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colrns

Correlation Learning for Variable Selection (COLRNS)

Description

This function allows you to implement COLRNS for high dimensional data analysis with continuous responses. This package is based on the 'glmnet' package (Friedman et al., 2019)

Usage

colrns(
  x,
  y,
  penalty.factor = rep(1, nvars),
  nfolds = 10,
  type.measure = "deviance",
  alpha = seq(0.1, 0.9, by = 0.1),
  lambda.min = TRUE,
  cutoff = 0.5,
  r.group = 0.3,
  niter = 150
)

Arguments

Argument Description
x Design matrix including predictors and confounders
y Response vector (continuous)
penalty.factor Separate penalty factor can be applied to each coefficient. It can be 0 for some variables. If 0, corresponding variables are always included in the model. Default is 1 for all variables.
nfolds Number of folds that is used in cross-validation. Default is 10.
type.measure Measurement to be optimized in cross-validation. Default is "deviance", which uses squared-error for gaussian models (a.k.a type.measure="mse"). Mean absolute error "mae" is another option.
alpha Grid for the parameter alpha, the proportion between the lasso penalty (alpha=1) and the ridge penalty (alpha=0). Default is from 0.1 to 0.9 with the interval of 0.1.
lambda.min Whether to estimate the coefficients at lambda.min or lambda.1se. Using lambda.1se tends to have a more sparse solution. Default is lambda.min.
cutoff Cutoff to construct the lead cluster. Default is 0.5.
r.group Minimum ratio of predictors to be included in the lead cluster. The minimum number of predictors recruited in the lead cluster d can be n-1 or n/log(n) (Fan and Lv, 2008), or 2n/log(n) (Zhong and Zhu, 2015). The minimum ratio can be d/number of total predictors*100. When sample size n is high, we recommend to use the number between 0.2 and 0.4. Default is 0.3.
niter Number of iterative procedures. Default is 150.

Value

coefficients: A named vector of coefficients.

select.vars: A list of selected variables, that is, the variables with non-zero coefficients.

References

Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer, New York

Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical Learning with Sparsityy: the LASSO and Generalizations. Boca Raton, FL, USA: CRC Press.

Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Simon, N., and Qian, J. (2019). Lasso and elastic-net regularized generalized linear models.

Jang, J. (2020). Variable Selection in Presence of Strong Collinearity with Application to Environmental Mixtures. PhD Dissertation, University of Illinois at Chicago

Examples

### NANES Persistent Organic Pollutant (POPs) Dataset (available in the github 'colrns' repository)
nhs.data <- read.csv("nhanes.pops.data.csv",sep=",", header=TRUE)
y = as.vector(nhs.data[,1]) #log-transformed longer leukocyte telomere length
x = as.matrix(nhs.data[,2:37]) #18 log-transformed POPs exposures and 18 confounders
### Fit the model (not penalizing confounders)
colrns(y=y, x=x, penalty.factor= c(rep(1,18),rep(0,18)))