search for: reziprok

...", model="onestep") > > I tried "Model" as "Modelp <- pdata.frame(..." and as "Model <- read.table(..." but in both cases there?s an error-massage: Error in solve.default(Reduce("+", A2)) : System ist f?r den Rechner singul?r: reziproke Konditionszahl = 4.08048e-22 Error in solve.default(Reduce("+", A2)) : System is singulary for the computer: reciprocal number of conditions = 4.08048e-22 I really can?t help myself since the standard plm-estimations "within" or "first difference" are working we...

...cken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"), model="random") summary(random) phtest(fixed, random) I get an error from phtest(fixed, random) Fehler in solve.default(dvcov) : System ist f?r den Rechner singul?r: reziproke Konditionszahl = ... Error in solve.default(dvcov) : system is computationally singular: reciprocal condition number =... Can someone Help me with this problem or give me a hint where to look? Thanks gloria -- View this message in context: http://r.789695.n4.nabble.com/Hausman-test-error...

...stic: 869.8 on 5 and 42738 DF, p-value: < 2.2e-16 > # ========================================= Now I use summaryHCCM(fit.lm.5): > # ========================================= > summaryHCCM(fit.lm.5) Fehler in solve.default(L %*% V %*% t(L)) : System ist f?r den Rechner singul?r: reziproke Konditionszahl = 6.98689e-19 > # ========================================= ("Error in solve.default(L %*% V %*% t(L)) : System is singulary for the computer: reciprocal number of conditions = 6.98689e-19") This does not happen if I omit I(x.log^5). I do not know what it means...

...es sem and psych but whatever I do to deal with models which do not converge I always get stuck and get error messages such as these: "Error in summary.sem(M1) : coefficient covariances cannot be computed" "Error in solve.default(res$hessian) : System ist f?r den Rechner singul?r: reziproke Konditionszahl = 8.61916e-17" I am aware that these models could not be computed but it is ok, itis part of what I am trying to show with the simulation, that under specifications x the models converge more easily than under specifications y. When models do not converge I just let R write a...