search for: infl

I'm trying to write some functions extending influence measures to multivariate linear models and also allow subsets of size m>=1 to be considered for deletion diagnostics. I'd like these to work roughly parallel to those functions for the univariate lm where only single case deletion (m=1) diagnostics are considered. Corresponding to...

Hi all, Two questions: 1. Is there a way to evaluate models from lmer() with a poisson distribution? I get the following error message: library(lme4) lmer(tot.fruit~infl.treat+def.treat+(1|initial.size),family=poisson)->model plot(fitted(model),resid(model)) Error: 'resid' is not implemented yet Are there any other options? 2. Why doesn't the function estimable() in gmodels work with lmer() using a poisson distribution? I know that my coefficient...

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat : Factor w/ 4 levels "1","2","3","4": 2 3 1 3 3 3 2 2 3 1 ... $ initial.size : num 854 158 256 332 396 ... $ tot.fruit : int 45 8 19 8 10 7 4 7 2 6 ... ## I fit a glm...

...that I'd like to migrate to R. So far, the only problem is the arima.filt function. This function allows me to filter an existing time-series through a previously estimated arima model, and obtain the residuals for further use. Here's the Splus code: # x is the estimation time series, new.infl is a timeseries that contains new information # a.mle is estimated result (list) from arima.mle, (1,0,1) x (1,0,1)12 seasonal model mdl _ list(list(order=c(1,0,1)), list(order=c(1,0,1), period=12)) a.mle _ arima.mle(x, model = mdl) # then, we get regular residuals: new.pred _ arima.filt(ne...

...the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example script: library(MASS) library(nnet) #### multinomial logistic options(contrasts=c('contr.treatment','contr.poly')) xx<-multinom(Type~Infl+Cont,data=housing[-c(1,10,11,22,25,30),]) yy<-predict(xx,type='probs') yy[1:10,] options(contrasts=c('contr.helmert','contr.poly')) xx<-multinom(Type~Infl+Cont,data=housing[-c(1,10,11,22,25,30),]) zz<-predict(xx,type='probs') zz[1:10,] ##### binary...

...main effects models, however when the model includes an interaction effect it seems to have problems with matching the parameters to the predictor terms. An example: library("MASS"); options(contrasts = c("contr.treatment", "contr.poly")); house.plr1 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing); drop1(house.plr1,attributes(house.plr1$terms)$term.labels,test="Chisq"); house.plr2 <- polr(Sat ~ Infl * Type + Cont, weights = Freq, data = housing); drop1(house.plr2,attributes(house.plr2$terms)$term.labels,test="Chisq"); N...

...and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I prefer to use R's native capabilities whenever possible. I think something along the following lines should do it: rstandard.glm <- function(model, infl=influence(model, do.coef=FALSE), type=c("deviance", "pearson"), ...) { res <- switch(type, pearson = infl$pear.res, infl$dev.res) res <- res/sqrt(1-infl$hat) res[is.infinite(res)] <- NaN res }

.... Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version library( MASS ) data( housing ) hnames <- lapply( housing[,-5], levels ) house.plr <- polr( Sat ~ Infl + Type + Cont, data=housing, weights=Freq ) summary( house.plr ) newdat <- expand.grid( hnames[-1] )[1:5,] cbind( newdat, predict( house.plr, newdat, type="probs" ) ) # Baseline probs: diff( c(0,tigol( c(-0.4961,0.6907) ), 1) ) # First level of Infl: diff( c(0,tigol( c(-0.4961,0.6907)...

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables (factors). And I have some questions, hope someone could help me out. The following commands are from the MASS book as well. > house.plr<-polr(Sat~Infl+...

...accepts 9 observations in two of the objects. The third must have 10! Very confusing because there is no 10th observation! # Adjusted Real rate - P > Sweden.p.adjust <- c(4.70243, 1.3776655, 1.117755, 1.6695175, 1.59282, > 1.1017625, -0.04295, 2.2552875, 0.0552875) > > # Adjusted Inflation deviation > Sweden.infl.dev.adjust <- c(0.110382497, -0.261612509, 0.040847515, > -0.195062497, -0.234362485, -0.023408728, 0.206421261, -0.079401261, > 0.071828752) > > # Adjusted GDP-gap > Sweden.GDP.gap.adjust <- c(0.673792123, 1.196706756, 1.196131539, > 0.6469...

...near-zero rstandard(Uniform)[as.logical(diag(8))] #mostly Inf/NaN plot(Uniform) #breaks on qqnorm plot (or any 'which' > 1) This could be fixed by replacing standardized residuals with zero where the hat value is one, e.g. rstandard.glm <- function (model, infl = lm.influence(model, do.coef = FALSE), ...) { res <- infl$wt.res hat <- infl$hat ifelse(hat == 1, 0, res/sqrt(summary(model)$dispersion * (1 - infl$hat))) } etc.

Hi, I want to calculate the t- and p-values for a linear model using the Newey West estimator. I tried this Code and it usually worked just fine: > oberlm <- lm(DYH ~ BIP + Infl + EOil, data=HU_H) > coeftest(oberlm, NeweyWest(oberlm, lag=2)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1509950 0.0743832 2.0300 0.179486 BIP -5.5131683 1.2536813 -4.3976 0.048016 * Infl -0.0623530 0.0036215 -...

Hi all, I'm trying to discover the options available to me for logistic and linear regression. I'm doing some tests on a dataset and want to see how different flavours of the algorithms cope. So far for logistic regression I've tried glm(MASS) and lrm (Design) and found there is a big difference. Is there a list anywhere detailing the options available which details the specific

...the dispersion is inside the sqrt". (That's the way I read 12.5 on p. 397 too). I can't see that summary(model)$dispersion has already been subject to sqrt() in sum(object$weights * object$residuals^2)/df.r in summary.glm(). Should: res / (summary(model)$dispersion * sqrt(1 - infl$hat)) be: res / (sqrt(summary(model)$dispersion * (1 - infl$hat))) on line 117 in base/R/lm.influence.R? Best wishes, Roger -- Roger Bivand Econonic Geography Section, Department of Economics, Norwegian School of Economics and Business Administration, Breiviksveien 40, N-5045 Bergen, N...

...he Wald test and the likelihood ratio test to give similar p-values. However the p-values obtained from anova(fit1,fit3) (refer to example below) are very different (0.0002622986 vs. 1). Why is this so? > library(MASS) > fit1 <- polr(housing$Sat~1) > fit2<- polr(housing$Sat~housing$Infl) > fit3<- polr(housing$Sat~housing$Cont) > summary(fit1) Re-fitting to get Hessian Call: polr(formula = housing$Sat ~ 1) No coefficients Intercepts: Value Std. Error t value Low|Medium -0.6931 0.2500 -2.7726 Medium|High 0.6931 0.2500 2.7726 Residual Deviance:...

Hi, I've looked around but can't find a clear answer to the difference for these two? Any help? Thanks! -- View this message in context: http://old.nabble.com/dfbetas-vs-dfbeta-tp26331704p26331704.html Sent from the R help mailing list archive at Nabble.com.

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights = Freq, data = housing) > nobs(house.plr) [1] 1681 > nobs(house.logit) [1] 72 -- Wincent Ronggui HUANG Sociology Department of Fudan University PhD of Cit...

Dear All, I have been struggling to understand why for the housing data in MASS library R and stata give coef. estimates that are really different. I also tried to come up with many many examples myself (see below, of course I did not have the set.seed command included) and all of my `random' examples seem to give verry similar output. For the housing data, I have changed the data into numeric

...ons how to make this work? > > library(extracat) Loading required package: reshape Loading required package: plyr Loading required package: grid Loading required package: MASS > ?irmb starting httpd help server ... done > data(housing) > # example 1 > irmb(~Type+Infl+Cont+Sat,dset=housing,expected = TRUE, + eqwidth=FALSE,base=0.2,mult=2,lab.tv=TRUE,abbr=TRUE) iWidgets requires 'JGR' in order to run correctly. Please visit http://www.rosuda.org/software/ to download it.> > utils:::menuInstallPkgs() trying URL 'http://probability.ca...

Thanks to some comments from Brian D. Ripley, I found my error: I should not have given a data argument to lm() after creating a formula-object. This obviously confused things... Thanks again, I've really learnt again a bit more on R-programming... Cheers, Winfried --------------------------------------------------------------------- E-Mail: Winfried Theis <theis at