Analyzing a simulated data set
library(PP)
library(data.table)First example
suppressWarnings(RNGversion("3.5.0"))
set.seed(1526)
# intercepts
diffpar <- seq(-3.6,3,length=15)
# slope parameters
sl <- round(runif(15,0.5,1.5),2)
la <- round(runif(15,0,0.25),2)
ua <- round(runif(15,0.8,1),2)
awm <- PP::sim_4pl(beta = diffpar, alpha = sl,
lowerA = la, upperA = ua,
theta = rnorm(200))
awm <- as.data.frame(awm)Estimating and using person parameters
out <- PPass(respdf = awm,thres = diffpar,
items="all", mod=c("1PL"),
fitindices=c("lz","lzstar","infit","outfit"))## Estimating: 1pl model ...
## type = wle
## Estimation finished!# create a data.table
outdt <- data.table(out)
outdt[, ID := 1:.N]
outdt[, roundest := factor(round(estimate,8))]
# Compute Percentile Ranks with the Person Parameters
outdtu <- unique(outdt[,su := .N, keyby=roundest], by="roundest")
outdtu[,PR :=cumsum(su)/sum(su) * 100]
outdt <- outdt[outdtu[,list(roundest,PR)],,on="roundest"]
setorder(outdt,ID)
outdt[, c("su") := NULL]
# done
outdt## estimate SE lz lz_unst lzstar infit in_t
## 1: 0.44208265 0.7122795 -1.123225 -0.566720 -1.131614 1.439156 1.137559
## 2: 0.94642726 0.7235927 -0.995213 -0.530884 -1.021577 1.532953 1.332657
## 3: -2.62422313 0.7831439 -2.641876 -0.666216 -3.030420 1.499249 1.243558
## 4: -0.05351983 0.7073232 0.960996 -0.285822 0.961402 0.632956 -0.973855
## 5: -1.04208265 0.7122795 -2.957368 -0.818149 -2.976676 1.926268 2.039333
## ---
## 196: -0.05351983 0.7073232 -0.845575 -0.537251 -0.846244 1.402821 1.059277
## 197: -1.04208265 0.7122795 0.710918 -0.315292 0.713448 0.753587 -0.576558
## 198: 0.44208265 0.7122795 -0.893957 -0.535292 -0.900981 1.486505 1.233420
## 199: 0.44208265 0.7122795 0.252382 -0.378149 0.252183 0.928231 -0.064128
## 200: -0.05351983 0.7073232 1.186818 -0.254394 1.187358 0.521505 -1.392271
## in_chisq in_df in_pv outfit ou_t ou_chisq ou_df ou_pv ID
## 1: 22.501 14 0.069 1.500033 0.799227 22.501 14 0.069 1
## 2: 15.864 14 0.322 1.057589 0.399812 15.864 14 0.322 2
## 3: 283.959 14 0.000 18.930618 3.398825 283.959 14 0.000 3
## 4: 6.083 14 0.964 0.405564 -0.831833 6.083 14 0.964 4
## 5: 76.515 14 0.000 5.100981 2.879117 76.515 14 0.000 5
## ---
## 196: 18.261 14 0.195 1.217393 0.522043 18.261 14 0.195 196
## 197: 7.120 14 0.930 0.474689 -0.515916 7.120 14 0.930 197
## 198: 15.970 14 0.315 1.064664 0.353390 15.970 14 0.315 198
## 199: 10.502 14 0.725 0.700112 -0.126848 10.502 14 0.725 199
## 200: 5.029 14 0.985 0.335272 -1.019910 5.029 14 0.985 200
## roundest PR
## 1: 0.44208265 74.5
## 2: 0.94642726 88.5
## 3: -2.62422313 3.0
## 4: -0.05351983 58.5
## 5: -1.04208265 25.5
## ---
## 196: -0.05351983 58.5
## 197: -1.04208265 25.5
## 198: 0.44208265 74.5
## 199: 0.44208265 74.5
## 200: -0.05351983 58.5Using person fit statistics
Second example
Now there are two groups.
awm <- PP::sim_4pl(beta = diffpar, alpha = sl, lowerA = la,
upperA = ua, theta = c(rnorm(500),rnorm(500,0.5,1.2)))
awm <- as.data.frame(awm)Estimating and using person parameters
out <- PPass(respdf = awm, thres = diffpar,
items="all", mod=c("1PL"),
fitindices= c("lz","lzstar","infit","outfit"))## Estimating: 1pl model ...
## type = wle
## Estimation finished!outdt <- data.table(out)
outdt[, ID := 1:.N]
outdt[, agegroup := rep(c("young","old"),each=500)]
outdt[, roundest := factor(round(estimate,8))]
outdtu <- unique(outdt[,su := .N, keyby=c("agegroup","roundest")], by=c("agegroup","roundest"))[,PR :=cumsum(su)/sum(su) * 100, by=agegroup]
outdt <- outdt[outdtu[,list(agegroup,roundest,PR)],,on=c("agegroup","roundest")]
setorder(outdt,ID)
outdt[, c("su") := NULL]
outdt## estimate SE lz lz_unst lzstar infit in_t
## 1: -0.54648017 0.7073232 0.283532 -0.380108 0.283535 0.914011 -0.101851
## 2: 0.44208265 0.7122795 -1.352493 -0.598149 -1.362246 1.381684 1.018334
## 3: 0.94642724 0.7235928 0.420406 -0.342313 0.422851 0.844185 -0.305596
## 4: 0.94642724 0.7235928 -0.287404 -0.436598 -0.299363 1.114529 0.416902
## 5: 1.46851268 0.7447171 -0.665507 -0.460029 -0.718673 1.286378 0.818367
## ---
## 996: -1.54642724 0.7235928 -1.231150 -0.562313 -1.262315 1.607482 1.477556
## 997: -0.05351983 0.7073232 -3.103790 -0.851537 -3.105801 2.230098 2.517298
## 998: 2.02421867 0.7831539 0.149894 -0.320502 0.122736 1.009137 0.153158
## 999: -2.06851268 0.7447171 -0.175639 -0.397172 -0.202467 1.181492 0.578201
## 1000: 2.02421867 0.7831539 -0.865294 -0.446216 -1.023864 1.290598 0.813772
## in_chisq in_df in_pv outfit ou_t ou_chisq ou_df ou_pv ID
## 1: 10.597 14 0.717 0.706459 -0.210120 10.597 14 0.717 1
## 2: 44.408 14 0.000 2.960520 1.849372 44.408 14 0.000 2
## 3: 9.259 14 0.814 0.617256 -0.085055 9.259 14 0.814 3
## 4: 14.840 14 0.389 0.989352 0.334926 14.840 14 0.389 4
## 5: 16.648 14 0.275 1.109882 0.526179 16.648 14 0.275 5
## ---
## 996: 18.067 14 0.204 1.204439 0.530526 18.067 14 0.204 996
## 997: 39.081 14 0.000 2.605391 1.796104 39.081 14 0.000 997
## 998: 8.016 14 0.888 0.534419 0.276039 8.016 14 0.888 998
## 999: 10.219 14 0.746 0.681256 0.178470 10.219 14 0.746 999
## 1000: 22.247 14 0.074 1.483122 0.830121 22.247 14 0.074 1000
## agegroup roundest PR
## 1: young -0.54648017 40.6
## 2: young 0.44208265 77.0
## 3: young 0.94642724 88.2
## 4: young 0.94642724 88.2
## 5: young 1.46851268 95.6
## ---
## 996: old -1.54642724 11.8
## 997: old -0.05351983 50.4
## 998: old 2.02421867 96.4
## 999: old -2.06851268 4.8
## 1000: old 2.02421867 96.4