search for: dropterm

Displaying 14 results from an estimated 14 matches for "dropterm".

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2009 Jan 29
1
Inconsistency in F values from dropterm and anova
Hi, I'm working on fitting a glm model to my data using Gamma error structure and reciprocal link. I've been using dropterm (MASS) in the model simplification process, but the F values from analysis of deviance tables reported by dropterm and anova functions are different - sometimes significantly so. However, the reported residual deviances, degrees of freedom, etc. are not different. I don't know how to calculat...
2002 Apr 28
2
dropterm() in MASS
To compare two different models, I've compared the result of using dropterm() on both. Single term deletions Model: growth ~ days + I(days^0.5) Df Sum of Sq RSS AIC <none> 2.8750 -0.2290 days 1 4.8594 7.7344 4.6984 I(days^0.5) 1 0.0234 2.8984 -2.1722 AND Single term deletions Model: growth ~ days + I(days...
2017 Jun 08
1
stepAIC() that can use new extractAIC() function implementing AICc
I would like test AICc as a criteria for model selection for a glm using stepAIC() from MASS package. Based on various information available in WEB, stepAIC() use extractAIC() to get the criteria used for model selection. I have created a new extractAIC() function (and extractAIC.glm() and extractAIC.lm() ones) that use a new parameter criteria that can be AIC, BIC or AICc. It works as
2002 Apr 01
0
something confusing about stepAIC
Folks, I'm using stepAIC(MASS) to do some automated, exploratory, model selection for binomial and Poisson glm models in R 1.3. Because I wanted to experiment with the small-sample correction AICc, I dug around in the code for the functions glm.fit stepAIC dropterm.glm addterm.glm extractAIC.glm and came across something I just don't understand. stepAIC() passes dropterm.glm() a model object. dropterm.glm() then fits a number of submodels, computing for each some measure DeltaFit of the relative change in goodness of fit. It then returns to stepAIC() w...
2009 Jan 28
1
StepAIC with coxph
Hi, i'm trying to apply StepAIC with coxph...but i have the same error: stepAIC(fitBMT) Start: AIC=327.77 Surv(TEMPO,morto==1) ˜ VOD + SESSO + ETA + ........ Error in dropterm.default(fit,scope$drop, scale=scale,trace=max(0, : number of rows in use has changed: remove missing values? anybody know this error?? Thanks. Michele [[alternative HTML version deleted]]
2010 Mar 16
0
New package: ordinal
...nction-parameter bridges the log-log, probit and c-loglog links (log-gamma), and cloglog and logistic links (Aranda-Ordaz) - a suite of optimizers including an efficient Newton scheme. - works for binomial observations (a special case of ordinal data). - a suite of methods including anova, addterm, dropterm, profile, confint, plot.profile, predict, in addition to the standard print and summary methods. - an important special case is the proportional odds model (with random effects). - a range of examples illustrates how to use the functions. Future additions will include: - more general random effect...
2010 Apr 07
1
Step by significance
Does anybody know how to perform a step function by significance of p instead of AIC? I want to perform a forward-backward stepwise logistic regression and want to compare the results obtained by steps of significance and by steps of AIC. Thank you -- View this message in context: http://n4.nabble.com/Step-by-significance-tp1754237p1754237.html Sent from the R help mailing list
2010 Mar 16
0
New package: ordinal
...nction-parameter bridges the log-log, probit and c-loglog links (log-gamma), and cloglog and logistic links (Aranda-Ordaz) - a suite of optimizers including an efficient Newton scheme. - works for binomial observations (a special case of ordinal data). - a suite of methods including anova, addterm, dropterm, profile, confint, plot.profile, predict, in addition to the standard print and summary methods. - an important special case is the proportional odds model (with random effects). - a range of examples illustrates how to use the functions. Future additions will include: - more general random effect...
2011 Jun 16
0
Hauck-Donner
...PM, Rob James wrote: > Ben, > > Thanks for this. Very helpful and clearly others have tripped over the > same problem > I would have supposed that the solution was to ask lrm (or glm) to use > LR rather than Wald, but I don't see syntax to achieve this. Typically drop1 or dropterm (MASS package) will drop appropriate terms from the model and test the difference via LRT (or F test). stepAIC in the MASS package will do stepwise selection via AIC. This opens the larger can of worms of why you're doing stepwise model selection in the first place ... I was surprised to se...
2009 Jan 23
2
R stepping through multiplie interactions
I have a lm in R in the form model <- lm( Z ~ A*B*C*D,data=mydata) I want to run the model and include all interactions expect the 4 way (A:B:C:D) is there an easy way of doing this? I then want to step down the model eliminating the non-significant terms I understand step() does this but how would I do it by hand? -- View this message in context:
2008 Aug 01
5
drop1() seems to give unexpected results compare to anova()
...ls 195 6.002e-28 3.078e-30 All was well so far, as x4 was identified as not significant and its coeff was almost 0 (because I made it so in the first place). Now I expected it to be dropped in stepwise: step(model, direction = 'both', test = 'F') drop1(model, test = 'F') dropterm(model, test = 'F') Df Sum of Sq RSS AIC F value Pr(F) <none> 6.002e-28 -13585.7 x1 1 2555.1 2555.1 517.5 8.3006e+32 < 2.2e-16 *** x2 1 3707.0 3707.0 591.9 1.2043e+33 < 2.2e-16 *** x3 1 7851.9 7851.9 742.0 2.5508e+33 < 2.2e-16 *** x4 1 2.118e-27 2.718e-27 -13285.6 6.8806e+02...
2006 Aug 21
1
New version of glmmML
...onses can now be represented as a two-column matrix with No. of successes and No. of failures, respectively, as in glm. * New functions: 'ghq' for calculating the constants used in the Gauss-Hermite quadrature. 'extractAIC.glmmML', which makes it possible to use functions like 'dropterm' (MASS) on glmmML fits. * There are three choices of distribution for the random effects: 'gaussian' (default), 'logistic', and 'cauchy'. * The 'conditional' bootstrap is removed, so the only choice now is the parametric bootstrap. * The 'posterior.means...
2006 Aug 21
1
New version of glmmML
...onses can now be represented as a two-column matrix with No. of successes and No. of failures, respectively, as in glm. * New functions: 'ghq' for calculating the constants used in the Gauss-Hermite quadrature. 'extractAIC.glmmML', which makes it possible to use functions like 'dropterm' (MASS) on glmmML fits. * There are three choices of distribution for the random effects: 'gaussian' (default), 'logistic', and 'cauchy'. * The 'conditional' bootstrap is removed, so the only choice now is the parametric bootstrap. * The 'posterior.means...
2013 Oct 27
2
Heteroscedasticity and mgcv.
I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Models as it is for standard linear models? I am asking particularly about the GAMs as implemented in the mgcv package. Based upon my online search it seems that some