How to change the order of independent variables for regression summary output in R?

To change the order of independent variables in regression Output, we can pass the variables in the sequence we want while creating the regression model.

For example, if we want to have three independent variables and we want to display first at the last position then it can be done as follows −

lm(DP1~ ind_var_3+ ind_var_2+ind_var_1,data=”data_frame_name”)

Example

Following snippet creates a sample dataframe −

iv1<-rnorm(20) iv2<-rnorm(20) iv3<-rnorm(20) DP1<-rnorm(20,1,0.05) df1<-data.frame(iv1,iv2,iv3,DP1) df1

The following dataframe is created −

output

     iv1            iv2         iv3           DP1 1   0.27622283    0.3993088   0.009604179   0.9870641 2  -1.61822694   -0.8481482   0.455201989   1.0419490 3  -0.16453686   -1.4879353  -0.350820394   0.9798238 4  -1.05644448   -0.6567911   1.345854317   0.9589660 5   0.16128004   -1.5530191   1.248949489   1.0337228 6   0.26490779    0.1905057   0.664826658   0.9612587 7   0.75145959   -0.2902165   0.005533312   1.0167088 8  -0.11785438    0.6260407   1.116348214   1.0087205 9   0.25632653   -0.4080989  -0.314622661   0.9548039 10 -0.70829294   -1.4721428   0.303353402   0.9456278 11  0.96142734   -0.8047216  -1.423814934   1.0133855 12  0.47065716   -0.0145821  -0.871918075   1.0242987 13 -2.23836059    1.7323083  -1.417109201   0.9578229 14  0.76295739   -0.3704564   0.839145422   1.0706470 15  0.40626379    1.9601237   1.457727929   1.0253645 16 -0.75012537   -0.6982455  -1.512548488   0.9916308 17 -0.27124742   -0.9710179   0.284963380   0.9459357 18 -0.26442340    0.6065156  -0.498311289   1.0158016 19 -0.37278740   -0.2710638   0.643670976   0.9794339 20 -0.05907976    0.9741651   0.273533270   1.0329243

Now, to create a regression model for data in df1, add the following code to the above snippet −

Example

Model1<-lm(DP1~iv1+iv2+iv3,data=df1) summary(Model1)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

Call: lm(formula = DP1 ~ iv1 + iv2 + iv3, data = df1) Residuals:     Min      1Q       Median    3Q     Max -0.047785 -0.021889 0.000682 0.018709 0.071298 Coefficients:              Estimate  Std.rror t value  Pr(>|t|) (Intercept) 1.000534  0.008392  119.230  <2e-16 *** iv1         0.016620  0.010299  1.614    0.126 iv2         0.005927  0.008287  0.715    0.485 iv3         0.004480  0.008982  0.499    0.625 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0357 on 16 degrees of freedom Multiple R-squared: 0.1778, Adjusted R-squared: 0.02369 F-statistic: 1.154 on 3 and 16 DF, p-value: 0.3579

To create a regression model for data in df1 with different order of independent variables, add the following code to the above snippet −

Example

Model1<-lm(DP1~iv2+iv1+iv3,data=df1) summary(Model1)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

Call: lm(formula = DP1 ~ iv2 + iv1 + iv3, data = df1) Residuals:     Min      1Q      Median     3Q      Max -0.047785 -0.021889 0.000682 0.018709 0.071298 Coefficients:             Estimate Std. Error   t value  Pr(>|t|) (Intercept) 1.000534 0.008392     119.230   <2e-16 *** iv2         0.005927 0.008287     0.715      0.485 iv1         0.016620 0.010299     1.614      0.126 iv3        0.004480   0.008982     0.499      0.625 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0357 on 16 degrees of freedom Multiple R-squared: 0.1778, Adjusted R-squared: 0.02369 F-statistic: 1.154 on 3 and 16 DF, p-value: 0.3579

Example

Following snippet creates a sample data frame −

x1<-rpois(20,4) x2<-rpois(20,2) x3<-rpois(20,2) x4<-rpois(20,5) y<-rpois(20,10) df2<-data.frame(x1,x2,x3,x4,y) df2

The following dataframe is created −

output

    x1 x2 x3 x4 y 1   3  1  4  7 15 2   6  2  5  1  8 3   7  2  1  5 15 4   6  0  4  6 14 5   4  3  2  2  8 6   3  0  3  6  9 7   1  2  1  9 13 8   7  1  3  5 14 9   3  1  0  6  9 10  5  4  3  8 11 11  6  3  1  7  8 12  2  0  3  1 11 13  2  2  1  5  6 14  5  0  1  4 10 15  4  2  0  4  5 16  5  0  3  5 14 17  2  4  2  7 10 18  5  4  3  4  6 19  3  1  3  1  5 20  3  4  1 4 12

To create a regression model for data in df2 with different order of independent variables, add the following code to the above snippet −

Example

Model2<-lm(y~x3+x2+x4+x1,data=df2) summary(Model2)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

Call: lm(formula = y ~ x3 + x2 + x4 + x1, data = df2) Residuals:    Min    1Q     Median  3Q     Max -3.3049 -2.6574 -0.2113 1.6365 5.1192 Coefficients:      Estimate Std.  Error  t value  Pr(>|t|) (Intercept) 4.7478  2.6181  1.813  0.0898 . x3         0.5544   0.5061  1.095  0.2906 x2        -0.6848   0.4622 -1.482  0.1591 x4         0.7880   0.2979  2.645  0.0184 * x1         0.3886   0.3839  1.012  0.3274 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.873 on 15 degrees of freedom Multiple R-squared: 0.4061, Adjusted R-squared: 0.2478 F-statistic: 2.565 on 4 and 15 DF, p-value: 0.08123