I have a small dataframe xxF, a summary of which looks like this: > summary(xxF) T Dev Min. :10.44 Min. :0.008929 1st Qu.:10.44 1st Qu.:0.012048 Median :18.61 Median :0.031250 Mean :17.87 Mean :0.028286 3rd Qu.:22.24 3rd Qu.:0.041667 Max. :30.37 Max. :0.050000
I managed to make a non-linear fit after a lot of fiddling with initial values but it looks overly complicated and biologically unconvincing in part. The general form of a skewed t-distribution looks more appropriate so I tried selm from the sn package thus: > selmFt <- with(xxF, selm(Dev ~ T, family = "ST", method="MPLE")) > coef(selmFt, param.type="DP") (Intercept.DP) T omega alpha nu -0.015895099 0.002689226 0.002306132 -5.660870446 1.473210455 I wish to get predictions for values of T between 10 and 32 but I can't figure out how to use those coefficients. With an linear model or glm, even without a prediction method, it's fairly simple to get predictions from a range of values of the independent variable/s. For a skewed-t it's evidently less straightforward. Does it have to be done using CP type parameters? Ideas gratefully accepted In case it makes any difference.... > sessionInfo() R version 3.2.1 (2015-06-18) Platform: i686-pc-linux-gnu (32-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats4 grDevices utils stats graphics methods base other attached packages: [1] dplyr_0.3.0.2 nlmrt_2013-9.25 RColorBrewer_1.1-2 plyr_1.8.3 [5] stringr_1.0.0 reshape2_1.4.1 sn_1.2-2 lattice_0.20-31 loaded via a namespace (and not attached): [1] Rcpp_0.11.3 assertthat_0.1 grid_3.2.1 DBI_0.3.1 [5] magrittr_1.0.1 stringi_0.4-1 lazyeval_0.1.10 tools_3.2.1 [9] numDeriv_2014.2-1 parallel_3.2.1 mnormt_1.5-3 -- ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~. ___ Patrick Connolly {~._.~} Great minds discuss ideas _( Y )_ Average minds discuss events (:_~*~_:) Small minds discuss people (_)-(_) ..... Eleanor Roosevelt ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.