psych::multilevel.reliability() object for knitrR/psych_print.R
knit_print.multilevel.RdJust prints the normal output of psych::multilevel.reliability().
knit_print.multilevel(x, ...)
| x | a psych alpha object |
|---|---|
| ... | ignored |
#> #>#> #> #>#> #> #>#> #> mlr> #data from Shrout and Lane, 2012. #> mlr> #> mlr> shrout <- structure(list(Person = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, #> mlr+ 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), Time = c(1L, 1L, #> mlr+ 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, #> mlr+ 4L, 4L), Item1 = c(2L, 3L, 6L, 3L, 7L, 3L, 5L, 6L, 3L, 8L, 4L, #> mlr+ 4L, 7L, 5L, 6L, 1L, 5L, 8L, 8L, 6L), Item2 = c(3L, 4L, 6L, 4L, #> mlr+ 8L, 3L, 7L, 7L, 5L, 8L, 2L, 6L, 8L, 6L, 7L, 3L, 9L, 9L, 7L, 8L #> mlr+ ), Item3 = c(6L, 4L, 5L, 3L, 7L, 4L, 7L, 8L, 9L, 9L, 5L, 7L, #> mlr+ 9L, 7L, 8L, 4L, 7L, 9L, 9L, 6L)), .Names = c("Person", "Time", #> mlr+ "Item1", "Item2", "Item3"), class = "data.frame", row.names = c(NA, #> mlr+ -20L)) #> #> mlr> #make shrout super wide #> mlr> #Xwide <- reshape(shrout,v.names=c("Item1","Item2","Item3"),timevar="Time", #> mlr> #direction="wide",idvar="Person") #> mlr> #add more helpful Names #> mlr> #colnames(Xwide ) <- c("Person",c(paste0("Item",1:3,".T",1),paste0("Item",1:3,".T",2), #> mlr> #paste0("Item",1:3,".T",3),paste0("Item",1:3,".T",4))) #> mlr> #make superwide into normal form (i.e., just return it to the original shrout data #> mlr> #Xlong <-Xlong <- reshape(Xwide,idvar="Person",2:13) #> mlr> #> mlr> #Now use these data for a multilevel repliability study, use the normal wide form output #> mlr> mg <- mlr(shrout,grp="Person",Time="Time",items=3:5) #> #> mlr> #which is the same as #> mlr> #mg <- multilevel.reliability(shrout,grp="Person",Time="Time",items= #> mlr> # c("Item1","Item2","Item3"),plot=TRUE) #> mlr> #to show the lattice plot by subjects, set plot = TRUE #> mlr> #> mlr> #Alternatively for long input (returned in this case from the prior run) #> mlr> mlr(mg$long,grp="id",Time ="time",items="items", values="values",long=TRUE) #> #> Multilevel Generalizability analysis #> Call: mlr(x = mg$long, grp = "id", Time = "time", items = "items", #> long = TRUE, values = "values") #> #> The data had 5 observations taken over 4 time intervals for 3 items. #> #> Alternative estimates of reliability based upon Generalizability theory #> #> RkF = 0.97 Reliability of average of all ratings across all items and times (Fixed time effects) #> R1R = 0.6 Generalizability of a single time point across all items (Random time effects) #> RkR = 0.85 Generalizability of average time points across all items (Random time effects) #> Rc = 0.74 Generalizability of change (fixed time points, fixed items) #> RkRn = 0.85 Generalizability of between person differences averaged over time (time nested within people) #> Rcn = 0.65 Generalizability of within person variations averaged over items (time nested within people) #> #> These reliabilities are derived from the components of variance estimated by ANOVA #> variance Percent #> ID 2.34 0.44 #> Time 0.38 0.07 #> Items 0.61 0.11 #> ID x time 0.92 0.17 #> ID x items 0.12 0.02 #> time x items 0.05 0.01 #> Residual 0.96 0.18 #> Total 5.38 1.00 #> #> The nested components of variance estimated from lme are: #> variance Percent #> id 2.3 0.45 #> id(time) 1.1 0.21 #> residual 1.7 0.34 #> total 5.1 1.00 #> #> To see the ANOVA and alpha by subject, use the short = FALSE option. #> To see the summaries of the ICCs by subject and time, use all=TRUE #> To see specific objects select from the following list: #> ANOVA s.lmer s.lme alpha summary.by.person summary.by.time ICC.by.person ICC.by.time lmer long Call #> mlr> #example of mlArrange #> mlr> #First, add two new columns to shrout and #> mlr> #then convert to long output using mlArrange #> mlr> total <- rowSums(shrout[3:5]) #> #> mlr> caseid <- rep(paste0("ID",1:5),4) #> #> mlr> new.shrout <- cbind(shrout,total=total,case=caseid) #> #> mlr> #now convert to long #> mlr> new.long <- mlArrange(new.shrout,grp="Person",Time="Time",items =3:5,extra=6:7) #> #> mlr> headTail(new.long,6,6) #> id time values items total case #> 1 1 1 2 Item1 11 ID1 #> 2 1 2 3 Item1 10 ID1 #> 3 1 3 4 Item1 11 ID1 #> 4 1 4 1 Item1 8 ID1 #> 5 1 1 3 Item2 11 ID1 #> 6 1 2 3 Item2 10 ID1 #> ... ... ... ... <NA> ... <NA> #> 55 5 3 7 Item2 21 ID5 #> 56 5 4 8 Item2 20 ID5 #> 57 5 1 7 Item3 22 ID5 #> 58 5 2 9 Item3 25 ID5 #> 59 5 3 8 Item3 21 ID5 #> 60 5 4 6 Item3 20 ID5#>#> #> #> #> ``` #> #> Multilevel Generalizability analysis #> Call: mlr(x = shrout, grp = "Person", Time = "Time", items = 3:5) #> #> The data had 5 observations taken over 4 time intervals for 3 items. #> #> Alternative estimates of reliability based upon Generalizability theory #> #> RkF = 0.97 Reliability of average of all ratings across all items and times (Fixed time effects) #> R1R = 0.6 Generalizability of a single time point across all items (Random time effects) #> RkR = 0.85 Generalizability of average time points across all items (Random time effects) #> Rc = 0.74 Generalizability of change (fixed time points, fixed items) #> RkRn = 0.85 Generalizability of between person differences averaged over time (time nested within people) #> Rcn = 0.65 Generalizability of within person variations averaged over items (time nested within people) #> #> These reliabilities are derived from the components of variance estimated by ANOVA #> variance Percent #> ID 2.34 0.44 #> Time 0.38 0.07 #> Items 0.61 0.11 #> ID x time 0.92 0.17 #> ID x items 0.12 0.02 #> time x items 0.05 0.01 #> Residual 0.96 0.18 #> Total 5.38 1.00 #> #> The nested components of variance estimated from lme are: #> variance Percent #> id 2.3 0.45 #> id(time) 1.1 0.21 #> residual 1.7 0.34 #> total 5.1 1.00 #> #> To see the ANOVA and alpha by subject, use the short = FALSE option. #> To see the summaries of the ICCs by subject and time, use all=TRUE #> To see specific objects select from the following list: #> ANOVA s.lmer s.lme alpha summary.by.person summary.by.time ICC.by.person ICC.by.time lmer long Call #> ``` #> #>