> AML <- read.table('C:\\Aqing\\Collaboration\\Martincic_Danko\\AML.txt', header = TRUE) > names(AML) > dim(AML) > AML[1:10,] > AML$TGIF_RT_PCR[AML$TGIF_RT_PCR==50] <- NA > AML$tgif_hybridization[AML$tgif_hybridization==50] <- NA > plot(TGIF_RT_PCR ~ tgif_hybridization, data=AML) > areg.boot(TGIF_RT_PCR ~ tgif_hybridization, data=AML, B=20) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 avas Additive Regression Model areg.boot(x = TGIF_RT_PCR ~ tgif_hybridization, data = AML, B = 20) Frequencies of Missing Values Due to Each Variable TGIF_RT_PCR tgif_hybridization 4 1 n= 55 p= 1 Apparent R2 on transformed Y scale: 0.301 Bootstrap validated R2 : -0.028 Coefficients of standardized transformations: Intercept tgif_hybridization 1.104798e-07 1.007944e+00 Residuals on transformed scale: Min 1Q Median 3Q Max Mean S.D. -1.902429e+00 -5.399164e-01 -1.646699e-01 5.652760e-01 2.104872e+00 -1.677951e-17 8.435034e-01 > areg.boot(TGIF_RT_PCR ~ tgif_hybridization, data=AML, method="ace",B=20) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ace Additive Regression Model areg.boot(x = TGIF_RT_PCR ~ tgif_hybridization, data = AML, B = 20, method = "ace") Frequencies of Missing Values Due to Each Variable TGIF_RT_PCR tgif_hybridization 4 1 n= 55 p= 1 Apparent R2 on transformed Y scale: 0.411 Bootstrap validated R2 : -0.513 Coefficients of standardized transformations: Intercept tgif_hybridization 1.775225e-17 9.948417e-01 Residuals on transformed scale: Min 1Q Median 3Q Max Mean S.D. -1.585702e+00 -4.971269e-01 -1.039891e-02 5.413493e-01 1.731166e+00 -1.097607e-17 7.747645e-01 'areg.boot' uses 'avas' or 'ace' to fit additive regression models allowing all variables in the model (including the right-hand-side) to be transformed, with transformations chosen so as to optimize certain criteria. For 'method="avas"' the response transformation is restricted to be monotonic. > cor.test(AML$TGIF_RT_PCR, AML$tgif_hybridization, method="spearman") Spearman's rank correlation rho data: AML$TGIF_RT_PCR and AML$tgif_hybridization S = 37712, p-value = 0.007157 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.3604691 Warning message: p-values may be incorrect due to ties in: cor.test.default(AML$TGIF_RT_PCR, AML$tgif_hybridization, method = "spearman") > spearman2(AML$TGIF_RT_PCR, AML$tgif_hybridization) rho2 F df1 df2 P n Adjusted rho2 0.12993798 7.91519755 1.00000000 53.00000000 0.00686233 55.00000000 0.11352171 'spearman2' computes the square of Spearman's rho rank correlation and a generalization of it in which 'x' can relate non-monotonically to 'y'. This is done by computing the Spearman multiple rho-squared between '(rank(x), rank(x)^2)' and 'y'. When 'x' is categorical, a different kind of Spearman correlation used in the Kruskal-Wallis test is computed (and 'spearman2' can do the Kruskal-Wallis test). This is done by computing the ordinary multiple 'R^2' between 'k-1' dummy variables and 'rank(y)', where 'x' has 'k' categories. 'x' can also be a formula, in which case each predictor is correlated separately with 'y', using non-missing observations for that predictor. 'print' and 'plot' methods allow one to easily print or plot the results of 'spearman2(formula)'. The adjusted 'rho^2' is also computed, using the same formula used for the ordinary adjusted 'R^2'. The 'F' test uses the unadjusted R2. For 'plot', a dot chart is drawn which by default shows, in sorted order, the adjusted 'rho^2'.