psychmeta (version 2.3.4)

plot_funnel: Create funnel plots

Description

This function creates funnel plots for meta-analyses (plots of effect size versus . Both traditional funnel plots and

Usage

plot_funnel(ma_obj, se_type = c("auto", "mean", "sample"),
  label_es = NULL, conf_level = c(0.95, 0.99),
  conf_linetype = c("dashed", "dotted"), conf_fill = NA,
  conf_alpha = 1, null_effect = NA, null_conf_level = c(0.9, 0.95,
  0.99), null_conf_linetype = c("solid", "dashed", "dotted"),
  null_conf_fill = "black", null_conf_alpha = c(0.1, 0.2, 0.4),
  analyses = "all", match = c("all", "any"), case_sensitive = TRUE,
  show_filtered = FALSE)
plot_cefp(ma_obj, se_type = "sample", label_es = NULL,
  conf_level = NA, conf_linetype = NA, conf_fill = NA,
  conf_alpha = 1, null_effect = NULL, null_conf_level = c(0.9, 0.95,
  0.99), null_conf_linetype = c("solid", "dashed", "dotted"),
  null_conf_fill = "black", null_conf_alpha = c(0, 0.2, 0.4),
  analyses = "all", match = c("all", "any"), case_sensitive = TRUE,
  show_filtered = FALSE)

Arguments

ma_obj

Meta-analysis object.

se_type

Method to calculate standard errors (y-axis). Options are "auto" (default) to use the same method as used to estimate the meta-analysis models, "mean" to calculate SEs using the mean effect size and indivdiual sample sizes, or `"sample"`` to use the SE calculated using the sample effect sizes and sample sizes.

label_es

Label for effect size (x-axis). Defaults to "Correlation (r)" for correlation meta-analyses, "Cohen's d (Hedges's g)" for d value meta-analyses, and "Effect size" for generic meta-analyses.

conf_level

Confidence regions levels to be plotted (default: .95, .99).

conf_linetype

Line types for confidence region boundaries. Length should be either 1 or equal to the length of conf_level.

conf_fill

Colors for confidence regions. Set to NA for transparent. Length should be either 1 or equal to to the length of conf_level.

conf_alpha

Transparency level for confidence regions. Length should be either 1 or equal to to the length of conf_level.

null_effect

Null effect to be plotted for contour-enhanced funnel plots. If NA, not shown. If NULL, set to the null value for the effect size metric (0 for correlations and d values).

null_conf_level

Null-effect confidence regions levels to be plotted (default: .90, .95, .99).

null_conf_linetype

Line types for null-effect confidence region boundaries. Length should be either 1 or equal to the length of null_conf_level.

null_conf_fill

Colors for null-effect confidence regions. Set to NA for transparent. Length should be either 1 or equal to the length of null_conf_level.

null_conf_alpha

Transparency level for null-effect confidence regions. Length should be either 1 or equal to the length of null_conf_level.

analyses

Which analyses to extract? Can be either "all" to extract references for all meta-analyses in the object (default) or a list containing arguments for filter_ma.

match

Should extracted meta-analyses match all (default) or any of the criteria given in analyses?

case_sensitive

Logical scalar that determines whether character values supplied in analyses should be treated as case sensitive (TRUE, default) or not (FALSE).

show_filtered

Logical scalar that determines whether the meta-analysis object given in the output should be the modified input object (FALSE, default) or the filtered object (TRUE).

Value

A list of funnel plots.

Examples

# NOT RUN {
## Correlations
ma_obj <- ma_r(ma_method = "ic", rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi,
               construct_x = x_name, construct_y = y_name, sample_id = sample_id,
               moderators = moderator, data = data_r_meas_multi)
plot_funnel(ma_obj = ma_obj)
plot_funnel(ma_obj = ma_obj, analyses = list(pair_id = 2))
plot_funnel(ma_obj = ma_obj, analyses = list(pair_id = 1, analysis_id = 1), show_filtered = TRUE)

## d values
ma_obj <- ma_d(ma_method = "ic", d = d, n1 = n1, n2 = n2, ryy = ryyi,
               construct_y = construct, sample_id = sample_id,
               data = data_d_meas_multi)
plot_funnel(ma_obj = ma_obj)
plot_funnel(ma_obj = ma_obj, analyses = list(pair_id = 2))
plot_funnel(ma_obj = ma_obj, analyses = list(pair_id = 1, analysis_id = 1), show_filtered = TRUE)
# }