Description Usage Arguments Details Value References See Also Examples
Compute the R squared value for a given cluster or group of variables.
1 2  compute_r2(x, y, res.test.hierarchy, clvar = NULL,
family = c("gaussian", "binomial"), colnames.cluster = NULL)

x 
a matrix or list of matrices for multiple data sets. The matrix or matrices have to be of type numeric and are required to have column names / variable names. The rows and the columns represent the observations and the variables, respectively. 
y 
a vector, a matrix with one column, or list of the aforementioned objects for multiple data sets. The vector, vectors, matrix, or matrices have to be of type numeric. 
res.test.hierarchy 
the output of one of the functions

clvar 
a matrix or list of matrices of control variables. 
family 
a character string naming a family of the error distribution;
either 
colnames.cluster 
The column names / variables names of the cluster of interest. If not supplied, the R squared value of the full model is computed. 
The R squared value is computed based on the output of the multisample
splitting step. For each split, the intersection of the cluster / group
(specified in colnames.cluster
) and the selected variables is taken
and R squared values are computed based on the second halves of observations.
Finally, the R squared values are averaged over the B
splits and over
the different data sets if multiple data sets are supplied.
For a continuous response, the adjusted R squared values is
calculated for a given cluster or group of variables. The Nagelkerke’s
R squared values is computed for a binary response using the function
NagelkerkeR2
.
If colnames.cluster
is not supplied, the R squared value of the
full model is computed.
The returned value is the R squared value.
Renaux, C. et al. (2018), Hierarchical inference for genomewide association studies: a view on methodology with software. (arXiv:1805.02988)
Nagelkerke, N. J. et al. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78:691–692.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  n < 200
p < 500
library(MASS)
set.seed(3)
x < mvrnorm(n, mu = rep(0, p), Sigma = diag(p))
colnames(x) < paste0("Var", 1:p)
beta < rep(0, p)
beta[c(5, 20, 46)] < 1
y < x %*% beta + rnorm(n)
dendr < cluster_var(x = x)
set.seed(47)
sign.clusters < test_hierarchy(x = x, y = y, dendr = dendr,
family = "gaussian")
compute_r2(x = x, y = y, res.test.hierarchy = sign.clusters,
family = "gaussian",
colnames.cluster = c("Var1", "Var5", "Var8"))

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