similar to: Help producing plot for assessing forecasting accuracy

Hi, As part of an R code assingment I have been asked to find a quantitative procedure for assessing whether or not the data are normal? I have previously used the graphical procedure using the qqnorm command. Any help/tips would be greatly appreciated as to how I should start going about this! Henry _________________________________________________________________

I am analyzing a 1-to-2 matched case-control study using clogit in the survival package. I am interested in comparing and assessing fit of non-nested models. I don't want to program all the diagnostics described in Hosmer/Lemeshow (2000). Can someone proficient with clogit and assessing fit for non-nested models point me in the right direction. Many thanks! Tomas Tomas Aragon, MD, DrPH Tel:

Hello! I am trying to model data on species abundance (count data) with a poisson error distribution. I have a fixed and a random variables and thus needs a mixed model. I strongly doubt that my model is overdispersed but I don't know how to get the overdispersion parameter in a mixed model. Maybe someone can help me on this point. Secondly, it seems that quasi models cannot be implemented

Hi, using polynomial contrasts for the ordered factors in an experiment leads to much nicer covariance structure than using treatment contrasts. It is easy to assess the mean effect for each of the experimental groups. However, standard errors are provided only for the components of the orthogonal contrasts. I wonder how to assess the standard errors not of the components, but of the respective

This is a reminder. Please distribute this email. Data on both agile and plan-driven projects are welcome. To Whom It May Concern, My name is Donald Buresh, and I am a Ph.D. student at Northcentral University located in Prescott Valley, Arizona. The reason that I am writing to you is because I would like you to participate in an internet survey for my dissertation. The topic of my

Dear R-Users! I am using the svm function (e1071 package) to classify two groups using a set of 180 indicator variables. Now I am confused about the cross-validation procedure. (A) On one hand I use the setting cross=10 in the svm function to run 10 cross-validation iterations and to get an estimate of the svm's performance in prediction. (B) On the other hand most tutorials I found

Dear R-Helpers, I am looking for ways to assess quality of a predictor of variance of a random variable. Here a two related, but yet distinct, setups. 1. I observe y_t, t=1,...,T which is normally distributed with unknown variance v_t (note that the variance is time-dependent). I have two "predictors" for v_t, dubbed v1_t and v2_t, and I want to tell which predictor is better. Here

I have data like so: time datum 30 12 60 24 90 37 120 41 150 8 In addition to standard deviation, I want to measure the average of the differences in data for each time interval, i.e. average of 24-12, 37-24, 41-37, 8-41. Is there a statistical term for this task? Which package should I use please? rhelp at conference.jabber.org

I am in a situation where I have to fit a distrution, such as cauchy or normal, to an empirical dataset. Well and good, that is easy. But I wanted to assess just how good the fit is, using ks.test. I am concerned about the following note in the docs (about the example provided): "Note that the distribution theory is not valid here as we have estimated the parameters of the normal

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

I am trying to trouble shoot one of my ISP's network and compare to my other ISPs offering. Although network 1 is reasonably fast and has low enough latency, voice quality is not good and the reason for this is not readily apparent using standard network tools. What tools can be used to assess the quality of the network in terms of it's suitability for voice? I am using ping, mtr,

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

Hi the list, According to what I know, the Canberra distance between X et Y is : sum[ (|x_i - y_i|) / (|x_i|+|y_i|) ] (with | | denoting the function 'absolute value') In the source code of the canberra distance in the file distance.c, we find : sum = fabs(x[i1] + x[i2]); diff = fabs(x[i1] - x[i2]); dev = diff/sum; which correspond to the formula : sum[ (|x_i - y_i|) /

I am trying to find methods for testing and visualizing calibration to Cox models with time-depended coefficients. I have read this nice article <http://journals.sagepub.com/doi/10.1177/0962280213497434>. In this paper, we can fit three models: fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) p <- log(predict(fit0, newdata = data1, type = "expected")) lp

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hi all, I am attempting to apply a nonlinear model developed using nls to a new dataset and assess the fit of that model. At the moment, I am using the fitted model from my fit dataset as the starting point for an nls fit for my test dataset (see below). I would like to be able to view the t-statistic and p-values for each of the iterations using the trace function, but have not yet worked out

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

Canberra distance is defined in function `dist' (standard library `mva') as sum(|x_i - y_i| / |x_i + y_i|) Obviously this is undefined for cases where both x_i and y_i are zeros. Since double zeros are common in many data sets, this is a nuisance. In our field (from which the distance is coming), it is customary to remove double zeros: contribution to distance is zero when both x_i

Canberra distance is defined in function `dist' (standard library `mva') as sum(|x_i - y_i| / |x_i + y_i|) Obviously this is undefined for cases where both x_i and y_i are zeros. Since double zeros are common in many data sets, this is a nuisance. In our field (from which the distance is coming), it is customary to remove double zeros: contribution to distance is zero when both x_i