similar to: Type III sums of squares.

Hello, as far as I see, R reports type I sums of squares. I'd like to get R to print out type III sums of squares. e.g. I have the following model: vardep~factor1*factor2 to get the type III sum of squares for factor1 I've tried anova(lm(vardep~factor2+factor1:factor2),lm(vardep~factor1*factor2)) but that didn't yield the desired result. Could anyone give me a hint how to proceed?

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

hi there, i have a few questions about the correct interpretation of a paired t-test. (i don't think that this matters, but I'm using R 2.10.1 on Windows). to my questions: i've been using a lot of time about this minor concerns now and I hope you can help me... I use one- and two-sided t-tests. My questons are on one side about how R uses the hypothesis' and the second one

Hi everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

Here's one possibility, assuming muhat and sigmahat are estimtes of mu and sigma from N iid draws of N(mu, sigma^2): tStat <- abs(x - muhat) / sigmahat pValue <- pt(tStat, df=N, lower=TRUE) I'm not quite sure what df tStat should have (exercise for math stat), but given fairly large N, that should make little difference. Andy > From: Ross Clement > > Hi. I have a

Dear R users, I am analyzing several response variables (all scaled to [0;1]) using a multivariate linear model. After fitting the model, I set up a hypothesis matrix to test specific contrasts for these response variables; for example: "a always increases significantly more than b when regressed against x". What I am stuck with now is how to calculate the correct F-values (and

Hi. I have a question that I have asked in other stat forums but do not yet have an answer for. I would like to know if there is some way in R or otherwise of performing the following hypothesis test. I have a single data item x. The null hypothesis is that x was selected from a normal distribution N(mu,sigma). The alternate hypothesis is that x does not come from this distribution. However, I

Dear R developers, Currently many (all?) test functions in R describe the alternative hypothesis, but not the the null hypothesis being tested. For example, cor.test: > require(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="kendall")) Kendall's rank correlation tau data: mpg and wt z = -5.7981, p-value = 0.000000006706 alternative hypothesis: true tau is not

Hi, I had a question about specifying the Null hypothesis in a significance test. Advance apologies if this has already been asked previously or is a naive question. I have two samples A and B, and I want to test whether A and B come from the same distribution. The default Null hypothesis would be H0: A=B But since I am trying to prove that A and B indeed come from the same distribution, I think

# Hi all: #I got a vector with fish lengths(mm) # Can someone help me interpret the output of # a t.test in plain english? # Based on the t.test below I can say that # I reject the null hypothesis because # the p-value is smaller than the the significance # level(alpha=0.05). What else can I conclude here? Ho = 36 mm Ha <> 36 mm fishlength

I am working with a RCB 2x2x3 ANCOVA, and I have noticed a difference in the calculation of sum of squares in a Type III calculation. Anova output is a follows: > Anova(aov(MSOIL~Forest+Burn*Thin*Moisture+ROCK,data=env3l),type=3) Anova Table (Type III tests) Response: MSOIL Sum Sq Df F value Pr(>F) (Intercept) 22.3682 1 53.2141 3.499e-07 *** Forest

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

Hello everyone, I'm having some trouble interpreting the results of a Wilcoxon (Mann-Whitney U) test. Hope you can help. This is the R script that I am running: a <- c(1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 5, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1) b <- c(1, 2, 1, 1, 2, 3, 2, 2, 1, 2, 1, 1, 1, 2) wilcox.test(a, b, alternative="t", mu=0, exact=FALSE, paired=FALSE) #1st

Dear all, For analysis of a longitudinal data set with fixed measurement in time I built a gls model (nlme). For testing hypotheses in this model I used the linear.hypothesis function from the car package. A check with the results obtained in SAS proc MIXED with a repeated statement revealed an inconsistency in the results. The problem can be that the linear.hypothesis function (1) only gives the

Greetings, I'm rather new to R, and only just subscribed to the mailing list. I have run into a strange result in a t-test. The data represents the the hypothetical differences obtained for a t-test for dependent samples. All the numeric output looks OK, but the statement that the alternative hypothesis is accepted seems rather strange. > data <- c(-6,6,-4,11,6,-3,-12,7,-1,4) >

Hi There, Using t.test to test hypothesis about which one is greater, A or B? where A={10,20,30},B={25,30,15}. My question is which of the following conclusions is right? #################hypothesis testing 1 h0: A greater than or equal to B h1: A less than B below is splus code A=c(10,20,30) B=c(25,30,15) t.test(c(10,20,30),c(25,30,15),alternative="less") output: p-value=0.3359

Help needed! Has anyone access to StatXact? I just hacked exact two-sided p-values for rank tests (for package exactDistr, which will move to CRAN/contrib as exactRankTests soon ;-) and would like to compare the results of my implementation to that of StatXact. Could someone please calculate the exact one-sided (both greater and less) and two-sided p-values? # Data from the StatXact-4 manual,

Hi list,Could you please help me to explain the following error messages with 't.test' in R Unix 2.1.1? I don't see it in R under Windows (R 2.3.0) or Unix (R2.3.1). Is it really due to the different R versions?Thanks,...TaoUnix session: (R.2.1.1)========================> R.version _ platform x86_64-unknown-linux-gnuarch x86_64 os

Dear colleague: Actually that is what is done. When using the z-test between proportions in two different groups, or using chi-squared to test the null hypothesis of equal proportions of two or more groups, the null hypothesis is that H0: p1=p2=p3.....=p where this p is the pooled proportion, the proportion in the summed groups = n1+n2+n3+..../N1+N2+N3.... in the z-test between two

Hi There, There are 300 boy students and 100 girl students in a class. One interesting question is whether boy is smarter than girl or not. first given the exam with a difficulty level 1, the number of the student who got A is below 31 for boy, 10 for girl. Then we increase the difficulty level of the exam to level 2, the number of the student who got A is below 32 for boy, 10 for girl. We