[R] confint fails in quasibinomial glm: dims do not match
I am unable to calculate confidence intervals for the slope estimate in a
quasibinomial glm using confint(). Below is the output and the package info
for MASS. Thanks in advance!
R 2.9.2
MASS 7.2-48
> confint(glm.palive.0.str)
Waiting for profiling to be done...
Error: dims [product 37] do not match the length of object [74]
> glm.palive.0.str
Call: glm(formula = cbind(alive, red) ~ str, family = quasibinomial,
data = subset(master.palive, vtime == 0))
Coefficients:
(Intercept) strs
1.05 1.01
Degrees of Freedom: 36 Total (i.e. Null); 35 Residual
Null Deviance: 20800
Residual Deviance: 16200AIC: NA
> packageDescription("MASS")
Bundle: VR
Contains: MASS class nnet spatial
Priority: recommended
Version: 7.2-48
Date: 2009-07-29
Depends: R (>= 2.5.0), grDevices, graphics, stats, utils
Suggests: lattice, nlme, survival
Author: S original by Venables & Ripley. R port by Brian Ripley
, following earlier work by Kurt Hornik and Albrecht
Gebhardt.
Maintainer: Brian Ripley
BundleDescription: Functions and datasets to support Venables and Ripley,
'Modern Applied Statistics with S' (4th edition).
License: GPL-2 | GPL-3
URL: http://www.stats.ox.ac.uk/pub/MASS4/
Packaged: 2009-07-31 13:56:57 UTC; ripley
Repository: CRAN
Date/Publication: 2009-08-05 11:20:53
Package: MASS
Description: The main library and the datasets
Title: Main Package of Venables and Ripley's MASS
LazyLoad: yes
LazyData: yes
Built: R 2.9.2; i686-pc-linux-gnu; 2009-08-25 10:52:10 UTC; unix
Cheers,
Chad
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[R] Coda not providing summary on mcmc object
The object is a mcmc sample from lmer. I am using R v2.7.1. Please let me
know what additional information I can provide, hopefully I am just making a
simple mistake. Thanks in advance!
> data(ratdrink, package = 'faraway')
> rd.er <- lmer(wt ~ weeks*treat + (1 | subject), data = ratdrink)
> rd.mc <- mcmcsamp(rd.er, 1)
> library(coda)
Loading required package: lattice
> summary(rd.mc)
Length ClassMode
1 merMCMC S4
> HPDinterval(rd.mc)
Error in UseMethod("HPDinterval") :
no applicable method for "HPDinterval"
> str(rd.mc)
Formal class 'merMCMC' [package "lme4"] with 9 slots
..@ Gp : int [1:2] 0 27
..@ ST : num [1, 1:1] 1.179 0.878 0.864 0.760 0.614 ...
..@ call: language lmer(formula = wt ~ weeks * treat + (1 | subject),
data = ratdrink)
..@ deviance: num [1:1] 964 964 966 966 969 ...
..@ dims: Named int [1:14] 1 135 6 27 1 1 1 2 5 1 ...
.. ..- attr(*, "names")= chr [1:14] "nf" "n" "p" "q" ...
..@ fixef : num [1:6, 1:1] 52.880 26.480 4.780 -0.794 -9.370 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:6] "(Intercept)" "weeks" "treatthiouracil"
"treatthyroxine" ...
.. .. ..$ : NULL
..@ nc : int 1
..@ ranef : num[1:27, 0 ]
..@ sigma : num [1, 1:1] 7.16 6.68 6.60 7.14 7.96 ...
> search()
[1] ".GlobalEnv""package:lme4" "package:Matrix"
"package:coda" "package:lattice" "package:datasets"
"package:grDevices"
[8] "package:graphics" "package:stats" "package:utils"
"package:methods" "Autoloads" "package:base"
>
Cheers,
Chad
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[R] Weighted means in aggregate.table
I need to calculate the weighted means by two factors, the subject id and the
time of measurement. I was hoping to use aggregate.table, but this function
does not allow me to use different weights for each mean that is calculated.
id <- c(rep("a",4),rep("b",4))
time <- c(0,0,60,60,0,0,60,60)
y <- c(10,20,30,40,10,20,30,40)
weight <- c(1,2,3,4,4,3,2,1)
aggregate.table(y,id,time,FUN=weighted.mean,w=weight)
Error in FUN(X[[1L]], ...) : 'x' and 'w' must have the same length
The function works if the data for each time are weighted the same across
subjects:
aggregate.table(y,id,time,FUN=weighted.mean,w=c(1,2))
0 60
a 17 37
b 17 37
Can anyone suggest a solution? Thank you in advance!
Sincerely,
Chad Smith
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