# Predict zeta values for new environmental and distance data Predict the zeta values for new environmental and distance data from the models returned by `Zeta.msgdm`. ```r Predict.msgdm(model.msgdm, reg.type, newdata, type = "response") ``` ## Arguments - `model.msgdm`: A model returned by `Zeta.msgdm`. The class of the model depends on the type of regression used in `Zeta.msgdm`. - `reg.type`: Type of regression used in `Zeta.msgdm`. Options are "`glm`" for generalised linear models, "`ngls`" for negative linear models, "`gam`" for generalised additive models, "`scam`" for shape constrained additive models (with monotonic decreasing by default), and "`ispline`" for I-spline models (forcing monotonic decreasing), as recommended in generalised dissimilarity modelling by Ferrier **et al**. (2007). - `newdata`: A data frame with the new environmental and distance data. The names of the columns must be the same as the names used in the data frame used in `Zeta.msgdm`. For I-splines, the data frame must be generated beforehand from the original data by the function `Ispline`. - `type`: The type of prediction required, as for `predict.glm`. The default is on the scale of the response variable; the alternative "link" is on the scale of the linear predictors. ## Returns `Predict.msgdm` returns a vector of predicted zeta values. ## Examples ```r utils::data(bird.spec.fine) xy.bird <- bird.spec.fine[1:500,1:2] data.spec.bird <- bird.spec.fine[1:500,3:192] utils::data(bird.env.fine) data.env.bird1 <- bird.env.fine[1:500,3:9] ##training data data.env.bird2 <- bird.env.fine[501:604,3:9] ##data to predict on ##glm zeta.glm <- Zeta.msgdm(data.spec.bird, data.env.bird1, sam = 100, order = 3, reg.type = "glm") newdata <- data.frame(matrix(NA,100,ncol(data.env.bird1))) names(newdata) <- names(data.env.bird1) for(z in 1:100){ samp <- sample(1:104, 3, replace = FALSE) newdata[z,] <- apply(apply(data.env.bird2[samp,], 2, stats::dist), 2, mean) } ##rescale the data like during MS-GDM newdata <- newdata/matrix(rep(zeta.glm$rescale.factor,100), 100,length(zeta.glm$rescale.factor),byrow=TRUE) new.zeta.glm <- Predict.msgdm(model.msgdm = zeta.glm$model, reg.type = "glm", newdata = newdata) ##I-splines zeta.ispline <- Zeta.msgdm(data.spec.bird, data.env.bird1, sam = 100, order = 3, reg.type = "ispline", normalize = "Jaccard") data.env.bird.splines <- Ispline(data.env.bird2) newdata <- data.frame(matrix(NA,100,ncol(data.env.bird.splines$splines))) names(newdata) <- names(data.env.bird.splines$splines) for(z in 1:100){ samp <- sample(1:104, 3, replace = FALSE) newdata[z,] <- apply(apply(data.env.bird.splines$splines[samp,], 2, stats::dist), 2, mean) } ##rescale the data like during MS-GDM newdata <- newdata/matrix(rep(zeta.ispline$rescale.factor,100), 100,length(zeta.ispline$rescale.factor),byrow=TRUE) new.zeta.ispline <- Predict.msgdm(model.msgdm = zeta.ispline$model, reg.type = "ispline", newdata = newdata) ``` ## References Ramsay, J. O. (1988). Monotone regression splines in action. **Statistical Science**, 425-441. Ferrier, S., Manion, G., Elith, J., & Richardson, K. (2007). Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. **Diversity and Distributions**, 13(3), 252-264. ## See Also `Zeta.msgdm`

Predict.msgdm function