search for: linearhypothesi

...his. I'm running a regression, say: reg <- lm(Y ~ x1 + year) where x1 is a continuous variable and year is a factor with various year levels. Individually, each year factor variable is not significant, but I have a suspicion they are jointly significant. I can't figure out how to run a linearHypothesis test with a factor, i.e. linearHypothesis(reg, ??? year=0 ??? , vcov=vcovHC(reg, , "HC1")) Would be very much appreciated. Many thanks, Alex -- View this message in context: http://r.789695.n4.nabble.com/linearHypothesis-and-factors-tp4635814.html Sent from the R help mailing list ar...

...ture),c("I vs. X&M","X vs. M")) contrasts(env$Moisture)<-Transect_moisture_contrasts > contrasts(env3l$Moisture) I vs. X&M X vs. M X -1 1 I 2 0 M -1 -1 soilmodel<-lm(MSOIL~Forest+Burn*Thin*Moisture+ROCK,data=env3l) > linearHypothesis(soilmodel,"MoistureI vs. X&M") Linear hypothesis test Hypothesis: MoistureI vs. X&M = 0 Model 1: restricted model Model 2: MSOIL ~ Forest + Burn * Thin * Moisture + ROCK Res.Df RSS Df Sum of Sq F Pr(>F) 1 22 9.4106 2 21 8.8272 1 0.58333 1.3877 0.252 &g...

...HFe p[HF] p[HF]<.05 2 Factor 0.8480886 0.5258849 0.9795716 0.5408084 > ezANOVA(data, dv = .(Measure.1), wid = .(Participant), within = .(Factor), + type = 3, detailed = T) Note: model has only an intercept; equivalent type-III tests substituted. Error in linearHypothesis.mlm(mod, hyp.matrix, SSPE = SSPE, idata = idata, : The error SSP matrix is apparently of deficient rank = 4 < 5 Error in ezANOVA_main(data = data, dv = dv, wid = wid, within = within, : The car::Anova() function used to compute results and assumption tests seems to have failed. Most com...

...ut setting parameters equal? I have used the car package to set up linear hypotheses: X1 = rnorm(20, 10, 5); X2 = rnorm(20, 10, 5); X3 = rnorm(20, 10, 5) Y = .5*X1 + 3*X2 + .5*X3 + rnorm(20, 0, 15) data.set = data.frame(cbind(X1, X2, X3, Y)) linMod = lm(Y~X1 + X2 + X3, data=data.set) require(car) linearHypothesis(linMod, c("(Intercept)=0", "X1-X3=0")) (forgive the unconventional use of the equal sign....old habit). Unfortunately, the linearHypothesis is always compared to a full model (where the parameters are freely estimated). I want to have an ANOVA summary table for the reduced mod...

...ericity is violated (i.e. leaving aside post-hoc to lme models). The best solution I found was John Fox's proposal to similar requests in R-help: http://tolstoy.newcastle.edu.au/R/e2/help/07/09/26518.html http://tolstoy.newcastle.edu.au/R/e10/help/10/04/1663.html However, I think that using linearHypothesis() is not as straightforward as I would desire for testing specific contrasts between factor levels. The hypotheses must be defined as linear combinations of the model coefficients (subject to response transformations according to the intra-subjects design), which may need some involved calculation...

...gender = c("Male", "Female", "Male", "Female", "Male", "Female")) model <- lm(income ~ education * gender, data = data) # Test if the beta for "education" is significantly different between genders test <- linearHypothesis(model, "genderMale - genderFemale = 0") print(test) This, however, produces an error that I can't find a way to resolve. Can this test actually be done in this manner, or is this a case of AI run amok. Guidance would be appreciated. --John Sparks [[alternative HTML version del...

...c("Male", "Female", "Male", "Female", "Male", "Female")) >> model <- lm(income ~ education * gender, data = data) >> # Test if the beta for "education" is significantly different between genders >> test <- linearHypothesis(model, "genderMale - genderFemale = 0") That last line appears unlikely to be parsed correctly. In R a ?=? sign is interpreted as assignment whereas a ?==? (doubled equals) is a logical operator. Since I?ve only got a iPhone at hand I can?t test. In the future you should include full te...

...gender = c("Male", "Female", "Male", "Female", "Male", "Female")) > model <- lm(income ~ education * gender, data = data) > # Test if the beta for "education" is significantly different between genders > test <- linearHypothesis(model, "genderMale - genderFemale = 0") > print(test) > > This, however, produces an error that I can't find a way to resolve. > > Can this test actually be done in this manner, or is this a case of AI run amok. > > Guidance would be appreciated. > --John S...

Dear all If I run the model and get the estimated parameter a11. Then I want to use the estimated parameter to calculate the elasticities by using the formula e11=a11/mw1-1. What I have done is using the command of linear. Hypothesis. > formulas1=dWfresh~dlnPfresh+dlnPfrozen+dlnPsmoke+dlnQP+cosL1+sinL1+cosL2 >

Dear R fans, I have got a difficult sounding problem. For fitting a linear model using continuous response and then for re-fitting the model after excluding every single variable, the following functions can be used. library(MASS) model = lm(perf ~ syct + mmin + mmax + cach + chmin + chmax, data = cpus) dropterm(model, test = "F") But I am not sure whether any similar functions is

Dear friends, Sorry for this somewhat generically titled posting but I had a question with using contrasts in a manova context. So here is my question: Suppose I am interested in doing inference on \beta in the case of the model given by: Y = X %*% \beta + e where Y is a n x p matrix of observations, X is a n x m design matrix, \beta is m x p matrix of parameters, and e is a

Hello, I am work with a linear regression model: y=ax+b with the function of lm. y= observed migration distance of butterflies x= predicted migration distance of butterflies Usually the result will show if the linear term a is significantly different from zero based on the p-value. Now I would like to test if the linear term is significantly different from one. (because I want to know

...re applicable. An overview which lists some (but not all) useful methods is in Table 1 of vignette("betareg", package = "betareg"). For coef() and vcov() it is useful/important that the names and dimension match. Then Wald tests can be easily computed in functions like car::linearHypothesis(), car::deltaMethod(), lmtest::waldtest(), or lmtest::coeftest(). Thanks & best wishes, Achim > http://www.milbo.org/doc/modguide.pdf > > Your comments would be appreciated. > > Stephen Milborrow > > ______________________________________________ > R-package-devel at...

...t with the other factors), but when calling Anova on the model, I don't know how I can specify the between-within design (i.e., which parts of the model should interact with the repeated measures factors). As far as I understand it, neither the idesign, icontrasts or imatrix arguments, nor the linearHypothesis function can specify the within-between design (as far as I get it they all specify the within or intra-subject design, see John Fox's slides from User 2011: http://web.warwick.ac.uk/statsdept/useR-2011/TalkSlides/Contributed/17Aug_1705_FocusV_4-Multivariate_1-Fox.pdf). If this it is not poss...

Can anyone show me how to test for significant simple slopes of a 3-way interaction, with covariates. my equation tmod<-(glm(PCL~ rank.f + gender.f + MONTHS + CEXPOSE.M + bf.m + MONTHS*CEXPOSE.M*bf.m, data=mhatv, family=gaussian ,na.action=na.omit)) Thank you Mike [[alternative HTML version deleted]]

Dear r-helpers, I have a query regarding use of contrasts in MANOVA. summary(manova(model)) gives me only result of test for overall difference. Would you be so kind and give me a hint how to get the same test statistics (e.g.Pillai's) and P values for the predefined contrasts? Best regards Ondrej Mikula -- Institute of Animal Physiology and Genetics Academy of Sciences of the Czech

...s of multiple comparisons of factor contrasts, and is specially suited for the analysis of interaction effects. The function "testFactors" provides a flexible user interface for defining combinations of factor levels and covariates, to evaluate and test the model, using the function "linearHypothesis" from package "car". "testInteractions" gives a simpler interface to test multiple comparisons of simple effects, interaction residuals, interaction contrasts, or user-defined contrasts. "interactionMeans" may be used to explore the "cell means" of fact...

...s of multiple comparisons of factor contrasts, and is specially suited for the analysis of interaction effects. The function "testFactors" provides a flexible user interface for defining combinations of factor levels and covariates, to evaluate and test the model, using the function "linearHypothesis" from package "car". "testInteractions" gives a simpler interface to test multiple comparisons of simple effects, interaction residuals, interaction contrasts, or user-defined contrasts. "interactionMeans" may be used to explore the "cell means" of fact...

Dear List, I am trying to analyze a dataset where I have 1 continuous between-item variable (C), and 2 factorial within-item variables (3- and 2-level: F3, F2). I'm interested in whether slope of C is different from 0 at different combinations of F3 and F2, and whether it varies between these combinations. And unfortunately I need a decent anova-like table with p-values. The reason is that

Dear list, I use Sweave almost exclusively for writing papers, and I have become quite spoiled by the excellent xtable export facilities. Has anybody written an xtable method for the Anova function in CAR, or has anybody used a different set of functions to import Anova results into a table in an Sweave document? If not, any handy hints on how to write a good homebrew based on the output of Anova