similar to: Strange parametrization in polr

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

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

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

Hi everyone, If I compute a "Ordered Logistic or Probit Regression" with the polr function from MASS package. the summary give me : coefficients, Standard error and Tvalue.. but not directly the p.value. I can compute "manualy" the Pvalue, but Is there a way to directly obtain the pa.value, and I wonder why the p.valeu is not directly calculated, is there a reason? exemple

Dear list members, The polr() function in the MASS package takes an optional weights argument for case weights. Is there any way to retrieve the case weights from the fitted "polr" object? Examining both the object and the code, I don't see how this can be done, but perhaps I've missed something. Any help would be appreciated. John ------------------------------ John Fox,

Hi all, I have a problem to estimate Std. Error and t-value by ?polr? in library Mass. They result from the summary of a polr object. I can obtain them working in the R environment with the following statements: temp <- polr(formula = formula1, data = data1) coeff <- summary(temp), but when the above statements are enclosed in a function, summary reports the following error:

I am new to R, ordered logistic regression, and polr. The "Examples" section at the bottom of the help page for polr<http://stat.ethz.ch/R-manual/R-patched/library/MASS/html/polr.html>(that fits a logistic or probit regression model to an ordered factor response) shows options(contrasts = c("contr.treatment", "contr.poly")) house.plr <- polr(Sat ~ 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 stats::hatvalues.lm, the S3 method for class

I would like to do a SS type III analysis on a proportional odds logistic regression model. I use drop1(), but dropterm() shows the same behaviour. It works as expected for regular 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 =

Hi, I found that using different contrasts (e.g. contr.helmert vs. contr.treatment) will generate different fitted probabilities from multinomial logistic regression using multinom(); while the fitted probabilities from binary logistic regression seem to be the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" 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

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 :

Hi! I'm trying to do a lm() test on three objects. My problem is that R protests and says that the variable lengths differ for one of the objects (Sweden.GDP.gap). But I have double checked that the number of observations are the same. All three objects should contain 9 observations but R only accepts 9 observations in two of the objects. The third must have 10! Very confusing because there

Hi, I have the following code from Splus 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

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

Full_Name: Heather Turner Version: 2.2.0 OS: Windows XP Submission from: (NULL) (137.205.240.44) Standardized residuals as calculated by rstandard.lm, rstandard.glm and plot.lm are Inf/NaN rather than zero when the un-standardized residuals are zero. This causes plot.lm to break when calculating 'ylim' for any of the plots of standardized residuals. Example:

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, I would like apply different link functions from Aranda-Ordaz (1981) family to large binary dataset (n = 2000). The existing links in glm for binomial data (logit, probit, cloglog) are not adequate for my data, and I need to test some other transformations. Is it possible to do this in R? And how? Thank you for your help, /Sharon

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson",

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