L: traceS): 23.six, Efficient degrees of freedom (model: traceS): 7.39, Sigma (model: traceS
L: traceS): 23.6, Productive degrees of freedom (model: traceS): 7.39, Sigma (model: traceS): 0.99, Sigma (ML): 0.86, AICc (GWR p. six, eq two.33; p. 96, Eq 4.2): 307.836, AIC (GWR p. 96, Eq 4.22): 264.07, Residual sum of squares: 69.9, Quasiglobal R2: 0.77; OLS residuals 277.20, GWR residuals 69.9.) The FTR coefficients from the GWR do not appear to cluster by area. That is, the information does not seem to divide into `European’ and `nonEuropean’ categories. In order to test the effect of geography, the predicted FTR values from the GWR were integrated into a PGLS model (predicting savings from FTR with observations weighted by a phylogenetic tree, see below). This proficiently removes the variance as a result of geographic spread. The results from the PGLS show that the correlation among savings and FTR is weakened, but still significant (r .84, t 2.094, p 0.039).PLOS A single DOI:0.37journal.pone.03245 July 7,35 Future Tense and Savings: Controlling for Cultural EvolutionFig 7. Geographic distribution of FTR and savings. The map on the left shows the geographic distribution `strong’ and `weak’ FTR languages. The map on the right shows the distribution with the savings residuals variable. Points represent languages and colour represents the value with the MedChemExpress PD 151746 propensity to save residuals. The values variety from a low propensity (yellow) to a high propensity(red). doi:0.37journal.pone.03245.gPhylogenetic Generalised Least SquaresIn order to test how savings behaviour is impacted by FTR, a test is needed that makes it possible for a continuous dependent variable (the savings residuals) and a discrete independent variable (FTR) that also requires the historical relationships in between languages into account. Phylogenetic Generalised Least Squares (PGLS) is really a method for calculating relationships among observations which can be not independent. The anticipated similarity in between every single pair of observations is estimated to make an anticipated covariance matrix. The covariance matrix is used to weight observations in a regular linear generalised least squares regression. When analysing observations which might be connected within a phylogeny, the similarity reflects the phylogenetic distance between two observations on the tree. We assume that all language households are connected to each other deep in time by a single node. This implies that the similarity between any two languages in the unique language families is going to be equally massive, even though the similarity amongst languages within a language family will probably be extra finegrained. To be clear, although we analyse languages from several households, we do not make any assumptions concerning the topology of your tree between language families (aside from that they are connected deed in time somehow). There are many solutions of calculating the covariance matrix to get a phylogeny. As an example, the traits is often assumed to transform based on Brownian motion (in which case PGLS is equivalent to an independent contrasts test), or the similarity among traits decreases exponentially with distance within the phylogeny (OrnstenUhlenbeck model). Some models, such as Grafen’s model rescale the branch lengths, which we take into account inappropriate here. The test of phylogenetic signal above demonstrated that both the FTR and savings variable had been unlikely to be altering based on Brownian motion. As a result, inside the tests beneath we use Pagel’s covariance matrix [07], which requires a Brownian motion covariance matrix and scales PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/24134149 the offdiagonal values by the estimated phylogenetic signal stre.