Description Usage Arguments Author(s) See Also Examples
Control function for multord, a model for multivariate ordinal responses response styles
1 2 3 | ctrl.multord(RS = TRUE, thresholds.acat = c("full", "shift",
"minimal"), XforRS = TRUE, opt.method = c("L-BFGS-B", "nlminb"),
Q = 10, cores = 5, lambda = 0.01)
|
RS |
Logical value indicating whether response style should be modelled. |
thresholds.acat |
Type of parametrization used for thresholds: |
XforRS |
Logical value indicating whether also covariate effects on the
response style should be considered. Only relevant if |
opt.method |
Specifies optimization algorithm used by |
Q |
Number of nodes to be used (per dimension) in Gauss-Hermite-Quadrature. If |
cores |
Number of cores to be used in parallelized computation. |
lambda |
Tuning parameter for internal ridge penalty. It is supposed to be set to a small value to stabilize estimates. |
Gunther Schauberger
gunther.schauberger@tum.de
https://www.researchgate.net/profile/Gunther_Schauberger2
multord
MultOrd-package
plot.MultOrd
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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | ## Not run:
data(tenseness)
## create a small subset of the data to speed up calculations
set.seed(1860)
tenseness <- tenseness[sample(1:nrow(tenseness), 300),]
## scale all metric variables to get comparable parameter estimates
tenseness$Age <- scale(tenseness$Age)
tenseness$Income <- scale(tenseness$Income)
## two formulas, one without and one with explanatory variables (gender and age)
f.tense0 <- as.formula(paste("cbind(",paste(names(tenseness)[1:4],collapse=","),") ~ 1"))
f.tense1 <- as.formula(paste("cbind(",paste(names(tenseness)[1:4],collapse=","),") ~ Gender + Age"))
####
## Adjacent Categories Models
####
## Multivariate adjacent categories model, without response style, without explanatory variables
m.tense0 <- multord(f.tense0, data = tenseness, control = ctrl.multord(RS = FALSE))
m.tense0
## Multivariate adjacent categories model, with response style as a random effect, without explanatory variables
m.tense1 <- multord(f.tense0, data = tenseness)
m.tense1
## Multivariate adjacent categories model, with response style as a random effect,
## without explanatory variables for response style BUT for location
m.tense2 <- multord(f.tense1, data = tenseness, control = ctrl.multord(XforRS = FALSE))
m.tense2
## Multivariate adjacent categories model, with response style as a random effect, with explanatory variables for location AND response style
m.tense3 <- multord(f.tense1, data = tenseness)
m.tense3
plot(m.tense3)
####
## Cumulative Models
####
## Multivariate cumulative model, without response style, without explanatory variables
m.tense0.cumul <- multord(f.tense0, data = tenseness, control = ctrl.multord(RS = FALSE), model = "cumulative")
m.tense0.cumul
## Multivariate cumulative model, with response style as a random effect, without explanatory variables
m.tense1.cumul <- multord(f.tense0, data = tenseness, model = "cumulative")
m.tense1.cumul
## Multivariate cumulative model, with response style as a random effect,
## without explanatory variables for response style BUT for location
m.tense2.cumul <- multord(f.tense1, data = tenseness, control = ctrl.multord(XforRS = FALSE), model = "cumulative")
m.tense2.cumul
## Multivariate cumulative model, with response style as a random effect, with explanatory variables for location AND response style
m.tense3.cumul <- multord(f.tense1, data = tenseness, model = "cumulative")
m.tense3.cumul
plot(m.tense3.cumul)
## End(Not run)
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