Hello I am using the step function in order to do backward selection for a
linear model of 52 variables with the following commands:

object<-lm(vars[,1] ~ (vars[,2:(ncol(predictors)+1)]-1))
BackS<-step(object,direction="backward")

but it isn't dropping any if the variables in the model, but there are lots
of not significant variables as you can see here

> object<-lm(vars[,1] ~ (vars[,2:(ncol(predictors)+1)]-1))
> summary(object)
Call:
lm(formula = vars[, 1] ~ (vars[, 2:(ncol(predictors) + 1)] -
    1))
Residuals:
     Min       1Q   Median       3Q      Max
-0.56388 -0.10762 -0.01433  0.08495  0.82477
Coefficients:
                                                           Estimate Std.
Error t value Pr(>|t|)
vars[, 2:(ncol(predictors) + 1)]SS.1                       0.028772
0.025458   1.130 0.260896
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[1]  -0.308076
0.096243  -3.201 0.001795 **
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[2]   0.130134
0.101734   1.279 0.203559
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[3]   0.014345
0.106282   0.135 0.892887
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[4]  -0.175958
0.107097  -1.643 0.103268
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[5]   0.016270
0.106081   0.153 0.878391
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[6]  -0.089018
0.091132  -0.977 0.330834
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[7]  -0.270550
0.075537  -3.582 0.000512 ***
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[8]  -0.106691
0.074448  -1.433 0.154694
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[9]   0.118962
0.076886   1.547 0.124699
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[10] -0.055112
0.076225  -0.723 0.471218
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[11] -0.135113
0.076307  -1.771 0.079415 .
vars[, 2:(ncol(predictors) + 1)]Precio.Promedio.Bolsa[12]  0.082478
0.075130   1.098 0.274707
vars[, 2:(ncol(predictors) + 1)]Anomalia[0]                0.123054
0.213980   0.575 0.566426
vars[, 2:(ncol(predictors) + 1)]Anomalia[1]                0.078511
0.507544   0.155 0.877353
vars[, 2:(ncol(predictors) + 1)]Anomalia[2]               -0.399726
0.581594  -0.687 0.493357
vars[, 2:(ncol(predictors) + 1)]Anomalia[3]               -0.002103
0.583109  -0.004 0.997129
vars[, 2:(ncol(predictors) + 1)]Anomalia[4]                0.596937
0.678115   0.880 0.380640
vars[, 2:(ncol(predictors) + 1)]Anomalia[5]               -0.547555
0.710687  -0.770 0.442695
vars[, 2:(ncol(predictors) + 1)]Anomalia[6]               -0.142452
0.678536  -0.210 0.834106
vars[, 2:(ncol(predictors) + 1)]Anomalia[7]                0.506431
0.692960   0.731 0.466455
vars[, 2:(ncol(predictors) + 1)]Anomalia[8]               -0.117177
0.662596  -0.177 0.859958
vars[, 2:(ncol(predictors) + 1)]Anomalia[9]               -0.550570
0.563421  -0.977 0.330638
vars[, 2:(ncol(predictors) + 1)]Anomalia[10]               0.799499
0.555007   1.441 0.152587
vars[, 2:(ncol(predictors) + 1)]Anomalia[11]              -0.577416
0.504046  -1.146 0.254485
vars[, 2:(ncol(predictors) + 1)]Anomalia[12]               0.204479
0.221030   0.925 0.356948
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[0]       -0.572351
1.303885  -0.439 0.661561
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[1]        0.270387
1.715912   0.158 0.875082
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[2]        1.939207
1.806931   1.073 0.285549
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[3]        1.501964
1.779253   0.844 0.400432
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[4]        1.292790
1.759802   0.735 0.464147
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[5]        1.197978
1.760600   0.680 0.497670
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[6]        0.338068
1.720709   0.196 0.844608
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[7]       -2.197186
1.616212  -1.359 0.176805
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[8]       -2.050263
1.542936  -1.329 0.186687
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[9]       -0.103823
1.541956  -0.067 0.946441
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[10]       0.349220
1.545823   0.226 0.821693
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[11]      -0.654607
1.476141  -0.443 0.658313
vars[, 2:(ncol(predictors) + 1)]demanda.nacional[12]      -0.254144
1.193506  -0.213 0.831772
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[0]          -1.500119
0.428395  -3.502 0.000671 ***
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[1]          -1.058775
0.475011  -2.229 0.027869 *
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[2]           0.818735
0.497920   1.644 0.102994
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[3]           0.057331
0.528216   0.109 0.913769
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[4]          -0.529271
0.519284  -1.019 0.310350
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[5]          -0.649193
0.508210  -1.277 0.204171
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[6]           0.511649
0.490911   1.042 0.299605
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[7]          -0.545404
0.473994  -1.151 0.252392
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[8]          -0.314593
0.489687  -0.642 0.521939
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[9]          -0.091112
0.510613  -0.178 0.858712
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[10]         -0.030684
0.492553  -0.062 0.950442
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[11]          0.162751
0.488237   0.333 0.739515
vars[, 2:(ncol(predictors) + 1)]Nivel.Embalse[12]          0.370126
0.458473   0.807 0.421250
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2159 on 109 degrees of freedom
Multiple R-squared: 0.566,      Adjusted R-squared: 0.359
F-statistic: 2.734 on 52 and 109 DF,  p-value: 5.24e-06

do you know how can I do this? how can I do backward selection on a
regression without an intercept? Thank you

Felipe Parra

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