[RsR] robustbase::lmrob()

Kaveh Vakili k@veh@v@k||| @end|ng |rom w|@@ku|euven@be
Sat Jan 19 19:29:40 CET 2013 

Dear all,

I'm trying to estimate a model
using robustbase::lmrob. I get:


Warning message:
In lmrob.fit(x, y, control, init = init) :
   M-step did NOT converge. Returning unconverged M-SM-estimate.


It is not clear to me what parameter
of the lmrob() routine I should modify
to adress this warning...below, i join
  a reproducible example.

Best regards,

x0<-structure(list(y = c(3.99, 0.91, 3.5, 3.4, 2.42, 3.48, 4.07,
4.67, -2.88, 5.13, 1.57, 2.73, 2.43, 3.99, 3.52, -1.12, -0.28,
4.84, 2.16, 3.37, 2.11, 3.1, 6.49, 2.91, 5.06, 1.88, -0.64, -3.34,
2.36, 3.3, 0.07, 1.54, 2.84, 4.86, 1, 1.73, -1.45, 0.06, -0.9,
4.61, -0.9, -1.02, 3.68, 2.31, 3.03, 3.24, 2.5, 0.92, 1, 4.33,
3.15, -0.51, -0.16, 4.44, 1.24, -2.35, 0.3, 3.44, 2.1, 1.03,
-1.33, -0.89, 1.99, 0.18, 1.92, 2.38, 3.33, 4.29, 2.16, 1.12,
1.95, -2.39, 4.63, 1.62, 3.18, 1.26, 3.58, -0.94, 2.08, 4.8,
0.29, 7.91, 0.78, 1.66, -0.71, 0.23, -1.49, 2.23, 2.91, 4.26,
2.59, 0.44, 1.34, 1.68, 0.59, 0.04, 0.84, 1.95, -0.47, 5.49,
2.82, 0.03, 6.5, 0.74, -2.1, 4.95, -1.23, 3, 1.04, 0.45, 2.64,
-1.24, 1.92, 3.48, 4.45, 0.46, 4.42, -0.15, 3.87, 1.44, 359.21,
360.58, 362.31, 360.13, 361.31, 360.2, 360.94, 363.48, 363.29,
360.57, 364.73, 361.29, 363.84, 359.07, 360.63, 362.99, 359.36,
359.92, 363.05, 366.36, 358.93, 362.51, 365.04, 360.55, 363.93,
361.98, 362.45, 362.81, 362.48, 363.86, 362.99, 359.57, 363.78,
361.18, 357.84, 364.83, 364.93, 359.34, 360.67, 361.86, 363.26,
360.44, 361.55, 361.76, 360.85, 363.12, 363.43, 360.41, 363.78,
360.6, 359.32, 362.06, 361.42, 359.6, 359.19, 361.64, 362.91,
360.13, 360.94, 359.32, 362.72, 359.97, 360.78, 361.43, 362.19,
360.04, 361.37, 363.8, 360.42, 365, 362.43, 361.86, 361.43, 363.09,
364.56, 359.67, 359.8, 362.67, 362.65, 361.87), C1 = c(-1.44,
0.62, 0.17, 1.26, 0.96, -0.69, -0.59, 1.94, -0.65, 2.19, -0.57,
-1.57, 0.94, 0.61, 0.28, -1.3, -0.14, 1.58, 1.1, -0.17, 0.51,
0.13, -0.21, -0.68, 0.74, -0.36, 0.22, -0.12, 0.4, 0.44, -1.24,
0.09, -1.61, 0.83, -0.36, 0.08, -0.89, 0.24, 0.74, 0.15, -1.01,
-0.49, -0.01, 0.18, 0.73, -0.14, 1.92, 0.95, -0.12, 1.21, 0.13,
-0.46, -1.04, 0.61, -0.64, -0.2, -0.36, 0.54, 0.05, -2.94, -0.74,
-1.27, -0.47, -1.11, 0.52, -1.09, 1.19, 0.72, 0.35, 1.54, 0.18,
-1.43, -0.53, 0.52, -0.71, 1.34, 0.83, 1.52, 0.43, 1.59, 0.21,
0.99, 1.52, 0.49, -2.23, -0.19, 0.73, -0.69, -1.2, 0.5, 1.02,
-1.44, -0.98, -0.96, -0.71, 0.22, 0.34, 0.02, -0.95, 0.31, 0.79,
0.11, 0.29, -0.11, -0.97, 0.29, -1.32, -0.71, -1.05, 0.11, 1.11,
-2.16, -1.31, 1.2, 0.78, 1.16, 0.58, 0.2, 0.56, 1.21, 88.77,
86.82, 84.98, 88.96, 87.07, 87.83, 89.23, 88.05, 88.49, 88.09,
88.29, 87.56, 87.56, 85.63, 86.07, 88.18, 86.94, 87.4, 87.76,
89.39, 87.17, 87.92, 87.36, 85.43, 90.24, 88.4, 87.82, 89.24,
87.14, 87.28, 88.79, 85.12, 89.8, 89.26, 86.44, 89.82, 86.77,
88.06, 88.21, 86.76, 88.75, 87.69, 88.47, 87.68, 87.88, 89.98,
87.6, 87.73, 89.67, 86.94, 85.89, 89.35, 88.18, 89.41, 87.64,
86.99, 87.8, 87.7, 87.07, 87.99, 89.05, 87.29, 88.04, 86.16,
87.6, 88.66, 87.41, 89.55, 88.17, 87.86, 89.25, 89.42, 87.32,
88.9, 89.8, 86.94, 87.15, 89.33, 88.06, 88.46), C2 = c(1.95,
0.29, -0.01, 1.03, 1.23, 2.18, 0.6, 1.12, -2.92, 0.03, 0.67,
0.72, 1.29, 0.14, 0.96, 0.71, 0.1, 0.26, 1.51, -0.18, -0.06,
-1.35, 1.13, 1.74, 1.66, 0.05, -0.73, -1.04, 0.7, -0.57, -0.16,
0.98, 1.2, 0.42, -0.03, -0.26, 0.07, -0.15, -2.53, -0.75, -0.46,
-1.62, -0.31, 1.18, -1.14, 0.8, 0.17, 1.31, -0.47, 0.93, 0.86,
0.46, -0.08, 1.13, 0.58, -0.97, -2.02, 1.56, -0.99, 2.2, -1.12,
-0.23, -0.13, -0.42, -1.02, 1.02, -0.93, 1.65, -0.3, 1.07, 0.62,
1.62, 0.58, -0.44, 0.59, -0.9, 0.81, -0.38, 1.34, -0.53, -0.23,
1.12, -0.15, -0.52, -0.23, 0.3, -0.91, 0.21, 0.24, 1.8, -0.22,
0.14, 0.19, 0.24, 0.88, -0.55, -1.23, -0.41, -0.54, 1.72, -0.64,
-1.04, 0.3, 1.57, -0.52, 1.33, -0.91, 1.84, 1.94, -1.8, -0.44,
0.08, -0.21, -2.92, -0.92, -1.22, 0.57, -1.25, 0.63, -1.64, 87.17,
86.95, 89.28, 88.39, 87.18, 86.52, 87.44, 89.63, 89.56, 88.11,
88.43, 88.77, 87.82, 89.17, 88.61, 88.36, 87.88, 85.93, 88.24,
88.29, 87.24, 88.41, 89.76, 88.72, 87.87, 87.94, 87.15, 88.04,
88.13, 88.48, 88.28, 87.91, 86.78, 87.88, 87.48, 87.49, 88.79,
88.06, 88.12, 86.16, 88.09, 88.6, 88, 88.38, 87.78, 87.18, 88.67,
87.51, 89.03, 87.52, 88.58, 87.94, 88.92, 86.82, 86.48, 88.92,
89.08, 86.46, 87.94, 88.19, 88.33, 88.24, 88.52, 89.15, 88.5,
87.26, 87.48, 88.56, 86.74, 89.07, 87.98, 88.07, 88.68, 88.53,
89.27, 87.41, 87.14, 88.26, 87.87, 87.71), C3 = c(0.87, -2.31,
0.96, 0.55, -0.63, -0.07, 0.76, -0.31, 0.74, 0.71, 0.73, 0.97,
-1.61, 1.33, -0.54, 0.16, -0.87, 1.47, -0.48, 1.03, 0.43, 1.23,
1.24, -0.57, 0.72, -0.5, -0.38, -1.56, -0.25, 0.82, 0.42, -2.49,
0.22, 1.01, 0.53, -0.19, -1.42, 0.18, 0.17, 0.78, -0.02, 0, 1.61,
-0.64, -0.01, -0.32, -1.49, -0.68, -0.42, 0.49, 0.8, -0.14, -0.94,
1.37, 0.99, -1.73, 1.87, -0.03, 0, -0.25, -0.64, 1.22, 1.24,
-1.66, 0.04, -1.01, 0.63, 0.76, -0.28, 0.01, -0.55, -0.19, -1.61,
-1.4, 0.02, -1.05, -0.75, -2.24, -1.1, -0.2, -0.48, 0.61, -0.85,
1.2, -0.35, -0.18, -1.39, 2.05, 1.06, 0.63, 0.75, 0.2, 0.72,
-2, 0.39, -0.36, 0.32, 0.32, -0.47, 0.79, -0.33, -0.55, 2.04,
-1.64, -0.37, 1.08, 0.08, 0.46, -1.31, -0.11, -0.03, -0.48, -0.02,
1.51, 1, -0.35, 1.83, -0.37, -1.05, -1.68, 85.42, 89.43, 89.79,
85.73, 89.35, 88.26, 86.46, 88.8, 88.43, 87.41, 88.18, 87.02,
88.32, 87.82, 88.97, 88.37, 87.64, 89.14, 87.47, 89.75, 86.3,
89.23, 88.65, 88.68, 87.66, 89.43, 88.82, 87.74, 88.95, 88.63,
87.58, 87.96, 88.58, 87.76, 87.77, 89.07, 89.59, 86.7, 87.31,
89.02, 89.48, 87.41, 88.64, 87.29, 88.48, 88.68, 87.21, 87.88,
87.92, 87.68, 88.31, 86.79, 87.63, 87.45, 87.87, 87.68, 87.74,
87.45, 86.93, 85.55, 87.56, 88.11, 86.86, 87.97, 89.11, 87.11,
87.78, 87.91, 86.67, 89.07, 88.23, 87.49, 87.34, 86.67, 88.83,
88.48, 86.15, 89.12, 87.93, 87.35), C4 = c(-0.81, 1.1, 0.42,
-0.96, -1.08, -1.06, 1.68, 0.22, -0.64, 0.61, -0.34, 1.74, -1.15,
-1.2, 1.04, -1.88, 0.3, -1.5, -2, 0.47, -1.71, 1.45, 1.21, 1.43,
0.42, -0.47, -1.22, -1.99, 0.36, -0.95, -0.08, 0.36, 2.23, 0.53,
-1.05, 0.08, -1.41, -1.48, 0.46, -0.37, -0.67, -0.1, 0.21, -0.85,
0.59, 0.72, -0.2, -2.45, 0.18, -1.36, -1.03, -0.97, -1.58, -1.32,
-0.96, -0.94, -1.25, -1.31, 1.26, -0.27, -1.11, -1.45, -1.29,
0.61, 1.14, -0.16, 0.2, 0.39, -0.89, -2.08, 1.14, -1.65, 2.23,
-0.3, 0.14, -0.18, 0.95, -0.74, 0.04, 1.31, -0.63, 1.44, -1.73,
-0.46, -0.65, 0.16, -0.69, -0.48, -0.12, -0.21, -1.44, 0.05,
-0.19, 1.69, -1.62, -0.54, -1.79, -0.33, -1.18, 0.59, 1.31, 1.04,
-0.15, 0.2, -1.02, 0.13, -0.53, -0.22, 0.73, -0.37, 0.42, -0.4,
0.31, 0.47, 0.14, -0.81, 0.32, -0.68, -0.89, 0.83, 88.14, 87.67,
88.55, 87.34, 88, 87.89, 88.11, 87.3, 87.1, 87.25, 90.11, 88.23,
90.44, 86.73, 87.28, 88.36, 87.19, 87.74, 89.86, 89.21, 88.52,
87.24, 89.57, 88.01, 88.44, 86.49, 88.94, 88.09, 88.56, 89.76,
88.63, 88.87, 88.91, 86.57, 86.44, 88.74, 90.08, 86.81, 87.32,
90.2, 87.22, 87.03, 86.73, 88.71, 87, 87.58, 90.24, 87.58, 87.46,
88.75, 86.83, 88.28, 86.98, 86.21, 87.49, 88.34, 88.58, 88.8,
89.29, 87.88, 88.08, 86.62, 87.65, 88.43, 87.26, 87.31, 89, 88.07,
89.13, 89.29, 87.26, 87.19, 88.37, 89.29, 86.95, 87.13, 89.65,
86.25, 89.08, 88.65), D1 = structure(c(1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L), .Label = c("0", "1"), class = "factor"), D2 = structure(c(2L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 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(1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
     1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
     2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
     1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
     2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
     1L, 2L, 1L, 2L, 2L, 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, 2L, 1L, 1L, 1L,
     2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
     2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
     1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
     1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
     2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0",
     "1"), class = "factor"), D4 = structure(c(1L, 1L, 1L, 1L,
     2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
     2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
     1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
     1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
     1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
     2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
     1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
     2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
     2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
     2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
     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")

     library(robustbase)
     a1<-lmrob.control()
     a1$max.it<-500
     a1$k.max<-500
     a1$k.m_s<-500
     a1$mts<-500
     a1$compute.rd<-FALSE
     v0<-lmrob(y~.,data=x0,init="M-S",control=a1)

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