[R-sig-ME] cross-validation

[shahla ebrahimi]

Dear Dr. BolkerGreetings,
I thank you so much for your time, consideration and knowledge.
Best regards,

      From: Ben Bolker <bbolker at gmail.com>
 To: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org> 
 Sent: Friday, August 12, 2016 6:39 PM
 Subject: [R-sig-ME] cross-validation
   
There was a request for cross-validation code some time ago.  This is a
simple example of leave-one-out CV at the level of groups.

dd <- read.table(header=TRUE,
text="
ID TIME EVENT  x1      x2    x3      x4      x5
1  1  0  1.281  0.023  0.875  1.216  0.061
1  2  0  1.270  0.006  0.821  1.005  -0.014
1  3  0  1.053  -0.059  0.922  0.729  0.020
1  4  0  1.113  -0.015  0.859  0.810  0.076
1  5  1  1.220  -0.059  0.887  0.484  0.010
 2  1  0  1.062  0.107  0.815  0.836  0.200
 2  2  0  1.056  0.082  0.879  0.687  0.143
 2  3  0  0.971  0.076  0.907  0.810  0.166
 2  4  0  1.059  0.130  0.818  0.876  0.234
 2  5  0  1.125  0.148  0.759  1.080  0.276
 2  6  0  1.600  0.262  0.546  1.313  0.369
 2  7  0  1.576  0.262  0.564  1.156  0.349
 2  8  0  1.544  0.241  0.591  1.077  0.326
 2  9  0  1.722  0.215  0.552  0.841  0.293
 2  10  0  1.723  0.209  0.534  0.787  0.293
 2  11  0  1.631  0.186  0.548  0.728  0.274
 2  12  0  2.172  0.319  0.441  0.947  0.427
 3  1  0  0.874  -0.035  0.794  0.610  -0.003
 3  2  1  0.825  -0.142  0.952  0.573  -0.019")
require(lme4)
model1 <- glmer(EVENT ~ TIME + (1+TIME|ID)+x1+x2+x3+x4+x5, data=dd,
                family=binomial)

cvfun1 <- function(pred_id,pcut=0.5) {
    train <- subset(dd,!(ID %in% pred_id))
    mm <- suppressWarnings(update(model1, data=train))
    test <- subset(dd,ID %in% pred_id)
    prob <- predict(mm,type="response",newdata=test,re.form=~0)
    outcome <- as.numeric(prob>pcut)
    acc <- mean(outcome==test$EVENT)
    return(acc)
}

sapply(1:3,cvfun1)

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