update read me

R/posi.R

@@ -55,10 +55,10 @@ debias_plmm <- function(x, y, series, plmm_output, a = 1, Z = NULL) {
   y_a <- Sigma_a_sqrt_inv %*% y_offset
 
   if (is.null(Z)) {
-    de_sparsified <- hdi::lasso.proj(x_a, y_a,
+    de_sparsified <- suppressMessages(hdi::lasso.proj(x_a, y_a,
       suppress.grouptesting = TRUE, return.Z = TRUE,
       do.ZnZ = TRUE, betainit = "scaled lasso"
-    )
+    ))
 
     debias_score_matrix <- de_sparsified$Z
   } else {

README.Rmd

@@ -78,9 +78,11 @@ plmm_output <- plmm_lasso(x, y, series, t,
   gamma = gamma, lambda = lambda, timexgroup = TRUE,
   criterion = "BIC"
 )
+plmm_output$lasso_output$theta
 ```
 
-With a larger lasso penalty more coefficients are set to zero. The coefficients associated to the nonlinear functions are alpha.
+With a larger lasso penalty more coefficients are set to zero.
+The coefficients associated to the nonlinear functions are alpha.
 ```{r}
 head(plmm_output$lasso_output$alpha)
 ```
@@ -102,7 +104,7 @@ tuned_plmm <- tune_plmm(x, y, series, t,
 ```
 
 
-The function tuned_plmm tries every possible combination of the values from lambdas and gamma and returns the model with the best BIC. This example is for illustration only and typicaly wider and finer grid should be used.
+The function tuned_plmm tries every possible combination of the values from lambdas and gamma and returns the model with the best BIC. This example is for illustration only, in practice a more exhaustive grid should be used.
 
 ### Plotting the results
 The function plot_fit allows for the vizualisation of the estimated mean trajectories as well as the estimate of the nonlinear functions. By default only the observed time points are being used, to use predicted time points the argument predicted can be set to TRUE.

README.md

@@ -82,6 +82,9 @@ plmm_output <- plmm_lasso(x, y, series, t,
   gamma = gamma, lambda = lambda, timexgroup = TRUE,
   criterion = "BIC"
 )
+plmm_output$lasso_output$theta
+#> Intercept     group        x1        x2        x3        x4        x5 
+#> 3.5544003 0.4262652 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
 ```
 
 With a larger lasso penalty more coefficients are set to zero. The
@@ -114,7 +117,7 @@ tuned_plmm <- tune_plmm(x, y, series, t,
 
 The function tuned_plmm tries every possible combination of the values
 from lambdas and gamma and returns the model with the best BIC. This
-example is for illustration only and typicaly wider and finer grid
+example is for illustration only, in practice a more exhaustive grid
 should be used.
 
 ### Plotting the results
@@ -144,9 +147,6 @@ used.
 
 ``` r
 debias_plmm(x, y, series, tuned_plmm)
-#> Nodewise regressions will be computed as no argument Z was provided.
-#> You can store Z to avoid the majority of the computation next time around.
-#> Z only depends on the design matrix x.
 #>         Estimate   Debiased Std. Error   Lower 95% Upper 95%      p-value
 #> group 3.20441442 3.31447827 0.33608394  2.65575376 3.9732028 6.079223e-23
 #> x1    1.95569696 2.03694234 0.21339004  1.61869786 2.4551868 1.352832e-21