.33, -1.21,
0.12, -0.54, -0.13, -0.03, -0.94, 0.98, -1.26, -0.19, -1.23,
-0.03, 0.04, 0.82, 1.37, 0.46, 1.81, 1.61, -2.26, -0.7, -0.31,
-2.88, -3.77, 0.2, -0.73, 0.83, -0.84, 0.88, 1.58, 0.38, 1.15,
-0.99, -0.65, 1.79, -0.2, -0.43, 0.5, 0.42, -0.49, -1.68, -1.94,
0.55, 0.31, 2.58, -1, 1.37, 2.12, 1.16, -1.49, 0.19, 0.68, 1.19,
-0.16, -0.21, -0.21, 1.24, 1.65, 0.31, -0.73, 0.33, -0.66, -0.9,
1.48, 0.1, -1.07, 0.96, -0.83, -1.49, 0.69, 0.3, -0.32, 0.2,
0.29, -1.16, -0.94, -2.37, 1.91, 0.17, -0.27, -1.25, 0.06, 0.19,
-0.14, -0.38, 2.34, -1.5, -0.19, 0.47, -0.08, -0.73, 0.45, 0.3,
0.32), D1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0",
"1"), class = "factor"), D2 = structure(c(1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), D3 = structure(c(2L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L), .Label = c("0", "1"), class = "factor"),
D4 = structure(c(1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L), .Label = c("0",
"1"), class = "factor")), .Names = c("y", "C1", "C2", "C3",
"C4", "D1", "D2", "D3", "D4"), row.names = c(NA, -200L), class =
"data.frame")
a1<-lmrob.control()
a1$nRes<-500
a1$maxit.scale<-500
a1$subsampling<-"nonsingular"
v0<-lmrob(y~.,x0,control=a1)
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