| Type: | Package |
| Title: | Meta-CART: A Flexible Approach to Identify Moderators in Meta-Analysis |
| Version: | 3.0.0 |
| Encoding: | UTF-8 |
| Author: | Xinru Li [aut], Elise Dusseldorp [aut, cph], Kaihua Liu [ctb] (supported with the plot function), Juan Claramunt [aut, cre], Jacqueline Meulman [ctb] |
| Maintainer: | Juan Claramunt <j.claramunt.gonzalez@fsw.leidenuniv.nl> |
| Description: | Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017) <doi:10.1111/bmsp.12088>. |
| LazyData: | TRUE |
| Depends: | R (≥ 3.0.2), stats, ggplot2, gridExtra, methods, rpart |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | Rcpp (≥ 1.0.0) |
| LinkingTo: | Rcpp |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2025-04-17 08:31:18 UTC; prueba |
| Repository: | CRAN |
| Date/Publication: | 2025-04-17 08:50:02 UTC |
R package for meta-CART
Description
In meta-analysis, heterogeneity often exists between studies. To understand this heterogeneity, researchers search for study characteristics (i.e., potential moderators) that may account for the variance in study effect sizes. When multiple potential moderators are available (e.g., intervention characteristics), traditional meta-analysis methods often lack sufficient power to investigate interaction effects between moderators, especially high-order interactions. To solve this problem, meta-CART was proposed by integrating Classification and Regression Trees (CART) into meta-analysis. The method idenfities the interaction effects between influential moderators, partitions the studies into more homogeneous subgroups, and estimates summary effect size in each subgroup. The fixed effect or random effects assumption can be consistently taken into account in both tree-growing process and subgroup analysis.
Details
This method is suitable for identifying interaction effects between dichotomous,
ordinal, continuous, and nominal moderators.
The output of a REmrt object shows meta-CART analysis results based on the random effects model.
And the output of a FEmrt object shows meta-CART analysis results based on the fixed effect model.
The two objects display results for subgroup analysis including the Q-statistic and estimates for the subgroup effect sizes.
Furthermore, the predict functions predict.REmrt and predict.FEmrt can be used to predict the effect size given the moderators.
The plot functions plot.REmrt and plot.FEmrt show the interaction effects between identified moderators.
The core functions of the package are FEmrt and REmrt.
Author(s)
Maintainer: Xinru Li <x.li@math.leidenuniv.nl>; Contributors: Elise Dusseldorp, Kaihua Liu (supported with the plot function), Juan Claramunt Gonzalez, and Jacqueline Meulman.
References
Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of techniques that effectively change health behavior: Evidence from meta-cart analysis. Health Psychology, 33(12), 1530-1540. doi: 10.1037/hea0000018.
Li, X., Dusseldorp, E., & Meulman, J. J. (2017). Meta-CART: A tool to identify interactions between moderators in meta-analysis. British Journal of Mathematical and Statistical Psychology, 70(1), 118-136. doi: 10.1111/bmsp.12088.
Li, X., Dusseldorp, E., Claramunt Gonzalez, J., Su, X., van Megen, J., & Meulman, J. J. (2025). Enhanced tree-based subgroup identification in meta-analysis. To be published.
Therneau, T., Atkinson, B., & Ripley, B. (2014) rpart: Recursive partitioning and regression trees. R package version, 4-1.
The articles of our own work can be found at https://elisedusseldorp.nl/
See Also
FEmrt, REmrt, summary.FEmrt,summary.REmrt,
plot.FEmrt,plot.REmrt,predict.FEmrt,predict.REmrt
Compute the subgroup effect sizes
Description
Compute the subgroup effect sizes
Usage
.ComputeY(x1, y, vi, tau2)
Arguments
x1 |
the node labels for each study |
y |
the effect size |
vi |
the sampling variance |
tau2 |
the residual heterogeneity |
Predict effect size for the test set
Description
Predict effect size for the test set
Usage
.PredY(x1, x2)
Arguments
x1 |
the list of subgroup means |
x2 |
predicted subgroup membership for the test set |
Replace missing values by the overall weighted mean
Description
Replace missing values by the overall weighted mean
Usage
.ReplaceNA(x1, x2, y, vi, tau2)
Arguments
x1 |
the two-column matrix of the indices of missing values |
x2 |
the matrix of predicted y with missing values |
y |
the effect size |
vi |
the sampling variance |
tau2 |
the residual heterogeneity |
Compute re Q for different values of tau2
Description
Compute re Q for different values of tau2
Usage
.compute_re_Q_(x1, x2, x3, x4, xuni, x5, x6)
Arguments
x1 |
the effect size g in the unsplit leaves |
x2 |
the sampling variance vi in the unsplit leaves |
x3 |
the labels of nodes in the unsplit leaves |
x4 |
tau2 |
xuni |
the unique labels in the unsplit leaves |
x5 |
the effect size g in the parent leaf |
x6 |
the sampling variance vi in the parent leaf |
Compute tau2
Description
Compute tau2
Usage
.compute_tau_(x1, x2, x3, xuni, x4, x5)
Arguments
x1 |
the effect size g in the unsplit leaves |
x2 |
the sampling variance vi in the unsplit leaves |
x3 |
the lable of the unsplit leaves |
xuni |
the unique labels in the unsplit leaves |
x4 |
the sorted effect size in the parent leaf |
x5 |
the sorted sampling variance in the parent leaf |
Partition the test set based on a trained tree
Description
Partition the test set based on a trained tree
Usage
.partition(x1, x2, x3, x4, x5, x6)
Arguments
x1 |
the tree component of the REmrt object |
x2 |
the moderators in the test set |
x3 |
indicates whether a moderator is numeric or not |
x4 |
the index vector of the spliting moderators |
x5 |
the list of split points |
x6 |
the moderators in the training set |
This functions performs bootstrap to compute the confidence intervals for the subgroup effect size estimates. This function is only applicable to Random effects metaregression trees with 2 terminal nodes or more.
Description
This functions performs bootstrap to compute the confidence intervals for the subgroup effect size estimates. This function is only applicable to Random effects metaregression trees with 2 terminal nodes or more.
Usage
BootCI(Metatree, nboot = 50)
Arguments
Metatree |
fitted tree of class |
nboot |
number of bootstrap samples. |
Value
tree containing the input tree, the Bootstrap estimates for the effect sizes and standard errors, Bootstrap estimate for tau2, and the Bootstrap bias correction.
Examples
set.seed(12345)
data(dat.BCT2009)
library(Rcpp)
REtree <- REmrt(g ~ T1 + T2+ T4 +T25, vi = vi, data = dat.BCT2009, c.pruning = 0)
BootTree<-BootCI(REtree, nboot = 3)
summary(BootTree)
A function to draw the confidence interval as a diamond
Description
A function to draw the confidence interval as a diamond
Usage
CI_draw(plotobj, x, y, a = 1, b = 1)
Arguments
plotobj |
the obj to be plot |
x |
the x coordinate of the center to be plotted |
y |
the y coordinate of the center to be plotted |
a |
the distance between the center to the vertext on x-axis of the diamond |
b |
the distance between the center to the vertext on x-axis of the diamond |
Value
a ggplot object
Fixed effect meta-tree
Description
A function to fit fixed effect meta-trees to meta-analytic data. The model is assuming a fixed effect within subgroups and between subgroups. The tree growing process is equivalent to the approach described in Li et al. (2017) using fixed effect weights in the function rpart() developed by Therneau, Atkinson & Ripley (2014).
Usage
FEmrt(
formula,
data,
vi,
subset,
c.pruning = 0.5,
perm = NULL,
sss = FALSE,
lookahead = FALSE,
cp = 1e-04,
maxdepth = 10L,
minsplit = 6,
xval = 10,
minbucket = 3,
a = 50,
alpha.endcut = 0.02,
multi.start = T,
n.starts = 3,
...
)
Arguments
formula |
A formula, with an outcome variable (usually the effect size) and the potential moderator variables but no interaction terms. |
data |
A data frame of a meta-analytic data set, including the study effect sizes, sampling variance, and the potential moderators. |
vi |
sampling variance of the effect size. |
subset |
optional expression that selects only a subset of the rows of the data. |
c.pruning |
A non-negative scalar.The pruning parameter to prune the initial tree by the "c*standard-error" rule. |
perm |
the number of data sets to permute for the permutation test. If set as NULL, then perumuation test will not be performed |
sss |
boolean indicating whether the SSS strategy is used or not. |
lookahead |
a boolean argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
cp |
complexity parameter as in rpart. |
maxdepth |
set the maximum depth of any node of the final tree, with the root node counted as depth 0 |
minsplit |
the minimum number of observations that must exist in a node in order for a split to be attempted. |
xval |
number of cross-validations. |
minbucket |
the minimum number of observations in any terminal <leaf> node. |
a |
parameter used in the sss to determine the slope of the logistic function that replaces the indicator function. |
alpha.endcut |
parameter used in the splitting algorithm to avoid the endcut preference problem. |
multi.start |
boolean indicating whether multiple starts must be used |
n.starts |
number of multiple starts |
... |
Additional arguments passed to the tree growing algorithm based on rpart. |
Value
If (a) moderator effect(s) is(are) detected, the function will return a FEmrt object including the following components:
tree: The pruned tree that represents the moderator effect(s) and interaction effect(s) between them.
n: The number of the studies in each subgroup
Qb: The between-subgroups Q-statistic
df: The degree of freedoms of the between-subgroups Q test
pval.Qb: The p-value of the between-subgroups Q test
Qw: The within-subgroup Q-statistic in each subgroup
g: The subgroup summary effect size, based on Hedges'g
se: The standard error of the subgroup summary effect size
zval: The test statistic of the subgroup summary effect size
pval: The p-value for the test statistics of the subgroup summary effect size
ci.lb: The lower bound of the confidence interval
ci.ub: The upper bound of the confidence interval
call: The matched call
If no moderator effect is detected, the function will return a FEmrt object including the following components:
n: The total number of the studies
Q: The Q-statistic of the heterogeneity test
df: The degrees of freedom of the heterogeneity test
pval.Q: The p-value of the heterogeneity test
g: The summary effect size for all studies
se: The standard error of the summary effect size
zval: The test statistic of the summary effect size
pval: The p-value of the test statistic of the summary effect size
ci.lb: The lower bound of the confidence interval for the summary effect size
ci.ub: The upper bound of the confidence interval for the summary effect size
formula: The formula provided as input.
call: The matched call
References
Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of techniques that effectively change health behavior: Evidence from meta-cart analysis. Health Psychology, 33(12), 1530-1540. doi: 10.1037/hea0000018.
Li, X., Dusseldorp, E., & Meulman, J. J. (2017). Meta-CART: A tool to identify interactions between moderators in meta-analysis. British Journal of Mathematical and Statistical Psychology, 70(1), 118-136. doi: 10.1111/bmsp.12088.
Therneau, T., Atkinson, B., & Ripley, B. (2014) rpart: Recursive partitioning and regression trees. R package version, 4-1.
See Also
summary.FEmrt, plot.FEmrt, rpart,rpart.control
Examples
data(dat.BCT2009)
library(Rcpp)
FEtree <- FEmrt(g ~ T1 + T2+ T4 + T25, vi = vi, data = dat.BCT2009, c.pruning = 0.5)
print(FEtree)
summary(FEtree)
#plot(FEtree)
A function returns the Q-between from the tree with given size
Description
A function returns the Q-between from the tree with given size
Usage
Q_selected_size_GS(tree, nsplit, mods, minbucket, minsplit, ...)
Arguments
tree |
A initial tree fitted by rpart, needs to an rpart object. |
nsplit |
the required number of splits |
mods |
the moderators found by the tree. |
minbucket |
the minimum number of observations in any terminal <leaf> node. |
minsplit |
the minimum number of observations that must exist in a node in order for a split to be attempted. |
... |
Additional arguments passed to prune.rpart(). |
Value
The pruned tree
Random effects meta-tree
Description
A function to fit a random effects meta-tree
Usage
REmrt(
formula,
data,
vi,
c.pruning = 1,
maxL = 5,
minsplit = 6,
cp = 1e-05,
minbucket = 3,
xval = 10,
lookahead = FALSE,
sss = FALSE,
alpha.endcut = 0.02,
a = 50,
multi.start = TRUE,
n.starts = 3,
perm = NULL,
...
)
Arguments
formula |
A formula, with a response variable (usually the effect size) and the potential moderator variables but no interaction terms. |
data |
A data frame of a meta-analytic data set, including the study effect sizes, sampling variance, and the potential moderators. |
vi |
sampling variance of the effect size. |
c.pruning |
A non-negative scalar.The pruning parameter to prune the initial tree by the "c*standard-error" rule. |
maxL |
the maximum number of splits |
minsplit |
the minimum number of studies in a parent node before splitting |
cp |
the stopping rule for the decrease of between-subgroups Q. Any split that does not decrease the between-subgroups Q is not attempted. |
minbucket |
the minimum number of the studies in a terminal node |
xval |
the number of folds to perform the cross-validation |
lookahead |
an argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
sss |
boolean parameter indicating whether the SSS algorithm must be used. |
alpha.endcut |
parameter used in the splitting algorithm to avoid the endcut preference problem. |
a |
parameter used in the sss to determine the slope of the logistic function that replaces the indicator function. |
multi.start |
boolean indicating whether multiple starts must be used |
n.starts |
number of multiple starts |
perm |
the number of permuted data sets, if NULL then no permutation test is performed |
... |
Additional arguments to be passed. |
Value
If (a) moderator effect(s) is(are) detected, the function will return a list including the following objects:
tree: A data frame that represents the tree, with the Q-between and the residual heterogeneity (tau^2) after each split.
n: The number of the studies in each subgroup
moderators: the names of identified moderators
Qb: The between-subgroups Q-statistic
tau2: The estimate of the residual heterogeneity
df: The degrees of freedom of the between-subgroups Q test
pval.Qb: The p-value of the between-subgroups Q test
g: The subgroup summary effect size, based on Hedges'g
se: The standard error of subgroup summary effect size
zval: The test statistic of the subgroup summary effect size
pval: The p-value of the test statistic of the subgroup summary effect size
ci.lb: The lower bound of the confidence interval
ci.ub: The upper bound of the confidence interval
call: The matched call
cptable: The cross-validation table
data: the data set subgrouped by the fitted tree
If no moderator effect is detected, the function will return a list including the following objects:
n: The total number of the studies
Q: The Q-statistics for the heterogeneity test
df: The degree of freedoms of the heterogeneity test
pval.Q: The p-value for the heterogeneity test
g: The summary effect size for all studies (i.e., the overall effect size)
se: The standard error of the summary effect size
zval: The test statistic of the summary effect size
pval: The p-value for the test statistic of the summary effect size
ci.lb: The lower bound of the confidence interval for the summary effect size
ci.ub: The upper bound of the confidence interval for the summary effect size
formula: The formula provided as input.
call: The matched call
initial.tree: The initial tree obtained before pruning.
See Also
Examples
#set.seed is required to obtain the same tree
#due to the use of a probabilistic algorithm for pruning
set.seed(12345)
data(dat.BCT2009)
library(Rcpp)
REtree <- REmrt(g ~ T1 + T2+ T4 +T25, vi = vi, data = dat.BCT2009, c.pruning = 0)
summary(REtree)
plot(REtree)
#You can obtain the non-pruned tree by calling the initial.tree output argument
REtree$initial.tree
A function to fit the tree with look-ahead option
Description
A function to fit the tree with look-ahead option
Usage
REmrt_GS_(mf, maxL, minbucket, minsplit, cp, lookahead)
Arguments
mf |
the data.frame to grow the tree |
maxL |
the maximum number of splits |
minbucket |
the minimum number of the studies in a terminal node |
minsplit |
the minimal number of studies in a parent node to be split |
cp |
the stopping rule for decrease of between-subgroups Q. Any split that does not decrease the between-subgroups Q is not attempted. |
lookahead |
an argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
Value
a list including a tree, the split points, the data, and the nodes after each split
A function to fit the tree with look-ahead option
Description
A function to fit the tree with look-ahead option
Usage
REmrt_SSS(
mf,
maxL,
minbucket,
minsplit,
cp,
lookahead,
alpha.endcut = 0.02,
a = 50,
multi.start = T,
n.starts = 3
)
Arguments
mf |
the data.frame to grow the tree |
maxL |
the maximum number of splits |
minbucket |
the minimum number of the studies in a terminal node |
minsplit |
the minimal number of studies in a parent node to be split |
cp |
the stopping rule for decrease of between-subgroups Q. Any split that does not decrease the between-subgroups Q is not attempted. |
lookahead |
an argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
alpha.endcut |
parameter used in the splitting algorithm to avoid the endcut preference problem. |
a |
parameter used in the sss to determine the slope of the logistic function that replaces the indicator function. |
multi.start |
boolean indicating whether multiple starts must be used |
n.starts |
number of multiple starts |
Value
a list including a tree, the split points, the data, and the nodes after each split
A simulated meta-analytic data set
Description
Data simuated from a true model with a three-way interaction between three moderators: m1, m2 and m3. If the values of the three moderators are all "B"s the true effect size will be 0.80. Otherwise, the true effect size is 0.
Usage
data(SimData)
Format
A data frame of 120 studies with 5 moderators
efk: The effect size of each study expressed as Hedges' g
vark: The sampling variance of the effect size
m1 to m5: Five randomly generated moderators. m1 and m2 have two levels (A and B), whereas m3, m4 and m5 have three levels (A, B and C)
A function to compute cross-validation errors
Description
A function to compute cross-validation errors
Usage
Xvalid_all(
mf,
maxL,
n.fold,
minbucket,
minsplit,
cp,
lookahead,
sss,
alpha.endcut,
a,
multi.start,
n.starts
)
Arguments
mf |
the data set with formula specified |
maxL |
the maximum number of splits |
n.fold |
the number of folds |
minbucket |
the minimum number of the studies in a terminal node |
minsplit |
the minimal number of studies in a parent node to be split |
cp |
the stopping rule for decrease of between-subgroups Q. Any split that does not decrease the between-subgroups Q is not attempted. |
lookahead |
an argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
A function to compute RE Q-between
Description
A function to compute RE Q-between
Usage
compute_rebetQ(y, vi, xk)
Arguments
y |
effect size |
vi |
sampling variance |
xk |
moderator |
Value
Q-between and tau2
A subset of data from Michie et al. (2009)
Description
The complete data consist of 101 studies reporting 122 interventions targeted at physical activity and healthy eating. In this subset of the data, the interventions that include at least one of the motivation-enhancing behaviour change techniques (BCTs) were selected (N = 106).
Usage
data(dat.BCT2009)
Format
A data frame of 106 interventions with five motivation-enhancing behavior change techniques (BCTs).
study: The name of the intervention.
g: The effect size of each intervention.
vi: The sampling variance of the effect size.
T1: Indicating whether the BCT1 "Provide information about behavior-health link" was used by the intervention. "1" for used and "0" for not used.
T2: Indicating whether the BCT2 "Provide information on consequences" was used by the intervention. "1" for used and "0" for not used.
T3: Indicating whether the BCT3 "Provide information about other's approval" was used by the intervention. "1" for used and "0" for not used.
T4: Indicating whether the BCT4 "Prompt intention formation" was used by the intervention. "1" for used and "0" for not used.
T25: Indicating whether the BCT25 " Motivational interviewing" was used by the intervention. "1" for used and "0" for not used.
Details
IMPORTANT: for questions about these data contact Juan Claramunt: j.claramunt.gonzalez@fsw.leidenuniv.nl.
References
If you use these data, please refer to: Michie, S., Abraham, C., Whittington, C., McAteer, J., & Gupta, S. (2009). Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychology, 28(6), 690.
An application of (a preliminary version of) meta-CART to this data set is given in: Dusseldorp, E., Van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of techniques that effectively change health behavior: Evidence from Meta-CART analysis. Health Psychology, 33(12), 1530.
A simulated meta-analytic data set with balanced pure interaction effects
Description
Data simulated from a true model with pure interactions between two moderators: x1, x2. If x1 = 0 and x2 = 1 or x1 = 1 and x2 = 0, the true effect size is 0.50. Otherwise, the true effect size is 0.
Usage
data(dat.balanced)
Format
A data frame of 60 studies with 4 moderators
efk: The effect size of each study expressed as Hedges' g
vark: The sampling variance of the effect size
x1 to x4: Four randomly generated moderators. x1, x2, and x4 are dichotomous variables, x3 is a continuous variable generated from uniform distribution.
A function to deal with symbols
Description
A function to deal with symbols
Usage
encodeHtml(input)
Arguments
input |
a string |
Value
converted string
A function to find the optimal combination of first two splits, and the corresponding Q-between given the first split
Description
A function to find the optimal combination of first two splits, and the corresponding Q-between given the first split
Usage
find_second_split(xk, first.splits, y, vi, minbucket, minsplit)
Arguments
xk |
moderator vector |
first.splits |
possible first splits |
y |
effect size |
vi |
sampling variance |
minbucket |
the minimal number of studies in child nodes |
minsplit |
the minimal number of studies in parent node |
Value
a list including all possible combinations of the triplet
A function to find the best triplets of parent, moderator, and split point.
Description
A function to find the best triplets of parent, moderator, and split point.
Usage
find_triplet(xk, nodeMbrship, y, vi, minbucket, minsplit)
Arguments
xk |
moderator vector |
nodeMbrship |
node membership vector |
y |
effect size |
vi |
sampling variance |
minbucket |
the minimal number of studies in child nodes |
minsplit |
the minimal number of studies in parent node |
Value
pleaf the parent node
cstar the split point
rank the rank used to order a categorical moderator
A function to list all possible split points for the first split
Description
A function to list all possible split points for the first split
Usage
make_first_split(xk, y, minbucket)
Arguments
xk |
moderator |
y |
effect size |
minbucket |
the minimum number of the studies in a terminal node |
Value
childNodes child nodes membership
rank the rank used to order a categorical moderator
is.num indicates if the moderator is numeric
A function to draw an oval
Description
A function to draw an oval
Usage
oval_draw(plotobj, x, y, c, x.scale = 1, y.scale = 1, ...)
Arguments
plotobj |
the obj to be plot |
x |
x |
y |
y |
c |
c |
x.scale |
x.scale |
y.scale |
y.scale |
Value
a ggplot object
A function returns the Q-between from the tree with given size
Description
A function returns the Q-between from the tree with given size
Usage
permuteFE(
mf,
Call,
nsplit,
P = 100,
sss,
lookahead,
minbucket = 3,
minsplit = 6,
cp = 1e-04,
maxdepth = 10,
alpha.endcut = 0.02,
a = 50,
multi.start = T,
n.starts = 3,
...
)
Arguments
mf |
data transformed to fit the tree. |
Call |
The matched call. |
nsplit |
the required number of splits. |
P |
the number of permuted data sets. |
sss |
boolean indicating whether the SSS strategy is used or not. |
lookahead |
a boolean argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
minbucket |
the minimum number of observations in any terminal <leaf> node. |
minsplit |
the minimum number of observations that must exist in a node in order for a split to be attempted. |
cp |
complexity parameter as in rpart. |
maxdepth |
set the maximum depth of any node of the final tree, with the root node counted as depth 0. |
alpha.endcut |
parameter used in the splitting algorithm to avoid the endcut preference problem. |
a |
parameter used in the sss to determine the slope of the logistic function that replaces the indicator function. |
multi.start |
boolean indicating whether multiple starts must be used |
n.starts |
number of multiple starts |
... |
Additional arguments passed to prune.rpart(). |
Value
The pruned tree
Permutation test
Description
Perform permutation test for an RE-tree
Usage
permuteRE(
mf,
nsplit,
P = 999,
sss,
lookahead,
minbucket = 3,
minsplit = 6,
cp = 1e-04,
alpha.endcut = 0.02,
a = 50,
multi.start = T,
n.starts = 3
)
Arguments
mf |
the data object of the RE-tree |
nsplit |
the number of splits in the RE-tree |
P |
the number of permuted data sets |
sss |
boolean indicating whether the SSS strategy is used or not. |
lookahead |
a boolean argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
minbucket |
the minimum number of observations in any terminal <leaf> node. |
minsplit |
the minimum number of observations that must exist in a node in order for a split to be attempted. |
cp |
complexity parameter as in rpart. |
alpha.endcut |
parameter used in the splitting algorithm to avoid the endcut preference problem. |
a |
parameter used in the sss to determine the slope of the logistic function that replaces the indicator function. |
multi.start |
boolean indicating whether multiple starts must be used |
n.starts |
number of multiple starts |
Value
a vector of Q-between computed from the permuted data sets
Visualisation of a FE meta-tree
Description
Plot function for a FEmrt object. The plot shows the result of FEmrt.
The plot function uses the plot method from the package ggplot2
Usage
## S3 method for class 'FEmrt'
plot(x, ...)
Arguments
x |
A FEmrt object. |
... |
additional arguments to pass |
Details
For categorical variables we recommend to use short names for levels to avoid overlapping labels at split points.
Value
A plot visualizing an FE meta-tree
Visualisation of a RE meta-tree
Description
Plot function for a REmrt object. The plot shows the result of REmrt.
The plot function uses the plot method from the package ggplot2
Usage
## S3 method for class 'REmrt'
plot(x, ...)
Arguments
x |
A REmrt object. |
... |
Additional arguments to pass. |
Details
For categorical variables we recommend to use short names for levels to avoid overlapping labels at split points.
Value
A plot visualizing an RE meta-tree
Predictions from a fitted FE metacart object
Description
Returns a data frame of predicted effect sizes and moderators from a fitted metacart object
Usage
## S3 method for class 'FEmrt'
predict(object, newdata, ...)
Arguments
object |
fitted model object of class "FEmrt". |
newdata |
data frame containing the values for which predictions are required. |
... |
Arguments that pass to other methods. |
Value
A data frame containing the predicted effect size, the moderators, and the corresponding node labels in the fitted tree.
Predictions from a fitted RE metacart object
Description
Returns a data frame of predicted effect sizes and moderators from a fitted metacart object
Usage
## S3 method for class 'REmrt'
predict(object, newdata, ...)
Arguments
object |
fitted model object of class "REmrt". |
newdata |
data frame containing the values for which predictions are required. |
... |
Arguments that pass to other methods. |
Value
A data frame containing the predicted effect size, the moderators, and the corresponding node labels in the fitted tree.
A function to predict newdata based on the fitted model
Description
A function to predict newdata based on the fitted model
Usage
prednode_cpp(x, newdata)
Arguments
x |
a fitted metaCART model |
newdata |
new data for prediction |
Print function for FEmrt
Description
Print the results of a FEmrt object
Usage
## S3 method for class 'FEmrt'
print(x, ...)
Arguments
x |
fitted tree of class |
... |
additional arguments to be passed. |
Details
The function returns the objects concerning the analysis results.
Value
Printed output of a FE meta-tree
Print function for REmrt
Description
Print the results of a REmrt object
Usage
## S3 method for class 'REmrt'
print(x, ...)
Arguments
x |
fitted tree of class |
... |
additional arguments to be passed. |
Details
The function returns the results (e.g., the value of the Q-between) after each split of the tree.
Value
Printed output of a RE meta-tree
Simple function using Rcpp
Description
Simple function using Rcpp
Usage
rcpp_hello_world()
Examples
## Not run:
rcpp_hello_world()
## End(Not run)
A function to find the split point
Description
A function to find the split point
Usage
re.cutoff_cpp(g, vi, x, inx.s, cnode, minbucket)
Arguments
g |
the effect size |
vi |
the sampling variance |
x |
the splitting moderator |
inx.s |
indicates whether a study belongs to the candidate parent leaf |
cnode |
the terminal nodes that the studies belong to in the current tree |
minbucket |
the minimum number of the studies in a terminal node |
Value
a vector including the split point, Q, and tau2
Summary of the results of a FE meta-tree object
Description
Summary of the results of a FE meta-tree object
Usage
## S3 method for class 'FEmrt'
summary(object, digits = 3, ...)
Arguments
object |
fitted tree of class |
digits |
specified number of decimals in the printed results. |
... |
additional arguments to be passed. |
Details
If no moderator effect is detected, the summary function will show the standard meta-analysis results. Otherwise, the summary function will show the subgroup meta-analysis results, with the significance test results for moderator effects, the split points of the moderators, and the estimated subgroup summary effect sizes.
Value
Summary of a FE meta-tree
Summary of the results of a RE meta-tree object
Description
Summary of the results of a RE meta-tree object
Usage
## S3 method for class 'REmrt'
summary(object, digits = 3, ...)
Arguments
object |
fitted tree of class |
digits |
specified number of decimals in the printed results. |
... |
additional arguments to be passed. |
Details
If no moderator effect is detected, the summary function will show the standard meta-analysis results. Otherwise, the summary function will show the subgroup meta-analysis results, with the significance test results for moderator effects, the split points of the moderators, and the estimated subgroup summary effect sizes.
Value
Summary of a RE meta-tree
Prune a tree
Description
Prune an initial rpart tree by "c-standard-error" rule.
Usage
treepruner(tree, c, ...)
Arguments
tree |
A initial tree fitted by rpart, needs to an rpart object. |
c |
A scalar to prune the tree by selecting the tree with minum cross-validation error plus the standard error multiplied by c. |
... |
Additional arguments passed to prune.rpart(). |
Value
The pruned tree
A function to update node
Description
A function to update node
Usage
updateNodes(nodes, newNode, name = "leaf")
Arguments
nodes |
a data frame |
newNode |
a new row |
name |
a string |
Value
a data.frame updated the nodes