Estimate Person Paramters and calculate Person Fit in one step to gain resonse pattern assessment. Submit a data.frame which contains item responses, or an fitted model (Rasch Model and Partial Credit Model are supported) of the eRm package.
PPass(...) # S3 method for default PPass( respdf, items = "all", mod = c("1PL", "2PL", "3PL", "4PL", "PCM", "GPCM", "MIXED"), fitindices = c("lz", "lzstar", "infit", "outfit"), ... ) # S3 method for Rm PPass(RMobj, fitindices = c("lz", "lzstar", "infit", "outfit"), ...)
| respdf | A data.frame which contains the items, and perhaps other informations. Each row is a person related resonse patter. Each column denotes a variable. |
|---|---|
| items | A numeric (integer) vector which indicates the positions of the items in the data.frame ( |
| mod | Choose your data generating model. This argument switches between the three person parameter estimating functions |
| fitindices | A character vector which denotes the fit indices to compute. |
| RMobj | A fitted Rasch Model ( |
| ... | Submit arguments to the underlying functions: |
The original data.frame and
The Person Parameter estimates incl. Standard Errors (2 columns)
Person Fit Indices you chose (1 or more)
PPass fuses Person Parameter estimation and Person Fit computation into a single function.
Manuel Reif, Jan Steinfeld
library(eRm) ### real data ########## data(pp_amt) d <- pp_amt$daten_amt rd_res <- PPass(respdf = d, items = 8:ncol(d), mod="1PL", thres = pp_amt$betas[,2], fitindices = "lz")#> Estimating: 1pl model ... #> type = wle #> Estimation finished!#> estimate SE lz lz_unst #> 1 1.4098572 0.4177670 -0.631024 -0.613445 #> 2 0.3515596 0.3728684 1.229632 -0.627897 #> 3 1.0745004 0.3741313 1.097723 -0.627596 #> 4 0.3204585 0.6900186 0.981576 -0.494311 #> 5 1.3879159 0.4643975 -0.203399 -0.600039 #> 6 0.9457363 0.4184999 0.573956 -0.615722## ========== RM - eRm my_data <- eRm::sim.rasch(200, 12) my_rm <- eRm::RM(my_data) res_pp1 <- PPass(my_rm)#> Estimating: 1pl model ... #> type = wle #> Estimation finished!## ========== PCM - eRm set.seed(2751) THRES <- matrix(c(-2,-1.23,1.11,3.48,1 ,2,-1,-0.2,0.5,1.3,-0.8,1.5),nrow=2) THRES <- rbind(THRES,c(-0.2,NA,NA,NA,NA,NA)) sl <- rep(1,6) THRESx <- rbind(0,THRES) THETA <- rnorm(200) simdat_gpcm <- sim_gpcm(thres = THRESx,alpha = sl,theta = THETA) my_pcm <- eRm::PCM(simdat_gpcm) res_pp2 <- PPass(my_pcm)#> Estimating: GPCM ... #> type = wle #> Estimation finished!#> Warning: Only 'infit and outfit' are currently supported. The calculation is executed with infit outfit## ========== 1PL model set.seed(1337) # intercepts diffpar <- seq(-3,3,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) # response matrix awm <- matrix(sample(0:1,100*15,replace=TRUE),ncol=15) awm <- as.data.frame(awm) # estimate person parameter # estimate person parameter and person fit out <- PPass(respdf = awm,thres = diffpar, items="all", mod=c("1PL"), fitindices= c("lz","lzstar","infit","outfit"))#> Estimating: 1pl model ... #> type = wle #> Estimation finished!#> estimate SE lz lz_unst lzstar infit in_t in_chisq #> 1 0.2283002 0.6819765 -3.083081 -0.870676 -3.086058 2.020659 2.305126 47.064 #> 2 0.2283002 0.6819765 -5.785447 -1.242104 -5.790820 2.966794 3.732302 70.659 #> 3 -0.6878210 0.6874864 -5.900059 -1.239635 -5.953959 2.886533 3.635684 80.964 #> 4 -2.1690639 0.7630686 -6.072526 -1.122556 -7.131986 2.506919 2.974178 227.247 #> 5 -1.1569232 0.6998950 -2.673938 -0.777026 -2.759479 1.761801 1.850323 60.601 #> 6 0.2283002 0.6819765 -6.824819 -1.384962 -6.831113 3.295854 4.156107 85.261 #> in_df in_pv outfit ou_t ou_chisq ou_df ou_pv #> 1 14 0 3.137599 2.433293 47.064 14 0 #> 2 14 0 4.710578 3.451529 70.659 14 0 #> 3 14 0 5.397579 3.423889 80.964 14 0 #> 4 14 0 15.149777 3.515269 227.247 14 0 #> 5 14 0 4.040039 2.329902 60.601 14 0 #> 6 14 0 5.684096 3.970965 85.261 14 0