Plot cpop coefficients

Plot cpop coefficients

plot_cpop(cpop_result, type = "point", s = "lambda.min")

Arguments

cpop_result

The output of cpop_model

type

One of "point", "bar", "text" and "ggraph"

s

lasso s

Examples

data(cpop_data_binary, package = 'CPOP')
## Loading simulated matrices and vectors
x1 = cpop_data_binary$x1
x2 = cpop_data_binary$x2
y1 = cpop_data_binary$y1
y2 = cpop_data_binary$y2
set.seed(1)
cpop_result = cpop_model(x1 = x1, x2 = x2, y1 = y1, y2 = y2, alpha = 1, n_features = 10)
#> Absolute colMeans difference will be used as the weights for CPOP
#> Fitting CPOP model using alpha = 1
#> Based on previous alpha, 0 features are kept 
#> CPOP1 - Step 01: Number of selected features: 0 out of 190
#> CPOP1 - Step 02: Number of selected features: 9 out of 190
#> CPOP1 - Step 03: Number of selected features: 16 out of 190
#> 10 features was reached. 
#> A total of 16 features were selected. 
#> Removing sources of collinearity gives 13 features. 
#> 10 features was reached. 
#> A total of 13 features were selected. 
#> CPOP2 - Sign: Step 01: Number of leftover features: 9 out of 13
#> The sign matrix between the two data:
#>     
#>      -1 0 1
#>   -1  0 0 1
#>   0   0 0 0
#>   1   3 0 0
#> CPOP2 - Sign: Step 02: Number of leftover features: 8 out of 13
#> The sign matrix between the two data:
#>     
#>      -1 0 1
#>   -1  0 0 0
#>   0   0 0 0
#>   1   1 0 0
#> CPOP2 - Sign: Step 03: Number of leftover features: 8 out of 13
#> The sign matrix between the two data:
#>     
#>      -1 0 1
#>   -1  0 0 0
#>   0   0 0 0
#>   1   0 0 0
plot_cpop(cpop_result, type = "point")
#> $plot

#> 
#> $data
#> # A tibble: 8 × 3
#>   coef_name  coef1    coef2
#>   <fct>      <dbl>    <dbl>
#> 1 X01--X10  -0.322 -0.246  
#> 2 X09--X17   0.722  0.521  
#> 3 X11--X14   0.130  0.00292
#> 4 X12--X20   0.404  0.170  
#> 5 X01--X07  -0.437 -0.408  
#> 6 X01--X15  -0.158 -0.334  
#> 7 X01--X17  -0.901 -0.644  
#> 8 X04--X12   0.353  0.431  
#> 
plot_cpop(cpop_result, type = "text")
#> $plot

#> 
#> $data
#> # A tibble: 8 × 3
#>   coef_name  coef1    coef2
#>   <fct>      <dbl>    <dbl>
#> 1 X01--X10  -0.322 -0.246  
#> 2 X09--X17   0.722  0.521  
#> 3 X11--X14   0.130  0.00292
#> 4 X12--X20   0.404  0.170  
#> 5 X01--X07  -0.437 -0.408  
#> 6 X01--X15  -0.158 -0.334  
#> 7 X01--X17  -0.901 -0.644  
#> 8 X04--X12   0.353  0.431  
#> 
plot_cpop(cpop_result, type = "bar")
#> $plot

#> 
#> $data
#> # A tibble: 16 × 3
#>    coef_name coef_key coef_value
#>    <fct>     <chr>         <dbl>
#>  1 X01--X10  coef1      -0.322  
#>  2 X01--X10  coef2      -0.246  
#>  3 X09--X17  coef1       0.722  
#>  4 X09--X17  coef2       0.521  
#>  5 X11--X14  coef1       0.130  
#>  6 X11--X14  coef2       0.00292
#>  7 X12--X20  coef1       0.404  
#>  8 X12--X20  coef2       0.170  
#>  9 X01--X07  coef1      -0.437  
#> 10 X01--X07  coef2      -0.408  
#> 11 X01--X15  coef1      -0.158  
#> 12 X01--X15  coef2      -0.334  
#> 13 X01--X17  coef1      -0.901  
#> 14 X01--X17  coef2      -0.644  
#> 15 X04--X12  coef1       0.353  
#> 16 X04--X12  coef2       0.431  
#> 
plot_cpop(cpop_result, type = "ggraph")
#> $plot

#> 
#> $data
#> # A tibble: 8 × 7
#>   from  to     coef1    coef2 coef_avg coef_abs sign_coef1
#>   <chr> <chr>  <dbl>    <dbl>    <dbl>    <dbl> <chr>     
#> 1 X01   X10   -0.322 -0.246    -0.284    0.284  Negative  
#> 2 X09   X17    0.722  0.521     0.621    0.621  Positive  
#> 3 X11   X14    0.130  0.00292   0.0666   0.0666 Positive  
#> 4 X12   X20    0.404  0.170     0.287    0.287  Positive  
#> 5 X01   X07   -0.437 -0.408    -0.423    0.423  Negative  
#> 6 X01   X15   -0.158 -0.334    -0.246    0.246  Negative  
#> 7 X01   X17   -0.901 -0.644    -0.773    0.773  Negative  
#> 8 X04   X12    0.353  0.431     0.392    0.392  Positive  
#>