similar to: strategy to iterate over repeated measures/longitudinal data

R-help: I am trying to decide between using a multiple linear regression or a linear mixed effects model for my data: model1 <- lm (responsevariable ~ predictor1 + predictor2 + predictor3 + predictor4, data= data) model2 <- lme (responsevariable ~ predictor1 + predictor2 + predictor3 + predictor4, random = ~1 | site, data= data) anova (model1, model2) but I keep getting the

Hi, I would like to combine multiple xyplots into a single, multipanel display. Using R 3.0.1 in Ubuntu, I have used c() from latticeExtra to combine three plots, but the x-axis for two plots are on a log scale and the other is on a normal scale. I also have included equispace.log=FALSE to clean up the tick labels. However, when I try all of these, the x-axis scale of the first panel is used

dear kind helper, i would like to know how to find the annual maximun for a table that basicly looks like this: date time measurement1 measurement2 measurement3 mm/dd/yyyy hh:mm:ss m1 m2 m3 there are about 9000 measurements for each year, which makes it quite large... i already tried to subset all rows for a year, to find the maximum within these choosen rows, y <-

R experts - I'm using John Fox's sem package to analyze a simple path model (two correlated predictor variables directly influencing a single criterion variable): Predictor1 -> Criterion Predictor2 -> Criterion Predictor1 <-> Predictor2 I'm giving a presentation on this material next week, and I'd like to use component-residual plots (i.e., partial residual plots)

I am using the gbm package for generalized boosted regression models, and would like to be able to extract the coefficients produced for storage in a database. I am already using R to automatically generate formulas that I can export to a database and store. For example, I have been using Dr. Harrell's lrm package to perform logistic regression, e.g.: output <-

Hi, I use a mboost model to predict my dependent variable on new data. I get the following warning message: In bs(mf[[i]], knots = args$knots[[i]]$knots, degree = args$degree, : some 'x' values beyond boundary knots may cause ill-conditioned bases The new predicted values are partly negative although the variable in the training data ranges from 3 to 8 on a numeric scale. In order to

I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10,

Hi all, I am trying to fit an ANOVA model in R using the aov/lm commands. I have a set of observational (i.e. no fixed experimental effects) data, in which I have identified high and low clusters of the response variable. The design is unbalanced, with 773 high cluster observations, and 523 low cluster observations. I would like to test a set of 7 correlates to see if there are significant

I was unsure of what suitable goodness-of-fit tests existed in R for logistic regression. After searching the R-help archive I found that using the Design models and resid, could be used to calculate this as follows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a

Hello, I came across a problem when building a randomForest model. Maybe someone can help me. I have a training- and a testdataset with a discrete response and ten predictors (numeric and factor variables). The two datasets are similar in terms of number of predictor, name of variables and datatype of variables (factor, numeric) except that only one predictor has got 20 levels in the training

I'm having trouble understanding how functions in the subset argument for lmList search for the objects they need. This trivial example produces "Error in rownames(fakedf) : Object "fakedf" not found": library(nlme) fitbyID <- function() { fakedf <- data.frame(ID = gl(5, 10, 50), A = sample(1:100, 50), B = rnorm(50))

If this has already been answered, my apologies in advance I am relatively new to this aspect of [R]. it is a bit of a basic question. I have 4 columns of data (site, Date, measurement type, value) in a tab delimited text file. Site is a site where measurements were collected, Date is a date in DD/MM/YYYY format, measurement is a code for the type of measurement made, and value just the value

Hi, I am trying to use CART to find an ideal cut-off value for a simple diagnostic test (ie when the test score is above x, diagnose the condition). When I put in the model fit=rpart(outcome ~ predictor1(TB144), method="class", data=data8) sometimes it gives me a tree with multiple nodes for the same predictor (see below for example of tree with 1 or multiple nodes). Is there a way

Hello! Just would like to make sure I am not doing something wrong. I am running an OLS regression. I have several subgroups in the data set (locations) - and in each location I have weekly data for 2 years - on my DV and on all predictors. Looks like this: location week DV Predictor1 Predictor 2 location1 week1 xxx xxxxxxx xxxxxxxxx location1 week2 xxx xxxxxxx xxxxxxxxx . .

If I do > example(lm) ... > mycoef <- function(object, ...) UseMethod("coef", object) > mycoef(lm.D9) Error in mycoef(lm.D9) : no applicable method for "coef" which is pretty surprising, as coef has a default method. After a bit of digging, this comes from do_usemethod having defenv = environment where the generic was defined */ defenv =

Hello! I'd like to create an assets-based economic indicator using data from a national household survey. The economic indicator is to be the first principal component from a principal components analysis, which (given the source of the data) I believe should take in consideration the sampling weights of the observations. After running the PCA with svyprcomp(), from the survey package, I

Hi all, I´m re-starting (as my name indicates) my little knowlegde of CRAN R. I made this function time before but I don´t know where is the error because nothing appears as an error but the histogram plot doesn´t appear. Should I install some special library to run sapply? pru<-function(){ randz<-matrix(rnorm(200000),100,2000) H<-matrix(0,100,2000) for (j in 2:2000){ for (i in

Hi all, I want to make the following: I want to run a linear regression on each column of a matrix "estima" on the correspondent column on the matrix "estima2". You see I want to regress estima[,1] on estima2[,1] this way to all columns.... At the same time I want to make a regression adding each time a new observation. You see, the first regression will regress only one

Hi, I've a data frame like this: > as.data.frame(cbind(rnorm(1:12),rnorm(1:12))) V1 V2 1 -1.30849402 -0.52094136 2 0.96157302 0.76217871 3 -0.44223351 -1.72630871 4 -0.10432438 -1.04732942 5 -1.38748914 0.95877311 6 -0.63965975 0.65494811 7 -0.24058318 0.19496830 8 -0.11172988 1.01680655 9 0.08065333 0.22168589 10 0.25196536 0.84619914 11

Hi, I'm using R to do a time series analysis. In the model, I use the lags of some variables. such the lags of the variables have different length, I just can't use them directly in the lm function. Intuitively, I feel that "subset" might be useful, but I do not know how to use it. Anyone can give me an example syntax? Thanks. Harry [[alternative HTML version deleted]]