search for: satterthwaite's

Dear R experts, I was wondering if there are any R functions that give the tail area of a sum of chisquare distributions of the type: a_1 X_1 + a_2 X_2 where a_1 and a_2 are constants and X_1 and X_2 are independent chi-square variables with different degrees of freedom. Thanks, Klaus -- "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ...

I'm fairly new to R and am wondering if anybody knows of R code to calculate confidence intervals for parameters (fixed effects and variance components) from mixed effects models based on Sattherthwaite's method? I'm also interested in Satterthwaite-based confidence intervals for linear combinations (mostly sums) of various variance components. [[alternative HTML version deleted]]

This is not a bug report but didn't see another way to ask a question. For the approximate t-test assuming unequal variances, the R docs cite Welch's method for the df of the approximating distribution. I have several methods books, and they all uses Satterthwaite's method. Why does R use Welch's method where can I learn about Welch's method? Sincerely, David Allen -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", &...

...L> Well, you could try the r-help or r-devel mailing lists >> For the approximate t-test assuming unequal variances, >> the R docs cite Welch's method for the df of the >> approximating distribution. I have several methods >> books, and they all uses Satterthwaite's method. Why >> does R use Welch's method where can I learn about Welch's >> method? >> TL> It's the same method. TL> The t-statistic is the difference in means divided by a TL> more-or-less unbiased estimate of its standard...

..."less", "greater"),mu = 0, paired = FALSE, var.equal = FALSE,conf.level = 0.95, ...) var.equal: a logical variable indicating whether to treat the two variances as being equal. If 'TRUE' then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. We are curious why sam in package siggenes do not have var.equal option ? Are there some reason ? sam(data,cl,B=100,balanced=FALSE,mat.samp=NULL,delta=(1:10)/5,med.fdr=TRUE,s 0=NA,alpha.s0=seq(0,1,.05),include.s0=TRUE,p0=NA,lambda.p0=1,vec.lambda...

...ultiple terms or random interactions, the documentation available just doesn't hold up. Here's an example: a split block (strip plot) design evaluated in SAS with PROC MIXED (an excerpt of the model and random statements): model DryMatter = Compacting|Variety / outp = residuals ddfm = satterthwaite; random Rep Rep*Compacting Rep*Variety; Now the fixed part of that model is easy enough in lme: "DryMatter~Compacting*Variety" But I can't find anything that adequately explains how to simply add the random terms to the model, ie "rep + rep:compacting + rep:variety"; any...

...n.r-project.org/doc/FAQ/R-FAQ.html#Why-are-p_002dvalues-not-displayed-when-using-lmer_0028_0029_003f) >From the help page for lmerTest-anova (?lmerTest::anova.merModLmerTest): > Usage: > > ## S4 method for signature 'merModLmerTest' > anova(object, ... , ddf="Satterthwaite", > type=3) > > Arguments: > ... > type: type of hypothesis to be tested. Could be type=3 or type=2 or > type = 1 (The definition comes from SAS theory) So lmerTest-anova by default gives you Type III ('marginal', although Type II is what...

...run your code with "var.equal" I do not see it. > > The explanation is that, since "equal.var" is not a recognised > parameter for t.test(), it has assumed the default value FALSE > for var.equal, and has therefore (since it is a 2-sample test) > adopted the Welch/Satterthwaite procedure: > > var.equal: a logical variable indicating whether to treat > the two variances as being equal. If 'TRUE' then the > pooled variance is used to estimate the variance > otherwise the Welch (or Satterthwaite) approximation > to the degrees of f...

...AQ/R-FAQ.html#Why-are-p_002dvalues-not-displayed-when-using-lmer_0028_0029_003f) > > From the help page for lmerTest-anova (?lmerTest::anova.merModLmerTest): >> Usage: >> >> ## S4 method for signature 'merModLmerTest' >> anova(object, ... , ddf="Satterthwaite", >> type=3) >> >> Arguments: >> > ... >> type: type of hypothesis to be tested. Could be type=3 or type=2 or >> type = 1 (The definition comes from SAS theory) > > > So lmerTest-anova by default gives you Type III ('m...

Dear all, I'm performing a t-test on two normal distributions with identical mean & standard deviation, and repeating this tests a very large number of times to describe an representative p value distribution in a null case. As a part of this, the program bins these values in 10 evenly distributed bins between 0 and 1 and reports the number of observations in each bin. What I have noticed

...dex1* LSI+ (1 + Index1+LSI |ID), data = LSIDATA, control = lmerControl(check.nobs.vs.nRE = "ignore", optimizer ="bobyqa", check.conv.singular = .makeCC(action = "ignore", tol = 1e-4)), REML=TRUE) summary(modelLSI_maineff_RE) Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest'] Formula: SA ~ Index1 * LSI + (1 + Index1 + LSI | ID) Data: LSIDATA Control: lmerControl(check.nobs.vs.nRE = "ignore", optimizer = "bobyqa", check.conv.singular = .makeCC(action = "ignore", tol = 1e-04)) REML criterion at conver...

...dex1* LSI+ (1 + Index1+LSI |ID), data = LSIDATA, control = lmerControl(check.nobs.vs.nRE = "ignore", optimizer ="bobyqa", check.conv.singular = .makeCC(action = "ignore", tol = 1e-4)), REML=TRUE) summary(modelLSI_maineff_RE) Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest'] Formula: SA ~ Index1 * LSI + (1 + Index1 + LSI | ID) Data: LSIDATA Control: lmerControl(check.nobs.vs.nRE = "ignore", optimizer = "bobyqa", check.conv.singular = .makeCC(action = "ignore", tol = 1e-04)) REML criterion at conver...

...esting single- and multiple-contrast hypotheses based on Wald test statistics. The hypothesis tests incorporate small-sample corrections that lead to more accurate rejection rates when the number of clusters is small or the design is unbalanced/leveraged. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm(), plm() (from package 'plm'), gls() and lme() (from 'nlme'), robu() (from 'robume...

...esting single- and multiple-contrast hypotheses based on Wald test statistics. The hypothesis tests incorporate small-sample corrections that lead to more accurate rejection rates when the number of clusters is small or the design is unbalanced/leveraged. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm(), plm() (from package 'plm'), gls() and lme() (from 'nlme'), robu() (from 'robume...

Dear R users: Suppose I have two different response variables y1, y2 that I regress separately on the different explanatory variables, x1 and x2 respectively. I need to compare points on two regression lines. These are the x and y values for each lines. x1<-c(0.5,1.0,2.5,5.0,10.0) y1<-c(204,407,1195,27404313) x2<-c(2.5,5.0,10.0,25.0) y2<-c(440,713,1520,2634) Suppose we need to

Dear all Somebody 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

Dear list, I ran a mixed effect model using R 1.4.1 and SAS 8.0 on the SIMS data found in the SASmixed package and found that the degrees of freedoms for fixed effects are very different. From R, df = n - v -1 where n is total # of observations, v is the # of levels for the grouping factor. From SAS df = v -1. Am I wrong about this or can somebody explain which is correct and why? Thanks a

R 2.3 Windows XP I am trying to understand lme. My aim is to run a random effects regression in which the intercept and jweek are random effects. I am comparing output from SAS PROC MIXED with output from R. The point estimates and the SEs are the same, however the DFs and the p values are different. I am clearly doing something wrong in my R code. I would appreciate any suggestions of how I can

...dims[1]) ? #add model name ? mod.data.ef$model = name ? return(mod.data.ef) } I'm also including the structure of an example model that did converge (but I can I tell from the output?). List of 18 ?$ methTitle?? : chr "Linear mixed model fit by maximum likelihood? \nt-tests use? Satterthwaite approximations to degrees of freedom" ?$ objClass??? : atomic [1:1] lmerMod ? ..- attr(*, "package")= chr "lme4" ?$ devcomp???? :List of 2 ? ..$ cmp : Named num [1:10] 176.85 59.09 95.43 3.84 99.27 ... ? .. ..- attr(*, "names")= chr [1:10] "ldL2" &q...

...SI |ID), data = LSIDATA, control = lmerControl(check.nobs.vs.nRE = > "ignore", optimizer ="bobyqa", check.conv.singular = .makeCC(action = > "ignore", tol = 1e-4)), REML=TRUE) > > summary(modelLSI_maineff_RE) > Linear mixed model fit by REML. t-tests use Satterthwaite's method > ['lmerModLmerTest'] > Formula: SA ~ Index1 * LSI + (1 + Index1 + LSI | ID) > Data: LSIDATA > Control: lmerControl(check.nobs.vs.nRE = "ignore", optimizer = "bobyqa", > check.conv.singular = .makeCC(action = "ignore", tol = 1e-04))...