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* [[http://genome-tech.ucsd.edu/LabNotes/index.php/noi:DMR220k_LabNotes '''Link to calendar''']] = Regression analysis of UCLA SZ data (disease status) = * Continued from: http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2012-6-13 * From the comment on previous page ** LDA: random choose samples for training and (have UCLA-SZ and UCSD-GA on training and testing data sets) * Training and testing data set (I used the same group of training and testing data as assigned for age analysis) ** list 1-96 =>UCSD-SZ, 97-169 => UCSD-GA ** trainAge <- c(1:40, 91:132) **testAge <- c(41:90, 133:169) * From the previous analysis, I used PC1-PC3 scores as input for LDA analysis. However when I plot scree plot of the variances contributed on each PC, the change in slope in the scree plot occurs at component 4. In this analysis, I'll put PC1-PC4 scores as input. A=read.table("UCLA_regression_status_pval0.015_MM",header=TRUE,row.names=1) B=na.omit(A) B$STDEV=NULL B$min_RD=NULL B$mean_RD=NULL pca1 <- prcomp(t(B), scale=TRUE) screeplot(pca1, type="line") [[File:screeplot_status_pval0.015.png| 400px]] '''Script:'''[[Media:lda_status_PC1-4_v2.txt| lda_status_PC1-4_v2.R]] [[File:plot_LD1_status_PC1-4_p.015_v2_training.png| 550px]] [[File:plot_LD1_status_PC1-4_p.015_v2_test.png| 550px]] '''Table of raw data'''<br> {| {{table}} border = 1 | align="center" style="background:#f0f0f0;"|'''Status (p-value 0.015)''' | align="center" style="background:#f0f0f0;"|'''Predicted\Truth''' | align="center" style="background:#f0f0f0;"|'''Affected''' | align="center" style="background:#f0f0f0;"|'''Unaffected''' | align="center" style="background:#f0f0f0;"|'''Correct''' | align="center" style="background:#f0f0f0;"|'''FALSE''' | align="center" style="background:#f0f0f0;"|'''Total''' |- | Training||Affected||10||0||82||0||82 |- | ||Unaffected||0||72|||||| |- | |- | Testing||Affected||10||2||83||4||87 |- | ||Unaffected||2||73|||||| |- | |- | Cross validation||Affected||19||2||164||5||169 |- | ||Unaffected||3||145|||||| |} '''Table of accuracy and misclassification rate'''<br> {| {{table}} border = 1 | align="center" style="background:#f0f0f0;"|'''Training (Accuracy of allocation)''' | align="center" style="background:#f0f0f0;"|'''Misclassicication rate''' | align="center" style="background:#f0f0f0;"|'''Testing (quality of model)''' | align="center" style="background:#f0f0f0;"|'''Misclassicication rate''' | align="center" style="background:#f0f0f0;"|'''Cross validation''' | align="center" style="background:#f0f0f0;"|'''Misclassicication rate''' |- | 82 of 82||0.00%||83 of 87||4.60%||164 of 169 ||2.96% |} '''Compare the result to previous analysis by treating UCLA-SZ as training data and UCSD-GA as testing data {| {{table}} border = 1 | align="center" style="background:#f0f0f0;"|'''Training (Accuracy of allocation)''' | align="center" style="background:#f0f0f0;"|'''Misclassicication rate''' | align="center" style="background:#f0f0f0;"|'''Testing (quality of model)''' | align="center" style="background:#f0f0f0;"|'''Misclassicication rate''' | align="center" style="background:#f0f0f0;"|'''Cross validation''' | align="center" style="background:#f0f0f0;"|'''Misclassicication rate''' |- | 92 of 96||4.17%||73 of 73||0.00%||164 of 169||2.96% |} '''Summary'''
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