Description Usage Arguments Details Value See Also Examples
Calculates signed root deviance profiles given a glm
or lm
object. The profiled parameters of interest are defined by providing a contrast matrix.
1 2 3 4 5 6 7 8 9 | mcprofile(object, CM, control = mcprofileControl(), grid = NULL)
## S3 method for class 'glm'
mcprofile(object, CM, control = mcprofileControl(),
grid = NULL)
## S3 method for class 'lm'
mcprofile(object, CM, control = mcprofileControl(),
grid = NULL)
|
object |
An object of class |
CM |
A contrast matrix for the definition of parameter linear combinations ( |
control |
A list with control arguments. See |
grid |
A matrix or list with profile support coordinates. Each column of the matrix or slot in a list corresponds to a row in the contrast matrix, each row of the grid matrix or element of a numeric vector in each list slot corresponds to a candidate of the contrast parameter. If NULL (default), a grid is found automatically similar to function |
The profiles are calculates separately for each row of the contrast matrix. The profiles are calculated by constrained IRWLS optimization, implemented in function orglm
, using the quadratic programming algorithm of package quadprog
.
An object of class mcprofile. The slot srdp
contains the profiled signed root deviance statistics. The optpar
slot contains a matrix with profiled parameter estimates.
profile.glm
, glht
, contrMat
, confint.mcprofile
, summary.mcprofile
, solve.QP
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | #######################################
## cell transformation assay example ##
#######################################
str(cta)
## change class of cta$conc into factor
cta$concf <- factor(cta$conc, levels=unique(cta$conc))
ggplot(cta, aes(y=foci, x=concf)) +
geom_boxplot() +
geom_dotplot(binaxis = "y", stackdir = "center", binwidth = 0.2) +
xlab("concentration")
# glm fit assuming a Poisson distribution for foci counts
# parameter estimation on the log link
# removing the intercept
fm <- glm(foci ~ concf-1, data=cta, family=poisson(link="log"))
### Comparing each dose to the control by Dunnett-type comparisons
# Constructing contrast matrix
library(multcomp)
CM <- contrMat(table(cta$concf), type="Dunnett")
# calculating signed root deviance profiles
(dmcp <- mcprofile(fm, CM))
# plot profiles
plot(dmcp)
# confidence intervals
(ci <- confint(dmcp))
plot(ci)
|
Loading required package: ggplot2
'data.frame': 80 obs. of 2 variables:
$ conc: num 0 0 0 0 0 0 0 0 0 0 ...
$ foci: int 0 1 0 0 0 0 0 0 0 2 ...
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS
Attaching package: 'TH.data'
The following object is masked from 'package:MASS':
geyser
Multiple Contrast Profiles
Estimate Std.err
0.01 - 0 0.511 0.730
0.03 - 0 -0.405 0.913
0.1 - 0 1.386 0.645
0.3 - 0 2.159 0.610
1 - 0 2.730 0.596
3 - 0 2.813 0.594
10 - 0 2.979 0.592
mcprofile - Confidence Intervals
level: 0.95
adjustment: single-step
Estimate lower upper
0.01 - 0 0.511 -1.2503 2.56
0.03 - 0 -0.405 -3.0542 1.88
0.1 - 0 1.386 -0.0182 3.31
0.3 - 0 2.159 0.8953 4.04
1 - 0 2.730 1.5187 4.59
3 - 0 2.813 1.6076 4.67
10 - 0 2.979 1.7828 4.83
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