search for: freedom

...my Favorite fido7 echoconference RU.UNIX.BSD. Noone even told me that he have working audit (but also noone report the same samba bug). Then I decided to send a bug-report (I'm not sure about how I must send it - sorry, if i sent you bogus bugreport). My system is ===Cut=== # uname -a FreeBSD freedom.norma.perm.ru. 4.7-STABLE FreeBSD 4.7-STABLE #0: Sat Oct 26 22:43:56 YEKST 2002 root@:/usr/obj/usr/src/sys/FREEDOM i386 ===Cut=== I have samba compiled from FreeBSD Ports, with vfs support included - I'm sure about it, cause I got working Recycles on my shares. My samba is ===Cut=== WHAT...

Hello all. I was wondering if there is any way to adjust the denominator degrees of freedom in lme(). It seems to me that there is only one method that can be used. As has been pointed out previously on the list, the denominator degrees of freedom given by lme() do not match those given by SAS Proc Mixed or HLM5. Proc Mixed, for example, offers five different options for computing the...

Hello. Douglas Bates has explained in a previous posting to R why he does not output residual degrees of freedom, F values and probabilities in the mixed model (lmer) function: because the usual degrees of freedom (obs - fixed df -1) are not exact and are really only upper bounds. I am interpreting what he said but I am not a professional statistician, so I might be getting this wrong... Does anyone know of...

Hello, I'm running mixed models in GAMM4 with 2 (non-nested) random intercepts and I want to include a spline term for one of my exposure variables. However, when I include a spline term, I always get reported degrees of freedom of less than 1, even when I know that my spline is using more than 1 degree of freedom. For example, here is the code for my model: > global.gamm4<-gamm4(zcog~s(adjpatx, fx=TRUE, k=5)+int234+cogagec+cogagesq + + + oldfran +newus +alc2 +alc3 +alc4 +alcmiss +smk2 +smk3 +...

Hello, I've just carried out my first good-looking model using glmmPQL, and the output makes perfect sense in terms of how it fits with our hypothesis and the graphical representation of the data. However, please could you clarify whether my degrees of freedom are appropriate? I had 106 subjects, each of them was observed about 9 times, creating 882 data points. The subjects were in 3 treatment groups, so I have told the model to include subject as a random factor nested within treatment. There are two other variables and I'm interested in their two...

It appears that the degrees of freedom reported by logLik changed between R 1.4.1 and R 1.5.1. Is this true? Detail: > I have been using the lm and logLik functions in R to develop code using > version 1.4.1. When I run it on version 1.5.1, I'm getting different > degrees of freedom with the logLik function. Version 1...

After an RSiteSeach("Box.test") I found some discussion regarding the degrees of freedom in the computation of the Ljung-Box test using Box.test(), but did not find any posting about the proper degrees of freedom. Box.test() uses "lag=number" as the degrees of freedom. However, I believe the correct degrees of freedom should be "number-p-q" where p and q are...

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.9...

Dear R-ers, I am currently running some Wilcoxon tests in R-64. How do I find the degrees of freedom in the output I am receiving? > wilcox.test(good$TRUE, good$x4a, paired=FALSE) Wilcoxon rank sum test with continuity correction data: good$TRUE and good$x4a W = 2455, p-value < 2.2e-16 alternative hypothesis: true location shift is not equal to 0 Thank you, Stephen. University o...

...r(>|t|) (Intercept) 0.5043036 12.4874164 0.04038 0.968257 value 0.0695456 0.0183279 3.79452 0.001448 ** capital 0.3085445 0.0280530 10.99864 3.7702e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.409796 on 17 degrees of freedom Number of observations: 20 Degrees of Freedom: 17 SSR: 3056.984521 MSE: 179.822619 Root MSE: 13.409796 Multiple R-Squared: 0.911862 Adjusted R-Squared: 0.901493 SUR estimates for 'General.Electric' (equation 2) Model Formula: General.Electric_invest ~ General.Electric_value + General.Elec...

...ficients: Xs Xsx 0.04193 7.30660 Here ys= y*sqrt(W), Xs<- sqrt(W)*cbind(1,x) So we can see weights here for lm means the scale for X and y. But for glm and gls I try > glm1 Call: glm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual Null Deviance: 1049000 Residual Deviance: 28210 AIC: 7414 > glm2 Call: glm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.1955 7.3053 Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual Null Devianc...

Hello, I have a little problem about degree of freedom in R. if you can help me, I will be happy. I used nlme?function to analyze my data and run the linear mixed effects model in R. I did the linear mixed effect analysis in SAS?and SPSS as well. However, R gave?the different degrees of freedom than SAS?and SPSS did. Can you help me to learn what the...

...may have asked this before but I could not find any answers in the web so let me ask a question on lme. When I have a fixed factor of, say, three levels (A, B, C), in which each level has different size (i.e. no. of observations; e.g. A>B>C). When I run an lme model, I get the same degree of freedom for all the contrast t-tests (e.g. AvsB or BvsC). I have tried this to several data sets but the same thing happened. Whatever sample size I have in different levels (in a fixed factor), I get the same degree of freedom for t-tests. Why is this? Is this how mixed-effects model work? Does this mea...

...I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter for poisson family taken to be 1) Null deviance: 1904.7 on 19 degrees of freedom Residual deviance: 1154.3 on 16 degrees of freedom AIC: 1275.4 Number of Fisher Scoring iterations: 4 Wich suggests the existence of STRONG overdispersion, so I tried: > summary(model.qp<-glm(abund~mgmtcat,quasipoisson)) Call: glm(formula = abund ~ mgmt...

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1 80 4123.156 <.000...

Hi , Could someone pls help me about this topic, I dont know how can i extract them from my model!! Thanks, Sophie -- View this message in context: http://r.789695.n4.nabble.com/degrees-of-freedom-does-not-appear-in-the-summary-lmer-tp3741327p3741327.html Sent from the R help mailing list archive at Nabble.com.

I would like to document my findings (with a potential FIX) regarding the issue of calculation of the degrees of freedom with nlme(). The program given at the bottom of this email generates and fit 20 data sets with a mixed-effects LINEAR model, but using the function nlme() instead of lme(). In each case, the correct number of degrees of freedom for the intercept parameter is 12. However, in 7 of the 20 sets, ro...

Hello, I am struggling to understand how denominator degrees of freedom and subsequent significance testing based upon them works in nlme models. I have a data set of 736 measurements (weight), taken within 3 different age groups, on 497 individuals who fall into two morphological catagories (horn types). My model is: Y ~ weight + horn type / age group, random=~1...

Dear all, I am new to R and my question may be trivial to you... I am doing a GLM with binomial errors to compare proportions of species in different categories of seed sizes (4 categories) between 2 sites. In the model summary the residual deviance is much higher than the degree of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even after correcting for overdispersion by using a quasibinomial error structure instead of binomial the residual deviance does not change. Is this a data problem and I cannot use this statistic or is it because I do something wrong with R...

I'm running an ANOVA on some data for respiration in a forest. I am having a problem with my degrees of freedom. For one of my variables I get one fewer degrees of freedom than I should. I have 12 plots and I therefore expected 11 degrees of freedom, but instead I got 10. Any ideas? I have some code and output below: > class(Combined.Plot) [1] "character" > levels(as.factor(Combined.Pl...