woodyplants: Elevated CO_2 and total biomass of woody plants

Description Format Source References Examples

Description

Meta-analysis on effects of elevated CO_2 on total biomass of woody plants

This dataset has been used as an example in Hedges et al. (1999) to describe methods for the meta-analysis of response ratios. The complete dataset with 102 observations and 26 variables is available online as a supplement. Here only a subset of 10 variables is provided and used in the examples.

Format

A data frame with the following columns:

obsno observation number
papno database paper number
treat treatment code
level treatment level
n.elev number of observations in experimental group (elevated CO_2-level)
mean.elev estimated mean in experimental group
sd.elev standard deviation in experimental group
n.amb number of observations in control group (ambient CO_2-level)
mean.amb estimated mean in control group
sd.amb standard deviation in control group

Source

Website http://www.esapubs.org/archive/ecol/E080/008/

References

Hedges LV, Gurevitch J, Curtis PS (1999): The meta-analysis of response ratios in experimental ecology. Ecology, 80, 1150–6

Examples

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data(woodyplants)

# Meta-analysis of response ratios (Hedges et al., 1999)
#
m1 <- metacont(n.elev, mean.elev, sd.elev,
               n.amb, mean.amb, sd.amb,
               data = woodyplants, sm = "ROM",
               studlab = paste(obsno, papno, sep = " / "))
summary(m1, prediction = TRUE)

# Meta-analysis for plants grown with low soil fertility treatment
#
m2 <- update(m1, subset = (treat == "fert" & level == "low"))
summary(m2, prediction = TRUE)

# Meta-analysis for plants grown under low light conditions
#
m3 <- update(m1, subset = (treat == "light" & level == "low"))
summary(m3, prediction = TRUE)

Example output

Loading 'meta' package (version 4.9-5).
Type 'help(meta)' for a brief overview.
Number of studies combined: k = 102

                        ROM           95%-CI     z  p-value
Fixed effect model   1.2322 [1.2191; 1.2455] 38.34        0
Random effects model 1.2880 [1.2422; 1.3355] 13.70 < 0.0001
Prediction interval         [0.9597; 1.7285]               

Quantifying heterogeneity:
tau^2 = 0.0216; H = 2.76 [2.55; 2.99]; I^2 = 86.9% [84.6%; 88.8%]

Test of heterogeneity:
      Q d.f.  p-value
 769.02  101 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
Number of studies combined: k = 15

                        ROM           95%-CI    z  p-value
Fixed effect model   1.1737 [1.1254; 1.2241] 7.46 < 0.0001
Random effects model 1.1437 [1.0382; 1.2599] 2.72   0.0065
Prediction interval         [0.7966; 1.6420]              

Quantifying heterogeneity:
tau^2 = 0.0256; H = 2.13 [1.67; 2.72]; I^2 = 77.9% [64.0%; 86.5%]

Test of heterogeneity:
     Q d.f.  p-value
 63.42   14 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
Number of studies combined: k = 12

                        ROM           95%-CI     z  p-value
Fixed effect model   1.4459 [1.3580; 1.5394] 11.53 < 0.0001
Random effects model 1.5710 [1.3354; 1.8482]  5.45 < 0.0001
Prediction interval         [0.8736; 2.8252]               

Quantifying heterogeneity:
tau^2 = 0.0625; H = 2.43 [1.88; 3.13]; I^2 = 83.0% [71.7%; 89.8%]

Test of heterogeneity:
     Q d.f.  p-value
 64.72   11 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2

meta documentation built on Sept. 14, 2021, 5:14 p.m.