# Estimate subject loading of testing data This function estimates the subject loading of the testing data based on feature and temporal loading from training data, so that both the testing data and training data have the same dimensionality reduction. ```r est_test_subject(datlist, res_tempted, mean_svd = NULL) ``` ## Arguments - `datlist`: Testing data formatted into datlist in the same fashion as the training data. The same transformation needs to be used for both training and testing data. - `res_tempted`: Result from `tempted` ran on the training data. - `mean_svd`: Result from `svd_centralize` ran on the training data. ## Returns estimated subject loading of testing data ## Examples ```r # Take a subset of the samples so the example runs faster # Here we are taking samples from the odd months sub_sample <- rownames(meta_table)[(meta_table$day_of_life%/%12)%%2==1] count_table_sub <- count_table[sub_sample,] processed_table_sub <- processed_table[sub_sample,] meta_table_sub <- meta_table[sub_sample,] # split the example data into training and testing id_test <- meta_table_sub$studyid=="2" count_train <- count_table_sub[!id_test,] meta_train <- meta_table_sub[!id_test,] count_test <- count_table_sub[id_test,] meta_test <- meta_table_sub[id_test,] # run tempted on training data datlist_train <- format_tempted(count_train, meta_train$day_of_life, meta_train$studyid, threshold=0.95, pseudo=0.5, transform="clr") mean_svd_train <- svd_centralize(datlist_train, r=1) res_tempted_train <- tempted(mean_svd_train$datlist, r=2, smooth=1e-5) # get the overlapping features count_test <- count_test[,rownames(datlist_train[[1]])[-1]] datlist_test <- format_tempted(count_test, meta_test$day_of_life, meta_test$studyid, threshold=1, pseudo=0.5, transform="clr") # estimate the subject loading of the testing subject sub_test <- est_test_subject(datlist_test, res_tempted_train, mean_svd_train) # train logistic regression classifier on training subjects metauni <- unique(meta_table_sub[,c("studyid", "delivery")]) rownames(metauni) <- metauni$studyid Atrain <- as.data.frame(res_tempted_train$A_hat) Atrain$delivery <- metauni[rownames(Atrain),"delivery"]=="Cesarean" glm_train <- glm(delivery ~ PC1+PC2, data=Atrain, family=binomial(link="logit")) summary(glm_train) # predict the label of testing subject, whose true label is "Cesarean" predict(glm_train, newdata=as.data.frame(sub_test), type="response") ``` ## References Shi P, Martino C, Han R, Janssen S, Buck G, Serrano M, Owzar K, Knight R, Shenhav L, Zhang AR. (2023) **Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies**. bioRxiv. doi: 10.1101/550749. [https://www.biorxiv.org/content/10.1101/550749](https://www.biorxiv.org/content/10.1101/550749).

est_test_subject function