Refer to the Rmd source code to see how to adapt this template to your project.
spec <- list()
spec[1:6] <- rpf.grm(factors=2)
## Warning in `[<-`(`*tmp*`, 1:6, value = new("rpf.mdim.grm", spec = c(2, 2, :
## implicit list embedding of S4 objects is deprecated
gen.param <- sapply(spec, rpf.rparam)
colnames(gen.param) <- paste("i", 1:ncol(gen.param), sep="")
gen.param[2,] <- c(0,0,.5,.5,1,1)
resp <- rpf.sample(1000, spec, gen.param)
# hide latent factor that we don't know about
tspec <- list()
tspec[1:length(spec)] <- rpf.grm(factors=1)
## Warning in `[<-`(`*tmp*`, 1:length(spec), value = new("rpf.mdim.grm", spec =
## c(2, : implicit list embedding of S4 objects is deprecated
grp <- list(spec=tspec, param=gen.param[-2,], mean=c(0), cov=diag(1), data=resp)
ChenThissen1997(grp)
## Chen & Thissen (1997) local dependence test
## Magnitudes larger than abs(log(.01))=4.6 are significant at the p=.01 level
## A positive (negative) sign indicates more (less) observed correlation than expected
##
## i1 i2 i3 i4 i5
## i2 -2.65 NA NA NA NA
## i3 1.25 2.67 NA NA NA
## i4 -2.39 2.99 3.11 NA NA
## i5 -2.74 -3.47 -0.86 8.36 NA
## i6 0.94 2.83 2.51 1.12 3.41
(got <- SitemFit(grp))
## Orlando & Thissen (2000) sum-score based item fit test
## Magnitudes larger than abs(log(.01))=4.6 are significant at the p=.01 level
##
## i1 : n = 1000, S-X2( 6) = 6.51, log(p) = -1
## i2 : n = 1000, S-X2( 6) = 6.99, log(p) = -1.13
## i3 : n = 1000, S-X2( 6) = 7.59, log(p) = -1.31
## i4 : n = 1000, S-X2( 6) = 3.13, log(p) = -0.23
## i5 : n = 1000, S-X2( 5) = 2.12, log(p) = -0.18
## i6 : n = 1000, S-X2( 5) = 4.32, log(p) = -0.69
Who can resist plotting these tables?
(got <- sumScoreEAP(grp))
## p a1 se1 cov1
## 0 0.177006547 -1.0022418 0.7508276 0.5637421
## 1 0.288513776 -0.4291543 0.7068011 0.4995677
## 2 0.263058519 0.1376443 0.6572072 0.4319213
## 3 0.157244624 0.6884698 0.6401668 0.4098135
## 4 0.080063208 1.2255142 0.6342668 0.4022944
## 5 0.029790612 1.6791792 0.6386705 0.4079001
## 6 0.004322714 1.9920902 0.6464811 0.4179379
data(science)
spec <- list()
spec[1:25] <- rpf.nrm(outcomes=3, T.c = lower.tri(diag(2),TRUE) * -1)
## Warning in `[<-`(`*tmp*`, 1:25, value = new("rpf.mdim.nrm", spec = c(3, :
## implicit list embedding of S4 objects is deprecated
param <- rbind(a=1, alf1=1, alf2=0,
gam1=sfif$MEASURE + sfsf[sfsf$CATEGORY==1,"Rasch.Andrich.threshold.MEASURE"],
gam2=sfif$MEASURE + sfsf[sfsf$CATEGORY==2,"Rasch.Andrich.threshold.MEASURE"])
colnames(param) <- sfif$NAME
iorder <- match(sfif$NAME, colnames(sfpf))
responses <- sfpf[,iorder]
rownames(responses) <- sfpf$NAME
rpf.1dim.fit(spec, param, responses, sfpf$MEASURE, 2, wh.exact=TRUE)
## Warning in rpf.1dim.fit(spec, param, responses, sfpf$MEASURE, 2, wh.exact =
## TRUE): Excluding item GO TO MUSEUM because outcomes != 3
## Warning in rpf.1dim.fit(spec, param, responses, sfpf$MEASURE, 2, wh.exact =
## TRUE): Excluding response ROSSNER, LAWRENCE F. because it is a minimum or
## maximum
## n infit infit.z outfit outfit.z
## 1 74 0.7334760 -1.93854363 0.6662677 -1.84016154
## 2 74 0.7557765 -1.49109930 0.5601381 -1.43788307
## 3 74 0.6562136 -2.62314065 0.6235471 -2.50977057
## 4 74 0.9885977 -0.02982227 0.9833379 -0.05803811
## 5 74 2.2854600 5.28398081 3.9687036 6.98045705
## 6 74 0.8806001 -0.79728007 0.8212982 -1.02012688
## 7 74 0.9694889 -0.16907311 1.0049137 0.08462788
## 8 74 1.1684407 1.13263699 1.2320360 1.40767658
## 9 74 1.1125813 0.82684751 1.1337003 0.75931289
## 10 74 0.7756357 -1.09399342 0.5617036 -1.14659113
## 11 74 0.7286889 -1.83371616 0.5881417 -1.68055554
## 12 74 0.8493121 -0.62542291 0.7008119 -0.43349100
## 13 74 0.8730059 -0.86823601 0.8058509 -1.13628429
## 14 74 0.7502023 -1.67364559 0.6057915 -1.69066099
## 15 74 1.0934249 0.65700883 1.0512049 0.36542993
## 16 74 0.6632654 -2.59174746 0.6005216 -2.35711937
## 17 74 1.2325992 0.58992208 1.1912748 0.50748517
## 18 74 0.9690502 0.02438538 1.0925458 0.35481861
## 19 74 1.3529043 2.12137594 1.7997411 3.71876933
## 20 74 0.7334508 -1.59075191 0.5470207 -1.51023199
## 21 74 0.8040046 -1.40104331 0.7127619 -1.48373014
## 22 74 2.3647156 5.80477994 4.6517042 8.53849619
## 23 74 0.7907684 -1.42062168 0.6910078 -1.22075027
## 24 74 0.7830659 -1.61643142 0.7215023 -1.66985954
## name
## 1 WATCH BIRDS
## 2 READ BOOKS ON ANIMALS
## 3 READ BOOKS ON PLANTS
## 4 WATCH GRASS CHANGE
## 5 FIND BOTTLES AND CANS
## 6 LOOK UP STRANGE ANIMAL OR PLANT
## 7 WATCH ANIMAL MOVE
## 8 LOOK IN SIDEWALK CRACKS
## 9 LEARN WEED NAMES
## 10 LISTEN TO BIRD SING
## 11 FIND WHERE ANIMAL LIVES
## 12 GROW GARDEN
## 13 LOOK AT PICTURES OF PLANTS
## 14 READ ANIMAL STORIES
## 15 MAKE A MAP
## 16 WATCH WHAT ANIMALS EAT
## 17 GO ON PICNIC
## 18 GO TO ZOO
## 19 WATCH BUGS
## 20 WATCH BIRD MAKE NEST
## 21 FIND OUT WHAT ANIMALS EAT
## 22 WATCH A RAT
## 23 FIND OUT WHAT FLOWERS LIVE ON
## 24 TALK W FRIENDS ABOUT PLANTS
head(rpf.1dim.fit(spec, param, responses, sfpf$MEASURE, 1, wh.exact=TRUE))
## Warning in rpf.1dim.fit(spec, param, responses, sfpf$MEASURE, 1, wh.exact =
## TRUE): Excluding item GO TO MUSEUM because outcomes != 3
## Warning in rpf.1dim.fit(spec, param, responses, sfpf$MEASURE, 1, wh.exact =
## TRUE): Excluding response ROSSNER, LAWRENCE F. because it is a minimum or
## maximum
## n infit infit.z outfit outfit.z name
## 1 24 0.9693598 -0.01898239 0.8675200 -0.2174955 ROSSNER, MARC DANIEL
## 2 24 0.4687608 -2.24283176 0.4341095 -1.3589919 ROSSNER, TOBY G.
## 3 24 0.7377522 -0.97658469 0.6784338 -0.8996851 ROSSNER, MICHAEL T.
## 4 24 0.7940946 -0.75849430 1.3987520 1.1557079 ROSSNER, REBECCA A.
## 5 24 1.6391409 2.12339795 2.5979105 3.4653767 ROSSNER, TR CAT
## 6 24 1.8561200 1.94796584 1.2288375 0.5464035 WRIGHT, BENJAMIN