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Plot to compare estimates from original and follow-up studies

Source: R/meta_plots.R
replicate.plot.Rd

Generates a basic plot using ggplot2 to visualize the estimates from the original and follow-up studies.

Usage

replicate.plot(
  result,
  focus = c("Both", "Difference", "Average"),
  reference_line = NULL,
  diamond_height = 0.2,
  difference_axis_ticks = 5,
  ggtheme = ggplot2::theme_classic()
)

Arguments

result

  • a result matrix from any of the replicate functions in vcmeta

focus

  • Optional specification of the focus of the plot; defaults to 'Both'

  • Both - plots each estimate, differencence, and average

  • Difference - plot each estimate and difference between them

  • Average - plot each estimate and the average effect size

reference_line

  • Optional x-value for a reference line. Only applies if focus is 'Difference' or 'Both'. Defaults to NULL, in which case a reference line is not drawn.

diamond_height

  • Optional height of the diamond representing average effect size. Only applies if focus is 'Average' or 'Both'. Defaults to 0.2

difference_axis_ticks

  • Optional requested number of ticks on the difference axis. Only applies if focus is 'Difference' or 'Both'. Defaults to 5.

ggtheme

  • optional ggplot2 theme object; defaults to theme_classic()

Value

Returns a ggplot object. If stored, can be further customized via the ggplot API

Examples

   
# Compare Damisch et al., 2010 to Calin-Jageman & Caldwell 2014
# Damisch et al., 2010, Exp 1, German participants made 10 mini-golf putts.
# Half were told they had a 'lucky' golf ball; half were not.
# Found a large but uncertain improvement in shots made in the luck condition
# Calin-Jageman & Caldwell, 2014, Exp 1, was a pre-registered replication with
# input from Damisch, though with English-speaking participants.
#
# Here we compare the effect sizes, in original units, for the two studies.
# Use the replicate.mean2 function because the design is a 2-group design.

library(ggplot2)
damisch_v_calinjageman_raw <- replicate.mean2(
  alpha = 0.05,
  m11 = 6.42,
  m12 = 4.75,
  sd11 = 1.88,
  sd12 = 2.15,
  n11 = 14,
  n12 = 14,
  m21 = 4.73,
  m22 = 4.62,
  sd21 = 1.958,
  sd22 = 2.12,
  n21 = 66,
  n22 = 58
)

# View the comparison:
damisch_v_calinjageman_raw
#>                       Estimate        SE         t          p          LL
#> Original:                 1.67 0.7633058 2.1878519 0.03803154  0.09964253
#> Follow-up:                0.11 0.3682078 0.2987443 0.76566558 -0.61922358
#> Original - Follow-up:     1.56 0.8474743 1.8407638 0.07340024  0.13153488
#> Average:                  0.89 0.4237372 2.1003587 0.04232406  0.03245567
#>                              UL        df
#> Original:             3.2403575  25.54543
#> Follow-up:            0.8392236 116.88772
#> Original - Follow-up: 2.9884651  38.36331
#> Average:              1.7475443  38.36331


# Now plot the comparison, focusing on the difference
replicate.plot(damisch_v_calinjageman_raw, focus = "Difference")


# Plot the comparison, focusing on the average
replicate.plot(damisch_v_calinjageman_raw, 
  focus = "Average", 
  reference_line = 0,
  diamond_height = 0.1
)



# Plot the comparison with both difference and average.
# In this case, store the plot for manipulation
myplot <- replicate.plot(
  damisch_v_calinjageman_raw,
  focus = "Both",
  reference_line = 0
)

# View the stored plot
myplot


# Change x-labels and study labels
myplot <- myplot + xlab("Difference in Putts Made, Lucky - Control")
myplot <- myplot + scale_y_discrete(
    labels = c(
      "Average",
      "Difference",
      "Calin-Jageman & Caldwell, 2014",
      "Damisch et al., 2010"
      )
  )
  
# View the updated plot
myplot