search for: unsignificant

Ran a bunch of variables in R and the final result of StepAIC is as below: Why are the first 5 variables kept in the stepwise result?? Are the last 4 variables finally chosen after Stepwise? Thanks Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.315e-01 2.687e-01 0.490 0.63611 Core_CPI__ 1.290e-02 7.496e-03 1.721 0.11927 GDP_change -3.482e-03 2.075e-03 -1.678 0.12767

hello, I am looking for a step() function for GAM's. In the book Statistical Computing by Crawley and a removal of predictors has been done "by hand" model <- gam(y ~s(x1) +s(x2) + s(x3)) summary(model) model2 <- gam(y ~s(x2) + s(x3)) # removal of the unsignificant variable #then comparing these two models if an significant increase occurs. anova(model, model2, test="F") isn't there a way to drop and add variables automatically until the best model is received? like in step(lm(...))? Or as in grasp.step.gam() - but that doesn't work when...

...e model3<-lme (fixed=Peak~Limb, data=Loco, random=list(Dog=~Limb,Day=~1,Record=~1)) anova (model2,model3) showed a significant difference <0.0001 model2 seems to be the best model. Does it means the difference of variance between the two limb is significant for between-day variation and is unsignificant for within-day variation?? Finally VarCorr (model2) gives : Variance StdDev Corr Dog = pdLogChol(Limb) (Intercept) 567.553021 23.823371 (Intr) LimbRight 7.249064 2.692409 -0.166 Day = pdLogChol(Limb) (Intercept) 53.8...

...exp(coef) values in this case, as they are very large values? Also 3-case interaction is involved, which confuses the interpretation more. All the examples concerning Coxph-model I have found so far online have been really simple regarding the intercation terms (which have always turned out to be unsignificant) and also coefficient-values (=hazard rates) and exponentials of these (=hazard ratios) have been pretty small and "easy to handle" numbers, e.g. coefficient = 1.73 -> exp(coef) = 5.64. BUT mine are way bigger numbers as you can see from the summary output (above). And because they are...

...') plus produces following warning message, "Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights,: Ran out of iterations and did not converge." Tampering with the data (i.e. reducing the number of cases with the exposure to 7 of 11) renders the odds ratio unsignificant (p-value = 0.06681), yet makes clogit() assign a significant p-value to the exposure in question (0.043). Furthermore, the warning message is gone after the tampering. Interestingly reducing (instead of increasing) cases with the risk factor yields a significant result with clogit(). I believe no...

...nd pgAdmin or the like, > let R/RODBC create it thru sqlSave, using first 'append=FALSE' and > 'safer=FALSE' options > > 3. In pgAdmin, adjust cautiously the data type, even if you have to > accept missdimentionned integer or float (the price of disk is > unsignificant today) > > > I realize its look like vaudoo and black magic, but shoulf I have done > that from the start, I would have save a long day of work debugging > phantoms, ... > > If some body has better practices, there would be warmly wellcomed. > > Sorry for disturbance...

....862 ITU-T recommendation. The comparison is performed with ITU pesq utility. Source speech samples has the length about 8-15 seconds. The samples contain male and female voices, all sentence are pronounced in english. All speech samples are given from internet podcast interview, some of these has unsignificant noise artefacts. Every speech sample has at least 0.5 seconds of the silence on the bounds. Most of speech samples almost have no pauses inside. PESQ estimation is performed with 8kHz samples, resulting value is MOS LQO as defined in P.862.1. Experiment results and further work -----------------...