search for: homoscedast

...SS). Is there a Wilk's lambda for discriminant functions in R? or can I use an alternative measure? or am I thinking in the wrong direction? I have searched the help-archive to find similar questions to mine but no answer to them. -My second problem: to check the assumption of multivariate homoscedasticity I have to test if the variance-covariance matrices for my variables are homogene. My textbook suggests Box's M test. I can't find this statistic in R. Again I have found similar questions in the help-archives, but no answers. Is there a way to calculate Box's M in R? Or is ther...

Hi R-programmers, I can't find find the White test to check the homoscedasticity of the residuals from a linear model. Could you please help me with this? Thank you ! BC [[alternative HTML version deleted]]

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,...

...st: (This is probably a stupid question).  Is there a "quick and easy" way to confirm the gauss-markov conditions of a linear multiple regression model?  That the mean of the residuals is 0 can easily be tested for. The normality of the residuals as well (shapiro-wilk?).  But what about homoscedasticity? And independence of residuals with respect to the model variables? Thanks in advance [[alternative HTML version deleted]]

I have two regressions to perform - one with a metric DV (-3 to 3), the other with an ordered DV (0,1,2,3). Neither normal distribution not homoscedasticity is given. I have a two questions: (1) Some sources say robust regression take care of both lack of normal distribution and heteroscedasticity, while others say only of normal distribution. What is true? (2) Are there ways of using robust regressions with ordered data, or is that only possible...

Dear Sir, Many thanks for your reply. I have a query. I have a whole set of distributions which should be made normal / homoscedastic. Take for instance the warpbreaks data set. We have the following boxplots for the warpbreaks dataset: a. boxplot(breaks ~ wool) b. boxplot(breaks ~ tension) c. boxplot(breaks ~ interaction(wool,tension)) d. boxplot(breaks ~ wool @ each level of tension) e. boxplot(breaks ~ tension @ each level...

...p://socserv.mcmaster.ca/jfox/ On 2018-01-09, 10:18 AM, "Ashim Kapoor" <ashimkapoor at gmail.com> wrote: >Dear Sir, > > >Many thanks for your reply. > > >I have a query. > > > >I have a whole set of distributions which should be made normal / >homoscedastic. Take for instance the warpbreaks data set. > > > >We have the following boxplots for the warpbreaks dataset: > > >a. boxplot(breaks ~ wool) > >b. boxplot(breaks ~ tension) > >c. boxplot(breaks ~ interaction(wool,tension)) >d. boxplot(breaks ~ wool @ each level...

Dear Ashim, Try spreadLevelPlot(breaks ~ interaction(tension, wool), data=warpbreaks) . I hope this helps, John ----------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: socialsciences.mcmaster.ca/jfox/ > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Ashim > Kapoor > Sent:

Hi, I'm a new user of R. My background is Electrical Engineering, so please bear with me if this is a silly question. I'm trying to assess whether the results of an experiment satisfy the hypothesis of homoscedasticity (my ultimate goal is to use ANOVA). The result of the experiment is mean delay (dT), which depends on three factors, topology, drift, and lambda. The first two factors are categorical (with 4 levels each) and the last one is numerical, with two levels. A sample of my data is as follows: dT...

...rmine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity, homoscedasticity, independence, and normally-distributed residuals. Absent the last requirement it is optimal but only over unbiased linear estimators. What I am trying to determine is whether or not it is necessary to check for normally-distributed errors in a GAM from mgcv. I know that the unsmoothed term...

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be very cumbersome. Is there...

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 variance ratio of Lo and Mackinlay (1988) and Chow and Denning (1993)     Waiting eagerly for your reply. Amira Akl Ahmed [[alternativ...

Hello everybody, Is there in R a so called box m-test for testing the equality of the variance/cov. matrix for checking on homoscedasticity? I could not find it among the traditional packages for multivariate statistics... Petra -- Petra Wallem Centro de Estudios Avanzados en Ecolog伱伃a & Biodiversidad (CASEB) Departamento de Ecolog伱伃a Facultad de Ciencias Biol伱伋gicas Pontificia Universidad Cat伱伋lica de Chile Av. Libertador Be...

...for my response are, using the explanatory (covariates). (The other two factors are: Temperature and force needed). I mean, I want to create groups for the levels of the factor, where within each group no significant difference exist. My residuals haven't normal distribution and aren't homoscedastic. I have tried already the boxcox transformation and glm procedures, but whitout success. So I am looking for these methos for nonparametrics analysis of covariance. My model is: mod<-lm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 +...

...(ind), env) : # Missing value or an infinity produced when evaluating the model 1: In min (x) found no arguments to min; Inf is returned #it is my translation 2: In max (x): no arguments to max;-Inf is returned - Please where is the problem? - What I can do to obtain R^2? - The way to verify Homoscedasticity and normality is the same for linear model? Thank you in advance KOMINE -- View this message in context: http://r.789695.n4.nabble.com/exponential-model-with-decreasing-tp3747572p3747572.html Sent from the R help mailing list archive at Nabble.com.

...IDF, gr, CV = FALSE) and it works fine. But I am missing a test and cannot find any help on how to get it, if it exist. The "S" equivalent: discrim(structure(.Data = Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, class = "formula"), data = gr, family = Canonical(cov.structure = "homoscedastic"), na.action = na.omit, prior = "proportional") outputs a nice matrix of Mahalanobis distances between groups and even tests (Hotelling's T Squared) for significant distances. Why don't I just take the "S" output you say? Because like you, I'd rather put i...

Hi Deepak, In econometrics there is another test very often used : the white test. The white test is based on the comparison of the estimated variances of residuals when the model is estimated by OLS under the assumption of homoscedasticity and when the model is estimated by OLS under the assumption of heteroscedastic. The White test with R install.packages("bstats") library(bstats) white.test(LinearModel) Hope this helps. Sacha ________________________________ De : Deepak Singh <sdeepakrhelp at gmail.com...

...nmental variables The long-term plan is to perform a canonical correspondence analysis (CCA in the vegan package) on it but the data obviously has to conform to some standarts first. Ideally, any two variables should be in a linear relationship and multivariate normality should be given as well as homoscedasticity (I haven?t tested for this one yet, that?ll be another adventure). Now my data - surprise - does not conform to a normal distribution nor do the relationships seem linear so I need to transform it (but which parts?). The usual log transformation doesn't change anything so I found this one...

...f plsr or its underlying theory, so the fact that this is not throwing an error is no guarantee of validity. Using this construction in ordinary least squares regression has dangers with inferential statistics because of the correlation of the linear and squared terms as well as likely violation of homoscedasticity. -- David. > > May I have some help please? > > Thanks, > > Kelvin > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.eth...

...4 Apr 2016, varin sacha via R-help wrote: > Hi Deepak, > > In econometrics there is another test very often used : the white test. > The white test is based on the comparison of the estimated variances of > residuals when the model is estimated by OLS under the assumption of > homoscedasticity and when the model is estimated by OLS under the > assumption of heteroscedastic. The White test is a special case of the Breusch-Pagan test using a particular specification of the auxiliary regressors: namely all regressors, their squares and their cross-products. As this specification...