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empirical and model based (logistic) training sample adjustment
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| covariateShift <- function(data, resla, riskfac, ssize=10000){ | |
| ## importance sampling approach | |
| ## when different distributions for the | |
| ## training and test data | |
| require(plyr) | |
| Natsal.riskfac.table <- DistnTable(data, riskfac) | |
| Natsal.riskfac.table <- colNameReplace(Natsal.riskfac.table, "(all)", "Natsalfreq") | |
| res.df <- ldply(resla, data.frame) | |
| LA.riskfac.table <- DistnTable(res.df, riskfac) | |
| LA.riskfac.table <- colNameReplace(LA.riskfac.table, "(all)", "LAfreq") | |
| data.freq <- merge(LA.riskfac.table, Natsal.riskfac.table) | |
| data.freq <- transform(data.freq, ratio = LAfreq/Natsalfreq) | |
| data.freq$ratio[is.na(data.freq$ratio)] <- 0 | |
| datat <- merge(data, data.freq) | |
| set.seed(1968) | |
| sampleRows <- sample(1:nrow(datat), prob=datat$ratio, replace=TRUE, size=ssize) | |
| data.adj <- datat[sampleRows,] | |
| rownames(data.adj) <- NULL | |
| data.adj | |
| } | |
| covariateShift.glm <- function(data, resla, riskfac, ssize=10000){ | |
| ## alternative model-based approach: | |
| ## could fit a logistic regression to estimate the ratio of probabilities of each data set | |
| ## and then predict for (all) permutations | |
| ## http://blog.smola.org/post/4110255196/real-simple-covariate-shift-correction | |
| require(plyr) | |
| res.df <- ldply(resla, data.frame) | |
| res.df <- cbind(res.df, out=0) | |
| data <- cbind(data, out=1) | |
| rdata <- rbind(data[,c(riskfac,"out")], res.df[,c(riskfac,"out")]) | |
| formula <- as.formula(paste("out ~ ", paste(riskfac, collapse="+"), sep="")) | |
| wt <- c(rep(1/nrow(data), nrow(data)), rep(1/nrow(res.df), nrow(res.df))) | |
| fit <- glm(formula, family=binomial, data=rdata, weight=wt) | |
| # grid <- expand.grid(apply(rdata[,riskfac], 2, unique)) | |
| odds <- exp(predict(fit, newdata=data, type="link")) | |
| set.seed(1968) | |
| sampleRows <- sample(1:nrow(data), prob=odds, replace=TRUE, size=ssize) | |
| data.adj <- data[sampleRows,] | |
| rownames(data.adj) <- NULL | |
| data.adj | |
| } | |
| colNameReplace <- function(array, name.before, name.after){ | |
| names(array)[names(array)==name.before] <- name.after | |
| array | |
| } |
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