Print a psych::multilevel.reliability() object for knitr
Source: R/psych_print.R
knit_print.multilevel.RdJust prints the normal output of psych::multilevel.reliability().
Examples
example("mlr", "psych")
#>
#> 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
knitr::knit_print(mg)
#> No viewer found, probably documenting or testing
#>
#>
#>
#> ```
#>
#> 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
#> ```
#>
#>