ss: The Study Strap for Multi-Study Learning: Fits Study Strap...

In studyStrap: Study Strap and Multi-Study Learning Algorithms

Description

The Study Strap for Multi-Study Learning: Fits Study Strap algorithm

Usage

ss(formula = Y ~ ., data, target.study = NA,
  bag.size = length(unique(data$Study)), straps = 150,
  stack = "standard", sim.covs = NA, ssl.method = list("lm"),
  ssl.tuneGrid = list(c()), sim.mets = FALSE, model = FALSE,
  customFNs = list(), stack.standardize = FALSE)

Arguments

formula	Model formula
data	A dataframe with all the studies has the following columns in this order: "Study", "Y", "V1", ...., "Vp"
target.study	Dataframe of the design matrix (just covariates) of study one aims to make predictions on
bag.size	Integer indicating the bag size tuning parameter.
straps	Integer indicating the maximum number of study straps to generate and fit models with.
stack	String taking values "standard" or "ss" specifying how to fit the stacking regression. "standard" option uses all studies as the "test" studies. "ss" uses all the study straps as "test" studies.
sim.covs	Is a vector of names of covariates or the column numbers of the covariates to be used for the similarity measure. Default is to use all covariates.
ssl.method	A list of strings indicating which modeling methods to use.
ssl.tuneGrid	A list of the tuning parameters in the format of the caret package. Each element must be a dataframe (as required by caret). If no tuning parameters are required then NA is indicated.
sim.mets	Boolean indicating whether to calculate default covariate profile similarity measures.
model	Indicates whether to attach training data to model object.
customFNs	Optional list of functions that can be used to add custom covaraite profile similarity measures.
stack.standardize	Boolean determining whether stacking weights are standardized to sum to 1. Default is FALSE

Value

A model object of studyStrap class "ss" that can be used to make predictions.

Examples

##########################
##### Simulate Data ######
##########################

set.seed(1)
# create half of training dataset from 1 distribution
X1 <- matrix(rnorm(2000), ncol = 2) # design matrix - 2 covariates
B1 <- c(5, 10, 15) # true beta coefficients
y1 <- cbind(1, X1) %*% B1

# create 2nd half of training dataset from another distribution
X2 <- matrix(rnorm(2000, 1,2), ncol = 2) # design matrix - 2 covariates
B2 <- c(10, 5, 0) # true beta coefficients
y2 <- cbind(1, X2) %*% B2

X <- rbind(X1, X2)
y <- c(y1, y2)

study <- sample.int(10, 2000, replace = TRUE) # 10 studies
data <- data.frame( Study = study, Y = y, V1 = X[,1], V2 = X[,2] )

# create target study design matrix for covariate profile similarity weighting and
# accept/reject algorithm (covaraite-matched study strap)
target <- matrix(rnorm(1000, 3, 5), ncol = 2) # design matrix
colnames(target) <- c("V1", "V2")

##########################
##### Model Fitting #####
##########################

# Fit model with 1 Single-Study Learner (SSL): PCA Regression
ssMod1 <- ss(formula = Y ~.,
            data = data,
            target.study = target,
            bag.size = length(unique(data$Study)),
            straps = 5,
            stack = "standard",
            sim.covs = NA,
            ssl.method = list("pcr"),
            ssl.tuneGrid = list(data.frame("ncomp" = 1)),
            sim.mets = TRUE,
            model = TRUE,
            customFNs = list() )

# Fit model with 2 SSLs: Linear Regression and PCA Regression
ssMod2 <- ss(formula = Y ~.,
            data = data,
            target.study = target,
            bag.size = length(unique(data$Study)),
            straps = 10,
            stack = "standard",
            sim.covs = NA,
            ssl.method = list("lm","pcr"),
            ssl.tuneGrid = list(NA, data.frame("ncomp" = 2)),
            sim.mets = TRUE,
            model = TRUE,
            customFNs = list( ) )



# Fit model with custom similarity function for
# covariate profile similarity weighting

fn1 <- function(x1,x2){
return( abs( cor( colMeans(x1), colMeans(x2) )) )
}

ssMod3<- ss(formula = Y ~.,
            data = data,
            target.study = target,
            bag.size = length(unique(data$Study)),
            straps = 10,
            stack = "standard",
            sim.covs = NA,
            ssl.method = list("lm","pcr"),
            ssl.tuneGrid = list(NA, data.frame("ncomp" = 2)),
            sim.mets = TRUE,
            model = TRUE, customFNs = list(fn1) )

#########################
#####  Predictions ######
#########################

preds <- studyStrap.predict(ssMod1, target)

studyStrap documentation built on Feb. 20, 2020, 5:08 p.m.