L: traceS): 23.6, Efficient degrees of freedom (model: traceS): 7.39, Sigma (model: traceS
L: traceS): 23.six, Helpful degrees of freedom (model: traceS): 7.39, Sigma (model: traceS): 0.99, Sigma (ML): 0.86, AICc (GWR p. 6, eq 2.33; p. 96, Eq 4.two): 307.836, AIC (GWR p. 96, Eq four.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 seem to cluster by area. That’s, the data does not seem to divide into `European’ and `nonEuropean’ categories. So as to test the impact of geography, the predicted FTR values in the GWR have been integrated into a PGLS model (predicting savings from FTR with MedChemExpress NSC53909 observations weighted by a phylogenetic tree, see under). This correctly removes the variance because of geographic spread. The outcomes in the PGLS show that the correlation involving savings and FTR is weakened, but nevertheless important (r .84, t two.094, p 0.039).PLOS One 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 around the left shows the geographic distribution `strong’ and `weak’ FTR languages. The map on the correct shows the distribution of your savings residuals variable. Points represent languages and colour represents the worth of the propensity to save residuals. The values variety from a low propensity (yellow) to a higher propensity(red). doi:0.37journal.pone.03245.gPhylogenetic Generalised Least SquaresIn order to test how savings behaviour is affected by FTR, a test is needed that makes it possible for a continuous dependent variable (the savings residuals) in addition to a discrete independent variable (FTR) that also requires the historical relationships amongst languages into account. Phylogenetic Generalised Least Squares (PGLS) is a technique for calculating relationships among observations that are not independent. The expected similarity between each and every pair of observations is estimated to make an expected covariance matrix. The covariance matrix is made use of to weight observations within a common linear generalised least squares regression. When analysing observations that are related inside a phylogeny, the similarity reflects the phylogenetic distance between two observations on the tree. We assume that all language families are associated to one another deep in time by a single node. This implies that the similarity involving any two languages from the distinct language households will be equally massive, whilst the similarity among languages within a language family members is going to be additional finegrained. To become clear, even though we analyse languages from multiple 
families, we don’t make any assumptions about the topology of your tree amongst language households (apart from that they are connected deed in time somehow). There are numerous methods of calculating the covariance matrix for a phylogeny. For example, the traits can be assumed to modify according to Brownian motion (in which case PGLS is equivalent to an independent contrasts test), or the similarity amongst traits decreases exponentially with distance in the phylogeny (OrnstenUhlenbeck model). Some models, like Grafen’s model rescale the branch lengths, which we consider inappropriate here. The test of phylogenetic signal above demonstrated that both the FTR and savings variable were unlikely to be altering according to Brownian motion. Consequently, inside the tests under we use Pagel’s covariance matrix [07], which takes 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.
