similar to: testing effects of quantitative predictors on a categorical response variable

Hello, I am trying to get the P values from the output of a summary for lm. lm <- lm(y ~ age + sex) s <- summary(lm) I thought that I might be able to get them using a combination of scan, grep and sub. But I got stuck on the first step - being able to process "s" as a text string. I could perhaps write it to file than scan it back but there is probably an easier way to do

Hello, I've had no luck finding an R package that has the ability to estimate a Tobit model allowing for heteroskedasticity (multiplicative, for example). Am I missing something in survReg? Is there another package that I'm unaware of? Is there an add-on package that will test for heteroskedasticity? Thanks for your help. Cheers, Alan Spearot -- Alan Spearot Department of Economics

Hello, I would like to inlude the Q-Q plot by "qqmath" into a panel with other plots, say, using par(mfrow=c(1,2)). How can this be done given that "qqmath" refreshes the plotting window and there seems to be no series coming out of it? Thanks Serguei [[alternative HTML version deleted]]

Hi, I'm trying to aggregate date values using the aggregate function. For example: aggregate(data,by=list(weekdays(LM),months(LM)),FUN=length) I would also like to aggregate by year but there seems to be no years() function. Should there be one? Is there any alternative choice? Also, a hours() function would be great. Any tip on this? Thanks in advance! S?rgio Nunes

Hi there, I'm writing a function that calculates the probability of different outcomes of dice rolls (e.g., the sum of the highest three rolls of five six-sided dice). I'm using the "combinations" function from the "gtools" package, which is great: it gives me a matrix with all of the possible combinations (with repetitions allowed). Now I want to count the number

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

Hello, I'm a newcomer to R so please forgive me if this is a silly question. It's that I have a linear regression: fm <- lm (x ~ y) and I want to test whether the slope of the regression is significantly less than 1. How can I do this in R? I'm also interested in comparing the slopes of two regressions: fm1 <- lm (x ~ y) fm2 <- lm (a ~ b) and asking if the slope of fm1 is

Hello, I have a question about using extreme value distribution in R. I have two variables, X and Y, and have pairs of points (X1,Y1),(X2,Y2), (X3,Y3) etc. When I plot X against Y, it looks like the maximum value of Y (for a particular X) is correlated with X. Indeed, when I bin the data by X-value into equally sized bins, and test whether the maximum value of Y for a bin is correlated with

Dear R help list, I am trying to do a logistic regression where I have a categorical response variable Y and two numerical predictors X1 and X2. There are quite a lot of missing values for predictor X2. eg., Y X1 X2 red 0.6 0.2 * red 0.5 0.2 * red 0.5 NA red 0.5 NA green 0.2 0.1 * green 0.1 NA green 0.1 NA green 0.05 0.05 * I am wondering can I combine X1 and

Hello, I am wondering whether there is a way to test whether two non-independent correlation coefficients are significantly different, in R? I have an experimentally measure variable Y, and two different variables X1, and X2, which are predictions of Y that were predicted using two different computational models. I would like to see whether the correlation of Y and X1, and Y and X2 is

Hello, I have been looking for functions for calculating the within-groups variance and between-groups variance, for the case where you have several numerical variables describing samples from a number of groups. I didn't find such functions in R, so wrote my own versions myself (see below). I can calculate the within- and between-groups variance for the Sepal.length variable (iris[1]) in

Dear all, I've written a small booklet on using R for time series analysis, available here : http://a-little-book-of-r-for-time-series.readthedocs.org/ <http://a-little-book-of-r-for-time-series.readthedocs.org/> It is just a little booklet for beginners like myself (I am studying Stats, and mostly wrote it to help myself study and understand this material), but thought it

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

Dear all, I am running a logistic regression and this is the output: glm(formula = educationUniv ~ brncntr, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max # ???? ????? ?? ???????? -0.8825 -0.7684 -0.7684 1.5044 1.6516 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.06869 0.01155 -92.487 <2e-16 *** brncntrNo

I am getting a repeated error when I try to run a logistic regression in R 2.8.1 >(glm(prop1~x1,data=glm1,family=binomial("logit"),weights=nt1)) Error in model.frame.default(formula = prop1 ~ x1, data = glm1, weights = nt1, : invalid type (list) for variable 'x1' x1 is multistate categorical (3 categories). 2 of the categories have 12 observation, one has 9. Is this what