similar to: Robust regression for ordered data

Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you

Hello! I have a data frame A (below) with a grouping factor (group). I take my DV and create the new, lagged DV by applying the function lag.it (below). It works fine. A <- data.frame(year=rep(c(1980:1984),3), group= factor(sort(rep(1:3,5))), DV=c(rnorm(15))) lag.it <- function(x) { DV <- ts(x$DV, start = x$year[1]) idx <- seq(length = length(DV)) DVs <- cbind(DV, lag(DV,

This is a simple problem that has stumped me: I'm trying to loop through a few dozen variable names in graphs. I've tried various approaches like this: attach(mydata) ivs <- c("oneiv", "anotheriv", "yetanotheriv") dvs <- c("onedv", "anotherdv", "yetanotherdv") for (iv in ivs) { for (dv in dvs) { graphname <- paste(iv,

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

I would like to obtain Confidence Intervals for the estimates (unstandardized beta weights) of each predictor in a WLS regression: m1 = lm(x~ x1+x2+x3, weights=W, data=D) SPSS offers that output by default, and I am not able to find a way to do this in R. I read through predict.lm, but I do not find a way to get the CIs for multiple independent variables. Thank you Torvon [[alternative HTML

I have a dataset with plenty of variables and lots of missing data. As far as I understand, R automatically removes subjects with missing values. I'm trying to fit a mixed effects model, adding covariate by covariate. I suspect that my sample gets smaller and smaller each time I add a covariate, because more and more lines get deleted. Is there a way of displaying how many subjects are

A colleague wrote the following syntax for me: D = read.csv("x.csv") ## Convert -999 to NA for (k in 1:dim(D)[2]) { I = which(D[,k]==-999) if (length(I) > 0) { D[I,k] = NA } } The dataset has many missing values. I am running several regressions on this dataset, and want to ensure every regression has the same subjects. Thus I want to drop subjects listwise for

I have two very strong fixed effects in a LMM (both continuous variables). model <- lmer( y ~ time + x1+x2 + (time|subject)) Once I fit an interaction of these variables, both main effects disappear and I get a strong interaction effect. model <- lmer( y ~ time + x1*x2 + (time|subject)) I would like to plot this effect now, but have not been able to do so, reading through ggplot2 and

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

Dear Group, I have the following dataset: ID REPI DV CONC SS 1 1 156.84 116 0 1 2 146.56 116 0 1 3 115.13 116 0 1 4 207.81 116 0 1 5 129.53 116 0 1 6 151.48 116 0 1 7 158.95 116 0 1 8 192.37 116 0 1 9 32.97 116 0 1 10 151.66 116 0 I want to calculate the percentile of each CONC within ID=i and add as a column

I'm trying to fit a model to solve a biological problem. There are multiple independent variables, and also there are multiple responses. Each response is a function of all the independent variables, plus a set of parameters. All the responses depend on the same variables and parameters - just the form of the function changes to define each seperate response. Any ideas how I can fit

Dear R-helpers, I'm using a GLM with poisson errors to model integer count data as a function of one non-integer covariate. The model formula is: log(DV) ~ glm(log(IV,10),family=poisson). I'm getting a warning because the logged DV is no longer an integer. I have three questions: 1) Can I ignore the warning, or is logging the DV (resulting in non-integers) a serious violation of the

Hi r-programmers, I performe Breusch-Pagan tests (bptest in package lmtest) to check the homoscedasticity of the residuals from a linear model and I carry out carry out White's test via bptest (formula, ~ x * z + I(x^2) + I(z^2)) include all regressors and the squares/cross-products in the auxiliary regression. But what can I do if I want find coefficient and p-values of variables x, z, x*z,

Probably a stupidly simple question, but I wouldn't know how to google it: xyplot(neuro ~ time | UserID, data=data_sub) creates a proper plot. However, if I add type = "l" the lines do not go first through time1, then time2, then time3 etc but in about 50% of all subjects the lines go through points seemingly random (e.g. from 1 to 4 to 2 to 5 to 3). The lines always start at time

Hello I am a new user of R software. I benefit from using vrtest-package. However, the codes provided by the aforementioned package, for example, calculate the test statistics for Lo and Mackinlay (1988) under the assumptions of homoscedasticity and heteroscedasticity without computing the value of the variance ratio itself.   I would be grateful if you could instruct me how to calculate the

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

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i