similar to: Weighted Regression

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at

Hi all, I have a large data.frame, 1530 observation with 6 columns. I want to merge a 7th column, a transformation of the response variable (hospital admissions), namely trans<-sqrt(copd$admissions+0.25) trans<-data.frame(trans) And now when I do copd2<-merge(copd,trans) (copd being my original data.frame), R either crashes or is taking an extremely long time to do the computation. I

Hi all, Firstly I appologise if this question has been answered previously, however searching of the archives and the internet generally has not yielded any results. I am looking in to the effects of summer weather conditions (temperature, humidity etc), on the incidences of a breathing disorder brought on through smoking (COPD). I am fairly new to R and completely new to the idea of

Hi all, Thanks for the help with my previous post. I have just two more questions for the minute. I think I said in a previous post that I like to use the terminal, i.e. run rterm.exe. On exiting the terminal, I am asked if I want to save the workspace. If I hit y (yes), the workspace is just saved as .rdata in my working directory, does anyone know how I can name it directly from the

Hi all, I am very confused with class. I am looking at some weather data which I want to use as explanatory variables in an lm. R has treated these variables as factors (i.e. with different levels), whereas I want them treated as discretely measured continuous variables. So I need to reassign the class of these variables, right? Indeed, doing class(southwest$pressure) (pressure being air

Hi everyone, I have carried out a multiple imputation in R using Amelia II and have created 5 multiply imputed datasets. The purpose of my research is to fit a Poisson Model to the data to estimate numbers of hospital admissions. Now that I have 5 completed datasets and I have to pool all the 5 datasets to get one combined output for a poisson model. I have checked previous queries about

As John noted, there are different kinds of weights, and different terminology: * inverse-variance weights (accuracy weights) * case weights (frequencies, counts) * sampling weights (selection probability weights) I'll add: * inverse-variance weights, where var(y for observation) = 1/weight (as opposed to just being inversely proportional to the weight) * weights used as part of an

Dear all, I am trying to implement goodness-of-fit tests for logistic regression models described in : Hosmer, D.W., Lemeshow, S., 2000. Applied logistic regression. New-York, John Wiley & Sons, Inc., 373 p. (pp. 152-154) Namely I would like to reproduce Osius & Rojek's test (Osius, G., Rojek, D., 1992. Normal goodness-of-fit tests for multinomial models with large degrees of

In modelling functions some people like to use a weight of 0 to drop an observation instead of using a subset value of FALSE. E.g., weights=c(0,1,1,...) instead of subset=c(FALSE, TRUE, TRUE, ...) to drop the first observation. lm() and summary.lm() appear to treat these in the same way, decrementing the number of degrees of freedom for each dropped observation. However, predict.lm() does

Hi, I have eight vectors that I would like to orthogonalize preferably using R. The vectors are of considerable length, however due to their nature I know they satisfy the conditions needed to apply the Gram-Schmidt algorithm. Before I embark on some R coding, I wanted to check that there is no facility / function already around that computes the orthogonalized set of vectors? I have performed

Hi, Is there any quick and easy way to rename a number of objects, without having to rename each one individually and then remove the old one? And if so, is there anything I can do to adjust the associated comments accordingly? Thanks for any help, Robin Williams Met Office summer intern - Health Forecasting robin.williams@metoffice.gov.uk [[alternative HTML version deleted]]

Hi all, Is there an equivalent to the general * operator in R, where * can represent any character? I have a dataset, one column being date, date ranging between April-September, 97-06. I would like to be able to acquire the data for a specific month, say September, so that I can take average temperatures for the month, etc. I thought I would be able to do something like data.s1 <-

Hi, Appologies for the simple nature of this question, I am unable to find the answer in manuals (EG and introduciton to R). I have written a function in a text editor and saved it with an .R extension. It is saved in my working directory. How can I run it, do I need to use source? If so, how do I supply the arguments to the function? Or does it need to be saved in a particular directory?

Hello, I'm looking to calculate a 95% confidence interval about my estimate for a sample's weighted mean, where the calculated confidence interval would equal the t-test confidence interval of the sample in the case when all of the weights are equal. My initial thought was to simply implement a modified version of the t-test function but substituting the weighted variance and mean for the

Hi, Is there a function in R to calculate the coefficient of skewness of some data? I had expected there to be one, but can find no information about it. Thanks for any pointers. Robin Williams Met Office summer intern - Health Forecasting robin.williams@metoffice.gov.uk [[alternative HTML version deleted]]

Hi all, When using lm to model a response with 8 explanatory variables, one of the variables is not defined due to "singularities". I have checked the csv file from which the data come, there are no na's in the dataset, etc. What should I be looking for in this variable to correct the problem? Thanks for any help. Robin Williams Met Office summer intern - Health Forecasting

Dear R, We are seeking a version of summary.lm appropriate for a fit of cell means (with specified weights and variance). Imagine one had 5 observations from each of two groups and wished to test a difference in means. A simple call to lm() and summary.lm() will return the appropriate test: raw.data <- data.frame(x = rep(0:1, rep(5, 2)), y = 1:10) fit.raw <- lm(y ~ x, data=raw.data)

Hi, Please could someone explain how this element of predict.lm works? From the help file ` newdata An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. ' Does this dataframe (newdata) need to have the same variable names as was used in the original data frame used to fit the model? Or will R just look across consecutive

To all I have some simple questions pertaining to weights used in regression. If the variability of the dependent variable (y) is a function of the magnitude of predictor variable (x), can the use of weights give an appropriate answer to the regression parameters and the std errors? Assume that y at x=1 and 6 has a standard deviation of 0.1 and at x=11 it is 0.4 Then according to a web page on

Sorry in the previos replay I forgot monday' scale so weight should be: weight <- c(0.91,1,1,1,1,1.21,1.22) names(weight) <- c("mon","tue","wed","thu","fri","sat","sun") ----- Original Message ----- From: "Gabriela Cendoya" <gcendoya at balcarce.inta.gov.ar> To: "Robin Williams" <>