View source: R/plotfunctions.R

plot.eefAnalytics | R Documentation |

Plots different figures based on output from eefAnalytics package.

## S3 method for class 'eefAnalytics' plot(x, group, Conditional = TRUE, ES_Total = TRUE, slope = FALSE, ...)

`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.

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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