search for: hypotheses

I'm a programmer in a biology lab who is starting to use R to automate some of our statistical analysis of growth rate determination. But I'm running into some problems as I re-code. 1) Hypotheses concerning Slope similarity/difference: I'm using R's anova(lm()) methods to analyse a model which looks like this: growth.metric ~ time * test.tube I understand that testing the the interaction between time and tube (time:test.tube) will tell us if the growth rates (for...

...coef exp(coef) se(coef) z p treatmentpyridoxine -0.063 0.939 0.161 -0.391 0.70 treatmentthiotepa -0.159 0.853 0.168 -0.947 0.34 Likelihood ratio test=0.91 on 2 df, p=0.635 n= 294 > glht(fit,linfct=mcp(treatment='Tukey')) General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Linear Hypotheses: Estimate pyridoxine - placebo == 0 -0.06303 thiotepa - placebo == 0 -0.15885 thiotepa - pyridoxine == 0 -0.09582 However, once I added a strata term in the formula of coxph(), then glht() can't...

I'm coming from the AMOS world and am wondering if there is a simple way to do multiple hypothesis testing in the manner of BIC analyses in AMOS using the sem package in R. I've read the documentation, but don't see anything in there except for basic BIC scores. Perhaps someone has devised a simple way to compare the relative likelihood of all possible path-fittings within a

Hello everybody, I have some questions on ANOVA in general and on ANOVA in R particularly. I am not Statistician, therefore I would be very appreciated if you answer it in a simple way. 1. First of all, more general question. Standard anova() function for lm() or aov() models in R implements Type I sum of squares (sequential), which is not well suited for unbalanced ANOVA. Therefore it is better

Full_Name: Yves Rosseel Version: 2.2.1 OS: Submission from: (NULL) (157.193.116.152) The code below sketches a possible implementation of a function 'mlh.mlm' which I think would be a good complement to the 'anova.mlm' function in the stats package. It tests a single linear hypothesis of the form H_0: LBT'= 0 where B is the matrix of regression coefficients; L is a matrix

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

I was wondering, whether there is a way to have statistical significance test for cluster agreement. I know that I can use classAgreement() function to get Rand index, which will give me some indication whether the clusters agree or not, but it would be interesting to have a formal test. Thanks.

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 asymptotic chi square test and/...

Dear R Gurus: Is there a test for a single variance available in R, please? Thanks, Edna Bell

...ative difference in the redemption rate between control group and test groups. Now, applying the post hoc test: > Treat.comp<-glht(mod.binposthoc,mcp(bono_recibido='Tukey'))> summary(Treat.comp) # el modelo se encuentra en log odds aqui Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = TRAN_DURING_CAMP_FLG ~ bono_recibido, family = "binomial", data = exp2) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) BONO3EUROS - benchmark == 0 -0.87277 0.09931 -8.788...

...the results of a Friedman test. It seems to me that the p-value resulting from a Friedman test and with it the "significance" has to be interpreted in another way than the p-value resulting from e.g. ANOVA? Let me describe the problem with some detail: I'm testing a lot of different hypotheses in my observer study and only for some the premises for performing an ANOVA are fulfilled (tested with Shapiro Wilk and Bartlett). For the others I perform a Friedman test. To my surprise, the p-value of the Friedman test is < 0.05 for all my tested hypotheses. Thus, I tried to "compare&qu...

I have a three-way unbalanced ANOVA that I need to calculate (fixed effects plus interactions, no random effects). But word has it that aov() is good only for balanced designs. I have seen a number of different recommendations for working with unbalanced designs, but they seem to differ widely (car, nlme, lme4, etc.). So I would like to know what is the best or most usual way to go about working

...woing: > library(HH) > mca.1993 <- mcalinfct(mydata.aov, "Trt") > non.zero <- mca.1993[,5:6] != 0 > mca.1993[,5:6][non.zero] <- 1993 * sign(mca.1993[,5:6][non.zero]) > summary(glht(mydata.aov, linfct=mca.1993)) Simultaneous Tests for General Linear Hypotheses Fit: aov(formula = y ~ Trt * year, data = mydata) Linear Hypotheses: Estimate Std. Error t value Pr(>|t|) B - A == 0 2.8779 0.5801 4.961 0.00215 ** C - A == 0 -2.8845 0.5801 -4.972 0.00191 ** C - B == 0 -5.7624 0.5801 -9.933 < 0.001 *** --- Signif. codes:...

I have a system with a binary response variable that was hypothesized to follow a simple logistic function. The relationship between the continuous independent variable and the logit is clearly not monotonic. I have two questions. 1) Can anyone recommend a reference that describes my modeling options in this case, and 2) what facilities does R have to deal with this situation? Thanks, Kevin

...c tests (z-statistics): > ph_conditional <- c("des_days1 = 0", "des_days14 = 0", "des_days48 = 0"); > lev.ph <- glht(lev.lm, linfct = ph_conditional); > summary(lev.ph) Simultaneous Tests for General Linear Hypotheses Fit: lme.formula(fixed = data ~ des_days, data = data_red_trf, random = ~des_days | ratID, method = "ML", na.action = na.omit, control = lCtr) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) des_days1 == 0 -0.002632 0.007428 -0.354 0.971 des_days14...

Hello all! I would like to start off by saying that I am still really new to the vast world of R so please excuse my very limited vocabulary in the program. I have collected data from monkey videos and would like to setup some model(s) in R that can help with my hypotheses. I am having trouble figuring out which statistical tests/models to use for my two hypotheses. #1: Comparing the presence of an observer (a categorical variable consisting of either Approaching/Near, Near or On Top of Cage) to the presence of aggressive behavior (again categorical variables consi...

...#39;R for Windows GUI encountered a problem and needs to close...'). I think the problem comes from an rgl call, but, I can't get a traceback or other information because my R session crashes. I've never seen this behavior before. The problem occurs *whenever* I try to supply linear hypotheses to be displayed along with the model terms. Here is a small example: library(heplots) # Soils data, from car package soils.mod <- lm(cbind(pH,N,Dens,P,Ca,Mg,K,Na,Conduc) ~ Block + Contour*Depth, data=Soils) Anova(soils.mod) heplot(soils.mod, variables=c("Ca", "Mg")) ## th...

...is warning is smth that I should ignore or not. And if not, what I should do about it (I kind of know how to deal with this problem for categorical factors, however, in my data, one of the factors (AveScore) is continuous). Thanks a lot! Yuliia Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = EW1 ~ AveScore + Speaker + File + factor(Bplace) + factor(Sex) + AveScore:File, family = binomial(link = "logit"), data = data.0) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) In -...

...a post hoc test with glht() to find out between which samplings: >summary(glht(mod, linfct=mcp(sampling="Tukey"))) The results seem to say that there are no significantly different slopes for any of the pair-wise comparisons of factor levels: Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: aov(formula = h ~ sampling * dist, data = data) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) sp - au == 0 0.06696 0.04562 1.468 0.457 su - au == 0 -0.02238 0.04562 -0.491 0.961 wi - au == 0 0.01203...

...f Observations: 67 Number of Groups: 7 > anova(co.lme) numDF denDF F-value p-value (Intercept) 1 60 23.552098 <.0001 stand 2 4 3.738199 0.1215 > summary(glht(co.lme,linfct=mcp(stand="Tukey"))) Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: lme.formula(fixed = coye ~ stand, data = R, random = ~1 | site) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) 2 - 1 == 0 0.4853 0.3004 1.616 0.2385 3 - 1 == 0 -0.3159 0.3251 -0.972 0.5943 3 - 2 == 0 -...