sumlog: Identifying outlier using sum oflogs based meta-analysis...
In BudhlakotiN/OGS: Outlier in Genomics Data
Description
Usage
Arguments
Details
Value
References
Examples
Description
Identifies outlier in genomics data by combining various LASSO diagnostic followed by meta-analysis using sum of logs, also known as Fisher’s method.
Usage
1
OGS.sumlog(X,Y)
Arguments
X
X is a design matrix of marker genotype of size n×p where n are no of
Individuals under study (i.e. genotype, lines) and p are no of markers
Y
Y is a vector of response/individuals of size n×1
Details
This function first combines various LAASO diagnostic proposed for high dimensional regression i.e. df-model, df-lambda, df-regpath and df-cvpath and produce a single p-value for each individuals/observation using sum of logs method. Using a suitable p-value cut-off (i.e. .01 or .05), outlier can be identified.
Value
$p-values lists p-value for each observation/individuals/lines
References
Rajaratnam, B., Roberts, S., Sparks, D. & Yu, H. Influence Diagnostics for High-Dimensional Lasso Regression. Journal of Computational and Graphical Statistics, 1-14 (2019).
Becker BJ (1994). “Combining significance levels.” In Cooper H, Hedges LV (eds.), A handbook of research synthesis, 215–230. Russell Sage, New York.
Examples
1
2
3
4
5
library(STGS)
data(wheat_data)
X<-wheat_data[,1:100]
Y<-as.data.frame(wheat_data[,101])
OGS.sumlog(X,Y)
BudhlakotiN/OGS documentation built on Jan. 6, 2020, 12:43 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value References Examples
Description
Identifies outlier in genomics data by combining various LASSO diagnostic followed by meta-analysis using sum of logs, also known as Fisher’s method.
Usage
1 | OGS.sumlog(X,Y)
|
Arguments
X |
X is a design matrix of marker genotype of size n×p where n are no of Individuals under study (i.e. genotype, lines) and p are no of markers |
Y |
Y is a vector of response/individuals of size n×1 |
Details
This function first combines various LAASO diagnostic proposed for high dimensional regression i.e. df-model, df-lambda, df-regpath and df-cvpath and produce a single p-value for each individuals/observation using sum of logs method. Using a suitable p-value cut-off (i.e. .01 or .05), outlier can be identified.
Value
$p-values lists p-value for each observation/individuals/lines
References
Rajaratnam, B., Roberts, S., Sparks, D. & Yu, H. Influence Diagnostics for High-Dimensional Lasso Regression. Journal of Computational and Graphical Statistics, 1-14 (2019).
Becker BJ (1994). “Combining significance levels.” In Cooper H, Hedges LV (eds.), A handbook of research synthesis, 215–230. Russell Sage, New York.
Examples
1 2 3 4 5 | library(STGS)
data(wheat_data)
X<-wheat_data[,1:100]
Y<-as.data.frame(wheat_data[,101])
OGS.sumlog(X,Y)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.