Description Usage Arguments Details Value Examples
View source: R/plotfunctions.R
Plots different figures based on output from eefAnalytics package.
1 2 |
x |
an output object from the eefAnalytics package. |
group |
a string/scalar value indicating which intervention to plot. This must be one of the values of intervention variable excluding the control group. For a two arm trial, the maximum number of values to consider is 1 and 2 for three arm trial. |
Conditional |
a logical value to indicate whether to plot the conditional effect size. The default is Conditional=TRUE, otherwise Conditional=FALSE should be specified for plot based on the unconditional effect size. Conditional variance is total or residual variance from a multilevel model with fixed effects, whilst unconditional variance is total variance or residual variance from a multilevel model with only intercept as fixed effect. |
ES_Total |
A logical value indicating whether to plot the effect size based on total variance or within school variance. The default is ES_Total=TRUE, to plot the effect size using total variance. ES_Total=FALSE should be specified for the effect size based on within school or residuals variance. |
slope |
A logical value indicating whether to return the plot of random intercept (default is slope=FALSE). return other school-by-intervention interaction random slope (s) is slope=TRUE. This argument is suitable only for mstBayes and mstFREQ functions. |
... |
arguments passed to |
Plot produces a graphical visualisation depending on which model is fitted:
For srtFREQ()
, plot can only be used when nBoot
or nPerm
is specified to visualise the distribution of bootstrapped or permutated values.
For crtFREQ()
or mstFREQ()
, plot shows the distribution of random intercepts when group=NULL
.
It produces histogram of permutated or bootstrapped values when group
is specified and either nBoot
or nPerm
is also specified.
Returns relevant plots for each model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | if(interactive()){
#### read data
data(mstData)
data(crtData)
###############
##### SRT #####
###############
##### Bootstrapped
outputSRTBoot <- srtFREQ(Posttest~ Intervention + Prettest,
intervention = "Intervention",nBoot=1000, data = mstData)
plot(outputSRTBoot,group=1)
##### Permutation
outputSRTPerm <- srtFREQ(Posttest~ Intervention + Prettest,
intervention = "Intervention",nPerm=1000, data = mstData)
plot(outputSRTPerm,group=1)
###############
##### MST #####
###############
#### Random intercepts
outputMST <- mstFREQ(Posttest~ Intervention + Prettest,
random = "School", intervention = "Intervention", data = mstData)
plot(outputMST)
#### Bootstrapped
outputMSTBoot <- mstFREQ(Posttest~ Intervention + Prettest,
random = "School", intervention = "Intervention",
nBoot = 1000, data = mstData)
plot(outputMSTBoot)
plot(outputMSTBoot,group=1)
#### Permutation
outputMSTPerm <- mstFREQ(Posttest~ Intervention + Prettest,
random = "School", intervention = "Intervention",
nPerm = 1000, data = mstData)
plot(outputMSTPerm)
plot(outputMSTPerm,group=1)
###############
##### CRT #####
###############
#### Random intercepts
outputCRT <- crtFREQ(Posttest~ Intervention + Prettest, random = "School",
intervention = "Intervention", data = crtData)
plot(outputCRT)
## Bootstrapped
outputCRTBoot <- crtFREQ(Posttest~ Intervention + Prettest, random = "School",
intervention = "Intervention", nBoot = 1000, data = crtData)
plot(outputCRTBoot,group=1)
##Permutation
outputCRTPerm <- crtFREQ(Posttest~ Intervention + Prettest, random = "School",
intervention = "Intervention", nPerm = 1000, data = crtData)
plot(outputCRTPerm,group=1)
}
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