cv {roccv}

Cross validation results for a model

Description

Cross validation results for a model

Usage

cv(clinical_x = NULL, genomic_x = NULL, y = NULL, data = NULL,
  clinical_formula = NULL, family = "binomial", folds = NULL, k = 10,
  fit_method = "glm", method_name = NULL, n.cores = 1, ...)

Arguments

clinical_x

clinical variables that will always be included in the model

genomic_x

genomic variables that will be penalized if a penalized model is used

y

response variables

data

dataframe if clinical formula is used

clinical_formula

formula for clinical variables

family

gaussian, binomial or poisson

folds

predefined partions for cross validation

k

number of cross validation folds. A value of k=n is leave one out cross validation.

fit_method

glm or glmnet used to fit the model

method_name

tracking variable to include in return dataframe

n.cores

Number of cores to be used

...

additional commmands to glm or cv.glmnet

Value

returns a dataframe of predicted values and observed values. In addition, method_name is recorded if that variable is defined.

Author(s)

Ben Sherwood <[e-mail]>

Examples

x <- matrix(rnorm(800),ncol=8)
y <- runif(100) < exp(1 + x[,1] + x[,5])/(1+exp(1 + x[,1] + x[,5]))
cv_results <- cv(x,y=y,method_name="without_formula")
combined_data <- data.frame(y=y,x1=x[,1],x5=x[,5])
gx <- x[,c(2,3,4,6,7,8)]
cvf <- cv(genomic_x=gx,clinical_formula=y~x1+x5,data=combined_data,method_name="with_form")