search for: denominated

I have a dataset called "results" that looks like this: arrive depart intercept 1 1 1 1 2 1 1 3 1 1 2 2 1 3 2 1 3 3 2 2 2 2 3 2 3 3 3 where arrive is the period of arrival, depart is the period of departure, and intercept

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

Hi all, I came across an issue caused by the Call-Graph Simplify Pass. Here is a a small repro: ``` define double @foo(double %a1, double %a2, double %a3) #0 { entry: %a_mul = fmul double %a1, %a2 %a_cmp = fcmp ogt double %a3, %a_mul br i1 %a_cmp, label %a.then, label %a.end a.then: %a_div = fdiv double %a_mul, %a3 br label %a.end a.end: %a_factor = phi double [ %a_div, %a.then ],

On Wed, Sep 6, 2017 at 4:20 PM, Karol Herbst <karolherbst at gmail.com> wrote: >> In this instance, I think using multiplication is more intuitive >> than '&&', so I'm adding a comparison to zero instead to shut up >> the warning. To further improve readability, I also make the >> error case indented and leave the normal case as the final

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|individual I am modeling

[+current llvm-dev address] On 03/15/2017 09:23 AM, Hal Finkel wrote: > Hi Samuel, > > What are you taking about? ;) > > The only way to get a SIGFPE from a floating-point division by zero is > to modify the floating-point environment to enable those exceptions. > We don't support that (*). In the default (supported) environment, > floating point division is well

Dear R users: I have two set of data, as follow: x<-c(0,0,0.28,0.55,1.2,2,1.95,1.85, 1.6,0.86,0.78,0.6,0.21,0.18) y<-c(0,0,0,0.53,1.34,1.79,2.07,1.88, 1.52,0.92,0.71,0.55,0.32,0.19) i<-1:length(x) I want to sum each (x[i]-y[i])^2/x[i] together, like: >Sum <-sum((x[i]-y[i])^2/x[i]) >Sum [1] NaN Because the denominator shoud not be zero. So I want to overlook those

?s 10:01 de 02/02/2024, Troels Ring escreveu: > Hi friends - I'm plotting a ratio of bicarbonates i ggplot2 and > > ylab(expression(paste(frac("additive BIC","true BIC")))) worked OK - but > now I have been asked to put the chemistry instead - so I wrote > >

Hi friends - I'm plotting a ratio of bicarbonates i ggplot2 and ylab(expression(paste(frac("additive BIC","true BIC")))) worked OK - but now I have been asked to put the chemistry instead - so I wrote ?ylab(expression(paste(frac("additive",HCO[3]^"-","true",HCO[3]^"-")))) - and frac saw that as additive = numerator and HCO3- =

gcc thinks that interpreting a multiplication result as a bool is confusing: drivers/gpu/drm/nouveau/nvkm/subdev/clk/gt215.c: In function 'read_pll': drivers/gpu/drm/nouveau/nvkm/subdev/clk/gt215.c:133:8: error: '*' in boolean context, suggest '&&' instead [-Werror=int-in-bool-context] In this instance, I think using multiplication is more intuitive than

... or if I understand correctly, simply expression(frac(additive ~ HCO[3]^"-", true ~ HCO[3]^"-" ))) Cheers, Bert On Fri, Feb 2, 2024 at 3:06?AM Rui Barradas <ruipbarradas at sapo.pt> wrote: > ?s 10:01 de 02/02/2024, Troels Ring escreveu: > > Hi friends - I'm plotting a ratio of bicarbonates i ggplot2 and > > > >

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively

Every day I get a csv file containing the names of the 64 schools in our county, the number of students sent home ill, and the number of students absent (plus lots of other variables). The file is cumulative since fall of 2009. It is in "long" format: one line per school per day. Each line is also supposed to contain the total number of students enrolled in the school. That number

It’s true, I am working on this. I have committed the initial patch to add constrained intrinsics for the basic FP operations. This has the desired effect of preventing optimizations that would violate strict FP semantics with regard to rounding mode and exception status, but it also prevents many safe optimizations. Later this year I’ll be going through the code base and trying to teach

Users are often surprised and alarmed that the summary of a linear mixed model fit by lmer provides estimates of the fixed-effects parameters, standard errors for these parameters and a t-ratio but no p-values. Similarly the output from anova applied to a single lmer model provides the sequential sums of squares for the terms in the fixed-effects specification and the corresponding numerator

Just giving the survey package a spin... I'm accustomed to stata, and it seems very similar in many respects. One thing is throwing me, however. I've gotten my data in, and specified the design. Looks like the weighting is right (based on published population estimates from these data), but now I'd like to check my "marginal means" for proportions against those that have

hello I need to know the dfn and dfd of my Anova. But in the Anova output there is only "Df". Is this the dfn or the dfd? and how do I get both of it in R? Thanks for any answers -- View this message in context: http://r.789695.n4.nabble.com/df-of-numerator-and-denominator-tp3765526p3765526.html Sent from the R help mailing list archive at Nabble.com.

Dear All I am doing the following: > x <- yacas("3/2") > for (i in 2:400) + x <- yacas(paste(x,"*",x)) > x expression(Inf^1.260864167e+117/Inf^6.304320836e+116) > Eval(x) [1] NaN No luck this way. However, I am successful with > y <- yacas("(3/2)^400") > y expression(7.05507910865533e+190/2.58224987808691e+120) > Eval(y) [1]

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

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