In the present analysis, different dimensionalities consistently placed the elbow at the fifth component. Rotation is not available in the CatPCA software and for the nonlinear PCA solution on the IBIS data, rotation was not called for, as most variables already loaded highly on only one component. The object scores obtained from the five dimensions were saved for subsequent analysis. Correlations between the scores and EPDS >12 Inhibitors,research,lifescience,medical were

assessed by logistic-regression models with the exception of dimension 1 (maternal responsiveness), which was assessed using directed acyclic graphs to be an effect of the depressive symptoms measured by the EPDS. Odds ratios (ORs) were determined with 95% confidence intervals (95% CI). All analysis was undertaken using SPSS 19.0 © IBM 2010 (Armonk, New York). Ethics approval The study obtained ethics approval from the Human Research Ethics Committee, South Western Sydney Inhibitors,research,lifescience,medical Area Health Service and from the University of NSW Human Research Ethics Committee. Results Exploratory data analysis including nonlinear PCA solutions can best be interpreted through graphical visualization (Tukey 1980; Inhibitors,research,lifescience,medical Linting et al. 2007). The results section will focus on interpreting graphical outputs from CatPCA. The LY2157299 component loadings for a five-dimension analysis are shown in Table 1. Component loadings are arranged in decreasing order within

dimensions, loadings greater than 0.40 are in bold and loadings less than 0.30 are suppressed Inhibitors,research,lifescience,medical to aid interpretation. Table 1 Component loadings for

five dimensions Variance The five-dimensional nonlinear PCA yields an eigenvalue of 4.16 for the first component. This is approximately 14.8% of the variance of the transformed variables (4.16/28 with 28 being the number of variables). The eigenvalue of Inhibitors,research,lifescience,medical the second component is 3.67 (13%), third is 3.21 (11.5%), fourth is 1.91 (6.8%), and the fifth is 1.38 (4.9%). The total variance in the transformed variable accounted for by the five dimensions is 51%. Biplots Astemizole of component loadings Component loadings are presented in Figures 1–3 displayed as vectors. The component loadings range between −1 and 1, and indicate the Pearson correlations between the quantified variables and the principal components. The coordinates of the end point of each vector are the loadings of each variable on the two components plotted. Variable vectors that are close together in the plot are closely and positively related. Variables with vectors that make approximately a 180° angle with each other are closely and negatively related. Variables vectors with a 90° angle are not related (Linting et al. 2007). Figure 1 Biplot of dimensions 1 and 2. Figure 3 Biplot of dimensions 3 and 4. The variables in Figure 1 form two clearly distinct groups.