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authorGertjan van den Burg <gertjanvandenburg@gmail.com>2021-01-12 16:31:08 +0000
committerGertjan van den Burg <gertjanvandenburg@gmail.com>2021-01-12 16:31:08 +0000
commit4c49fabae8e6c4392dd6afa0c0be8731b16f1cee (patch)
tree4a0c1ce0017207f07ff5963433624f83f885f3fb
parentUpdate cran comments (diff)
downloadsparsestep-4c49fabae8e6c4392dd6afa0c0be8731b16f1cee.tar.gz
sparsestep-4c49fabae8e6c4392dd6afa0c0be8731b16f1cee.zip
minor readme fixesHEADmaster
-rw-r--r--README.md6
1 files changed, 2 insertions, 4 deletions
diff --git a/README.md b/README.md
index d77d513..6fb8c22 100644
--- a/README.md
+++ b/README.md
@@ -43,8 +43,7 @@ the [lasso2](https://cran.r-project.org/web/packages/lasso2/index.html)
package. First we load the data and create a data matrix and outcome vector:
```r
-> prostate <-
-> read.table("http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/prostate.data")
+> prostate <- read.table("http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/prostate.data")
> X <- prostate[prostate$train == T, c(-1, -10)]
> X <- as.matrix(X)
> y <- prostate[prostate$train == T, 1]
@@ -127,8 +126,7 @@ transform the input data and disable the intercept:
Note that since we add the constant through the data matrix it is subject to
regularization and therefore sparsity:
-![SparseStep regression on Prostate dataset (with
-constant)](./.github/images/sparsestep_prostate_2.png)
+![SparseStep regression on Prostate dataset (with constant)](./.github/images/sparsestep_prostate_2.png)
For more information and examples, please see the documentation included with
the package. In particular, the following pages are good places to start: