similar to: Basic Model Setup Question from a Beginner

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

Hi, I am experimenting with using glht() from multcomp package together with coxph(), and glad to find that glht() can work on coph object, for example: > (fit<-coxph(Surv(stop, status>0)~treatment,bladder1)) coxph(formula = Surv(stop, status > 0) ~ treatment, data = bladder1) coef exp(coef) se(coef) z p treatmentpyridoxine -0.063 0.939 0.161

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

Dear All I have run the following GLM binominal model on a dataset composed by the following variables: TRAN_DURING_CAMP_FLG enviados bono_recibido 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1

Hello, I have problems interpreting 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

Hi all, I am fitting a linear mixed model with lme4 in R. The model has a single factor (des_days) with 4 levels (-1,1,14,48), and I am using random intercept and slopes. Fixed effects: data ~ des_days Value Std.Error DF t-value p-value (Intercept) 0.8274313 0.007937938 962 104.23757 0.0000 des_days1 -0.0026322 0.007443294 962 -0.35363 0.7237 des_days14 -0.0011319

Hi there, The following data is obtained from a long-term experiments. > mydata <- read.table(textConnection(" + y year Trt + 9.37 1993 A + 8.21 1995 A + 8.11 1999 A + 7.22 2007 A + 7.81 2010 A + 10.85 1993 B + 12.83 1995 B + 13.21 1999 B + 13.70 2007 B + 15.15 2010 B + 5.69 1993 C + 5.76 1995 C + 6.39 1999

[Env: Tested under Win Xp, R 2.9.2 and R 2.10.1; sessionInfo() at end] I've run into a problem with the heplot3d() function in my heplots package which causes the R GUI to crash ('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

Dear all, I was wondering if you could give me any suggestions/help on the following issue. So I carried out the analysis of my data using generalized linear model (glm). After that, to check for multiple comparisons, I applied the glht function from the multcomp package in R. The output, however, gave me a warning (please see below). So my question is whether this warning is smth that I should

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

I have been reading about autocorrelation in linear models over the last couple of days, and I have to say the more I read, the more confused I get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for guidance. Most authors are mainly worried about autocorrelation in the residuals, but some authors are also worried about autocorrelation within Y and within X vectors

Hi all, I've spent quite a lot of time searching through the help lists and reading about how best to run perform a 2-way ANOVA with unbalanced data. I realize this has been covered a great deal so I was trying to avoid adding yet another entry to the long list considering the use of different SS, etc. Unfortunately, I have come to the point where I feel I have to wade in and see if someone

Hi there, as part of a 2 x 2 contingency table analysis I would like to estimate conditional probabilities (success rates) in a Bernoulli experiment. In particular I want to test a null hypothesis p <= p0 versus the alternative hypothesis p > p0. As far as I understand the subject, there are UMPU tests for these types of hypotheses. Now I know about R's "binom.test" but the

Hello. Please excuse this off-topic request, but I know that the question has been debated in summary form on this list a number of times. I would find a paper that lays out the advantages and disadvantages of using different types of SS in the context of unbalanced data in ANOVA, regression and ANCOVA, especially including the use of different types of contrasts and the meaning of the

No, since I'd like to test null: p <= p0 alternative: p > p0. and my understanding is that binom.test tests null: p = p0 (can only be a "simple" null hypothesis according to help(binom.test)) alternative: p > p0 (or p < p0 or p != p0). Thanks, Mirko. > -----Urspr?ngliche Nachricht----- > Von: Douglas Bates [mailto:bates at stat.wisc.edu] >

Hello all, I would like to do a Dunnett test in glht(). However, the factor level I want to use as the control is not the first. dunn1<-glht(model3, linfct = mcp(Container = "Dunnett"), alternative = "less") The factor container has 8 levels, so it would be nice not to manually enter in all of the contrasts. I originally discovered glht() when working with a glm model

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers

I don't know from the nortest package, but it should ***always*** be the case that you test hypotheses H_0: The data have a normal distribution. vs. H_a: The data do not have a normal distribution. So if you get a p-value < 0.05 you can say that ***there is evidence*** (at the 0.05 significance level) that the data are not from a normal distribution. If the nortest package does