macc: Mediation Analysis of Causality under Confounding
In macc: Mediation Analysis of Causality under Confounding
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
This function performs causal mediation analysis under confounding or correlated errors for single level, two-level, and three-level mediation models.
Usage
1
2
3
4
5
6
7
8
9
10
macc(dat, model.type = c("single", "multilevel", "twolevel"),
method = c("HL", "TS", "HL-TS"), delta = NULL, interval = c(-0.9, 0.9),
tol = 0.001, max.itr = 500, conf.level = 0.95,
optimizer = c("optimx", "bobyqa", "Nelder_Mead"), mix.pkg = c("nlme", "lme4"),
random.indep = TRUE, random.var.equal = FALSE, u.int = FALSE, Sigma.update = TRUE,
var.constraint = TRUE, random.var.update = TRUE, logLik.type = c("logLik", "HL"),
error.indep = TRUE, error.var.equal = FALSE,
sens.plot = FALSE, sens.interval = seq(-1, 1, by = 0.01), legend.pos = "topright",
xlab = expression(delta), ylab = expression(hat(AB)), cex.lab = 1, cex.axis = 1,
lgd.cex = 1, lgd.pt.cex = 1, plot.delta0 = TRUE, ...)
Arguments
dat
a data frame or a list of data. When it is a data frame, it contains Z as the treatment/exposure assignment, M as the mediator and R as the interested outcome and model.type should be "single". Z, M and R are all in one column. When it is a list, the list length is the number of subjects. For a two-level dataset, each list contains one data frame with Z, M and R, and model.type should be "twolevel"; for a three-level dataset, each subject list consists of K lists of data frame, and model.type should be "multilevel".
model.type
a character of model type, "single" for single level model, "multilevel" for three-level model and "twolevel" for two-level model.
method
a character of method that is used for the two/three-level mediation model. When delta is given, the method can be either "HL" (hierarchical-likelihood) or "TS" (two-stage); when delta is not given, the method can be "HL", "TS" or "HL-TS". The "HL-TS" method estimates delta by the "HL" method first and uses the "TS" method to estimate the rest parameters. For three-level model, when method = "HL" and u.int = TRUE, the parameters are estimated through a marginal-likelihood method.
delta
a number gives the correlation between the model errors. Default value is NULL. When model.type = "single", the default will be 0. For two/three-level model, if delta = NULL, the value of delta will be estimated.
interval
a vector of length two indicates the searching interval when estimating delta. Default is (-0.9,0.9).
tol
a number indicates the tolerence of convergence for the "HL" method. Default is 0.001.
max.itr
an integer indicates the maximum number of interation for the "HL" method. Default is 500.
conf.level
a number indicates the significance level of the confidence intervals. Default is 0.95.
optimizer
a character of the name of optimizing function(s) in the mixed effects model. This is used only for three-level model. For details, see lmerControl.
mix.pkg
a character of the package used for the mixed effects model in a three-level mediation model.
random.indep
a logic value indicates if the random effects in the mixed effects model are independent. Default is TRUE assuming the random effects are independent. This is used for model.type = "multilevel" only.
random.var.equal
a logic value indicates if the variances of the random effects are identical. Default is FALSE assuming the variances are not identical. This is used for model.type = "multilevel" only.
u.int
a logic value. Default is FALSE. When u.int = TRUE, a marginal-likelihood method, which integrates out the random effects in the mixed effects model, is used to estimate the parameters. This is used when model.type = "multilevel" and method = "HL" or method = "HL-TS".
Sigma.update
a logic value. Default is TRUE, and the estimated variances of the errors in the single level model will be updated in each iteration when running a two/three-level mediation model.
var.constraint
a logic value. Default is TRUE, and an interval constraint is added on the variance components in the two/three-level mediation model.
random.var.update
a logic value. Default is TRUE, and the estimates of the variance of the random effects in the mixed effects model are updated in each iteration. This is used when model.type = "multilevel" and method = "HL" or method = "HL-TS".
logLik.type
a character value indicating the type of likelihood value returned. It is used for "TS" method. When logLik.type = "logLik", the log-likelihood of the mixed effects model is maximized; when logLik.type = "HL", the summation of log-likelihood of the single level model and the mixed effects model is maximized. This is used for model.type = "multilevel".
error.indep
a logic value. Default is TRUE. This is used for model.type = "twolevel". When error.indep = TRUE, the error terms in the three linear models for A, B and C are independent.
error.var.equal
a logic value, Default is FALSE, This is used for model.type = "twolevel". When error.var.equal = TRUE, the variances of the error terms in the three linear models for A, B and C are assumed to be identical.
sens.plot
a logic value. Default is FALSE. This is used only for single level model. When sens.plot = TRUE, the sensitivity analysis will be performed and plotted.
sens.interval
a sequence of delta values under which the sensitivity analysis is performed. Default is a sequence from -1 to 1 with increment 0.01. The elements with absolute value 1 will be excluded from the analysis.
legend.pos
a character indicates the location of the legend when sens.plot = TRUE. This is used for single level model.
xlab
a title for x axis in the sensitivity plot.
ylab
a title for y axis in the sensitivity plot.
cex.lab
the magnigication to be used for x and y labels relative to the current setting of cex.
cex.axis
the magnification to be used for axis annotation relative to the current setting of cex.
lgd.cex
the magnification to be used for legend relative to the current setting of cex.
lgd.pt.cex
the magnification to be used for the points in legend relative to the current setting of cex.
plot.delta0
a logic value. Default is TRUE. When plot.delta0 = TRUE, the estimates when δ=0 is plotted.
...
additional argument to be passed.
Details
The single level mediation model is
M=ZA+E_1,
R=ZC+MB+E_2.
A correlation between the model errors E_1 and E_2 is assumed to be δ. The coefficients are estimated by maximizing the log-likelihood function. The confidence intervals of the coefficients are calculated based on the asymptotic joint distribution. The variance of AB estimator based on either the product method or the difference method is obtained from the Delta method. Under this single level model, δ is not identifiable. Sensitivity analysis for the indirect effect (AB) can be used to assess the deviation of the findings, when assuming δ=0 violates the independence assumption.
The two/three-level mediation models are proposed to estimate δ from data without sensitivity analysis. They address the within/between-subject variation issue for datasets with hierarchical structure. For simplicity, we refer to the three levels of data by trials, sessions and subjects, respectively. See reference for more details. Under the two-level mediation model, the data consists of N independent subjects and n_i trials for subject i; under the three-level mediation model, the data consists of N independent subjects, K sessions for each and n_{ik} trials. Under the two-level (three-level) models, the single level mediation model is first applied on the trials from (the same session of) a single subject. The coefficients then follow a linear (mixed effects) model. Here we enforce the assumption that δ is a constant across (sessions and) subjects. The parameters are estimated through a hierarchical-likelihood (or marginal likelihood) or a two-stage method.
Value
When model.type = "single",
Coefficients
point estimate of the coefficients, as well as the corresponding standard error and confidence interval. The indirect effect is estimated by both the product (ABp) and the difference (ABd) methods.
D
point estimate of the regression coefficients in matrix form.
Sigma
estimated covariance matrix of the model errors.
delta
the δ value used to estimate the rest parameters.
time
the CPU time used, see system.time.
When model.type = "multilevel",
delta
the specified or estimated value of correlation.
Coefficients
the estimated fixed effects in the mixed effects model for the coefficients, as well as the corresponding confidence intervals and standard errors. Here confidence intervals and standard errors are the estimates directly from the mixed effects model, the variation of estimating these parameters is not accounted for.
Cor.comp
estimated correlation matrix of the random effects in the mixed effects model.
Var.comp
estimated variance components in the mixed effects model.
Var.C2
estimated variance components for C', the total effect, if a mixed effects model is considered.
logLik
the value of maximized log-likelihood of the mixed effects model.
HL
the value of hierarchical-likelihood.
convergence
the logic value indicating if the method converges.
time
the CPU time used, see system.time
.
When model.type = "twolevel"
delta
the specified or estimated value of correlation.
Coefficients
the estimated population level effect in the regression models.
Lambda
the estimated covariance matrix of the model errors in the coefficient regression models.
Sigma
the estimated variances of E_1 and E_2 for each subject.
HL
the value of full-likelihood (hierarchical-likelihood).
convergence
the logic value indicating if the method converges.
Var.constraint
the interval constraints used for the variances in the coefficient regression models.
time
the CPU time used, see system.time
.
Author(s)
Yi Zhao, Brown University, [email protected];
Xi Luo, Brown University, [email protected].
References
Zhao, Y., & Luo, X. (2014). Estimating Mediation Effects under Correlated Errors with an Application to fMRI. arXiv preprint arXiv:1410.7217.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
# Examples with simulated data
##############################################################################
# Single level mediation model
# Data was generated with 500 independent trials. The correlation between model errors is 0.5.
data(env.single)
data.SL<-get("data1",env.single)
## Example 1: Given delta is 0.5.
macc(data.SL,model.type="single",delta=0.5)
# $Coefficients
# Estimate SE LB UB
# A 0.3572722 0.1483680 0.06647618 0.64806816
# C 0.8261253 0.2799667 0.27740060 1.37485006
# B -0.9260217 0.1599753 -1.23956743 -0.61247594
# C2 0.4952836 0.2441369 0.01678400 0.97378311
# ABp -0.3308418 0.1488060 -0.62249617 -0.03918738
# ABd -0.3308418 0.3714623 -1.05889442 0.39721087
## Example 2: Assume the errors are independent.
macc(data.SL,model.type="single",delta=0)
# $Coefficients
# Estimate SE LB UB
# A 0.3572721688 0.14836803 0.06647618 0.6480682
# C 0.4961424716 0.24413664 0.01764345 0.9746415
# B -0.0024040905 0.15997526 -0.31594984 0.3111417
# C2 0.4952835570 0.24413691 0.01678400 0.9737831
# ABp -0.0008589146 0.05715582 -0.11288227 0.1111644
# ABd -0.0008589146 0.34526154 -0.67755910 0.6758413
## Example 3: Sensitivity analysis (given delta is 0.5).
macc(data.SL,model.type="single",delta=0.5,sens.plot=TRUE)
##############################################################################
##############################################################################
# Three-level mediation model
# Data was generated with 50 subjects and 4 sessions.
# The correlation between model errors in the single level is 0.5.
# We comment out our examples due to the computation time.
data(env.three)
data.ML<-get("data3",env.three)
## Example 1: Correlation is unknown and to be estimated.
# Assume random effects are independent
# Add an interval constraint on the variance components.
# "HL" method
# macc(data.ML,model.type="multilevel",method="HL")
# $delta
# [1] 0.5224803
# $Coefficients
# Estimate LB UB SE
# A 0.51759400 0.3692202 0.6659678 0.07570229
# C 0.56882035 0.3806689 0.7569718 0.09599742
# B -1.13624114 -1.3688690 -0.9036133 0.11868988
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.58811160 -0.7952826 -0.3809406 0.10570145
# AB.diff -0.62961784 -1.0318524 -0.2273833 0.20522549
#
# $time
# user system elapsed
# 44.34 3.53 17.71
# "ML" method
# macc(data.ML,model.type="multilevel",method="HL",u.int=TRUE)
# $delta
# [1] 0.5430744
# $Coefficients
# Estimate LB UB SE
# A 0.51764821 0.3335094 0.7017871 0.09395011
# C 0.59652821 0.3715001 0.8215563 0.11481236
# B -1.19426328 -1.4508665 -0.9376601 0.13092240
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.61820825 -0.8751214 -0.3612951 0.13108056
# AB.diff -0.65732570 -1.0780742 -0.2365772 0.21467155
#
# $time
# user system elapsed
# 125.49 9.52 39.10
# "TS" method
# macc(data.ML,model.type="multilevel",method="TS")
# $delta
# [1] 0.5013719
# $Coefficients
# Estimate LB UB SE
# A 0.51805823 0.3316603 0.7044561 0.09510271
# C 0.53638546 0.3066109 0.7661601 0.11723409
# B -1.07930526 -1.3386926 -0.8199179 0.13234293
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.55914297 -0.8010745 -0.3172114 0.12343672
# AB.diff -0.59718295 -1.0204890 -0.1738769 0.21597645
#
# $time
# user system elapsed
# 19.53 0.00 19.54
## Example 2: Given the correlation is 0.5.
# Assume random effects are independent.
# Add an interval constraint on the variance components.
# "HL" method
macc(data.ML,model.type="multilevel",method="HL",delta=0.5)
# $delta
# [1] 0.5
# $Coefficients
# Estimate LB UB SE
# A 0.51760568 0.3692319 0.6659794 0.07570229
# C 0.53916412 0.3512951 0.7270331 0.09585330
# B -1.07675116 -1.3093989 -0.8441035 0.11869999
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.55733252 -0.7573943 -0.3572708 0.10207419
# AB.diff -0.59996161 -1.0020641 -0.1978591 0.20515811
#
# $time
# user system elapsed
# 2.44 0.22 1.03
##############################################################################
##############################################################################
# Two-level mediation model
# Data was generated with 50 subjects.
# The correlation between model errors in the single level is 0.5.
# We comment out our examples due to the computation time.
data(env.two)
data.TL<-get("data2",env.two)
## Example 1: Correlation is unknown and to be estimated.
# Assume errors in the coefficients regression models are independent.
# Add an interval constraint on the variance components.
# "HL" method
# macc(data.TL,model.type="twolevel",method="HL")
# $delta
# [1] 0.5066551
# $Coefficients
# Estimate
# A 0.51714224
# C 0.54392056
# B -1.05048406
# C2 -0.02924135
# AB.prod -0.54324968
# AB.diff -0.57316190
#
# $time
# user system elapsed
# 3.07 0.00 3.07
# "TS" method
# macc(data.TL,model.type="twolevel",method="TS")
# $delta
# [1] 0.4481611
# $Coefficients
# Estimate
# A 0.52013697
# C 0.47945755
# B -0.90252718
# C2 -0.02924135
# AB.prod -0.46943775
# AB.diff -0.50869890
#
# $time
# user system elapsed
# 1.60 0.00 1.59
## Example 2: Given the correlation is 0.5.
# Assume random effects are independent.
# Add an interval constraint on the variance components.
# "HL" method
macc(data.TL,model.type="twolevel",method="HL",delta=0.5)
# $delta
# [1] 0.5
# $Coefficients
# Estimate
# A 0.51718063
# C 0.53543300
# B -1.03336668
# C2 -0.02924135
# AB.prod -0.53443723
# AB.diff -0.56467434
#
# $time
# user system elapsed
# 0.21 0.00 0.20
##############################################################################
macc documentation built on Aug. 24, 2017, 1:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
As an Amazon Associate I earn from qualifying purchases.
Description Usage Arguments Details Value Author(s) References Examples
Description
This function performs causal mediation analysis under confounding or correlated errors for single level, two-level, and three-level mediation models.
Usage
1 2 3 4 5 6 7 8 9 10 | macc(dat, model.type = c("single", "multilevel", "twolevel"),
method = c("HL", "TS", "HL-TS"), delta = NULL, interval = c(-0.9, 0.9),
tol = 0.001, max.itr = 500, conf.level = 0.95,
optimizer = c("optimx", "bobyqa", "Nelder_Mead"), mix.pkg = c("nlme", "lme4"),
random.indep = TRUE, random.var.equal = FALSE, u.int = FALSE, Sigma.update = TRUE,
var.constraint = TRUE, random.var.update = TRUE, logLik.type = c("logLik", "HL"),
error.indep = TRUE, error.var.equal = FALSE,
sens.plot = FALSE, sens.interval = seq(-1, 1, by = 0.01), legend.pos = "topright",
xlab = expression(delta), ylab = expression(hat(AB)), cex.lab = 1, cex.axis = 1,
lgd.cex = 1, lgd.pt.cex = 1, plot.delta0 = TRUE, ...)
|
Arguments
dat |
a data frame or a list of data. When it is a data frame, it contains |
model.type |
a character of model type, "single" for single level model, "multilevel" for three-level model and "twolevel" for two-level model. |
method |
a character of method that is used for the two/three-level mediation model. When |
delta |
a number gives the correlation between the model errors. Default value is |
interval |
a vector of length two indicates the searching interval when estimating |
tol |
a number indicates the tolerence of convergence for the "HL" method. Default is 0.001. |
max.itr |
an integer indicates the maximum number of interation for the "HL" method. Default is 500. |
conf.level |
a number indicates the significance level of the confidence intervals. Default is 0.95. |
optimizer |
a character of the name of optimizing function(s) in the mixed effects model. This is used only for three-level model. For details, see |
mix.pkg |
a character of the package used for the mixed effects model in a three-level mediation model. |
random.indep |
a logic value indicates if the random effects in the mixed effects model are independent. Default is |
random.var.equal |
a logic value indicates if the variances of the random effects are identical. Default is |
u.int |
a logic value. Default is |
Sigma.update |
a logic value. Default is |
var.constraint |
a logic value. Default is |
random.var.update |
a logic value. Default is |
logLik.type |
a character value indicating the type of likelihood value returned. It is used for "TS" method. When |
error.indep |
a logic value. Default is |
error.var.equal |
a logic value, Default is |
sens.plot |
a logic value. Default is |
sens.interval |
a sequence of |
legend.pos |
a character indicates the location of the legend when |
xlab |
a title for |
ylab |
a title for |
cex.lab |
the magnigication to be used for |
cex.axis |
the magnification to be used for axis annotation relative to the current setting of |
lgd.cex |
the magnification to be used for legend relative to the current setting of |
lgd.pt.cex |
the magnification to be used for the points in legend relative to the current setting of |
plot.delta0 |
a logic value. Default is |
... |
additional argument to be passed. |
Details
The single level mediation model is
M=ZA+E_1,
R=ZC+MB+E_2.
A correlation between the model errors E_1 and E_2 is assumed to be δ. The coefficients are estimated by maximizing the log-likelihood function. The confidence intervals of the coefficients are calculated based on the asymptotic joint distribution. The variance of AB estimator based on either the product method or the difference method is obtained from the Delta method. Under this single level model, δ is not identifiable. Sensitivity analysis for the indirect effect (AB) can be used to assess the deviation of the findings, when assuming δ=0 violates the independence assumption.
The two/three-level mediation models are proposed to estimate δ from data without sensitivity analysis. They address the within/between-subject variation issue for datasets with hierarchical structure. For simplicity, we refer to the three levels of data by trials, sessions and subjects, respectively. See reference for more details. Under the two-level mediation model, the data consists of N independent subjects and n_i trials for subject i; under the three-level mediation model, the data consists of N independent subjects, K sessions for each and n_{ik} trials. Under the two-level (three-level) models, the single level mediation model is first applied on the trials from (the same session of) a single subject. The coefficients then follow a linear (mixed effects) model. Here we enforce the assumption that δ is a constant across (sessions and) subjects. The parameters are estimated through a hierarchical-likelihood (or marginal likelihood) or a two-stage method.
Value
When model.type = "single",
Coefficients |
point estimate of the coefficients, as well as the corresponding standard error and confidence interval. The indirect effect is estimated by both the product ( |
D |
point estimate of the regression coefficients in matrix form. |
Sigma |
estimated covariance matrix of the model errors. |
delta |
the δ value used to estimate the rest parameters. |
time |
the CPU time used, see |
When model.type = "multilevel",
delta |
the specified or estimated value of correlation. |
Coefficients |
the estimated fixed effects in the mixed effects model for the coefficients, as well as the corresponding confidence intervals and standard errors. Here confidence intervals and standard errors are the estimates directly from the mixed effects model, the variation of estimating these parameters is not accounted for. |
Cor.comp |
estimated correlation matrix of the random effects in the mixed effects model. |
Var.comp |
estimated variance components in the mixed effects model. |
Var.C2 |
estimated variance components for C', the total effect, if a mixed effects model is considered. |
logLik |
the value of maximized log-likelihood of the mixed effects model. |
HL |
the value of hierarchical-likelihood. |
convergence |
the logic value indicating if the method converges. |
time |
the CPU time used, see |
.
When model.type = "twolevel"
delta |
the specified or estimated value of correlation. |
Coefficients |
the estimated population level effect in the regression models. |
Lambda |
the estimated covariance matrix of the model errors in the coefficient regression models. |
Sigma |
the estimated variances of E_1 and E_2 for each subject. |
HL |
the value of full-likelihood (hierarchical-likelihood). |
convergence |
the logic value indicating if the method converges. |
Var.constraint |
the interval constraints used for the variances in the coefficient regression models. |
time |
the CPU time used, see |
.
Author(s)
Yi Zhao, Brown University, [email protected]; Xi Luo, Brown University, [email protected].
References
Zhao, Y., & Luo, X. (2014). Estimating Mediation Effects under Correlated Errors with an Application to fMRI. arXiv preprint arXiv:1410.7217.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | # Examples with simulated data
##############################################################################
# Single level mediation model
# Data was generated with 500 independent trials. The correlation between model errors is 0.5.
data(env.single)
data.SL<-get("data1",env.single)
## Example 1: Given delta is 0.5.
macc(data.SL,model.type="single",delta=0.5)
# $Coefficients
# Estimate SE LB UB
# A 0.3572722 0.1483680 0.06647618 0.64806816
# C 0.8261253 0.2799667 0.27740060 1.37485006
# B -0.9260217 0.1599753 -1.23956743 -0.61247594
# C2 0.4952836 0.2441369 0.01678400 0.97378311
# ABp -0.3308418 0.1488060 -0.62249617 -0.03918738
# ABd -0.3308418 0.3714623 -1.05889442 0.39721087
## Example 2: Assume the errors are independent.
macc(data.SL,model.type="single",delta=0)
# $Coefficients
# Estimate SE LB UB
# A 0.3572721688 0.14836803 0.06647618 0.6480682
# C 0.4961424716 0.24413664 0.01764345 0.9746415
# B -0.0024040905 0.15997526 -0.31594984 0.3111417
# C2 0.4952835570 0.24413691 0.01678400 0.9737831
# ABp -0.0008589146 0.05715582 -0.11288227 0.1111644
# ABd -0.0008589146 0.34526154 -0.67755910 0.6758413
## Example 3: Sensitivity analysis (given delta is 0.5).
macc(data.SL,model.type="single",delta=0.5,sens.plot=TRUE)
##############################################################################
##############################################################################
# Three-level mediation model
# Data was generated with 50 subjects and 4 sessions.
# The correlation between model errors in the single level is 0.5.
# We comment out our examples due to the computation time.
data(env.three)
data.ML<-get("data3",env.three)
## Example 1: Correlation is unknown and to be estimated.
# Assume random effects are independent
# Add an interval constraint on the variance components.
# "HL" method
# macc(data.ML,model.type="multilevel",method="HL")
# $delta
# [1] 0.5224803
# $Coefficients
# Estimate LB UB SE
# A 0.51759400 0.3692202 0.6659678 0.07570229
# C 0.56882035 0.3806689 0.7569718 0.09599742
# B -1.13624114 -1.3688690 -0.9036133 0.11868988
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.58811160 -0.7952826 -0.3809406 0.10570145
# AB.diff -0.62961784 -1.0318524 -0.2273833 0.20522549
#
# $time
# user system elapsed
# 44.34 3.53 17.71
# "ML" method
# macc(data.ML,model.type="multilevel",method="HL",u.int=TRUE)
# $delta
# [1] 0.5430744
# $Coefficients
# Estimate LB UB SE
# A 0.51764821 0.3335094 0.7017871 0.09395011
# C 0.59652821 0.3715001 0.8215563 0.11481236
# B -1.19426328 -1.4508665 -0.9376601 0.13092240
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.61820825 -0.8751214 -0.3612951 0.13108056
# AB.diff -0.65732570 -1.0780742 -0.2365772 0.21467155
#
# $time
# user system elapsed
# 125.49 9.52 39.10
# "TS" method
# macc(data.ML,model.type="multilevel",method="TS")
# $delta
# [1] 0.5013719
# $Coefficients
# Estimate LB UB SE
# A 0.51805823 0.3316603 0.7044561 0.09510271
# C 0.53638546 0.3066109 0.7661601 0.11723409
# B -1.07930526 -1.3386926 -0.8199179 0.13234293
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.55914297 -0.8010745 -0.3172114 0.12343672
# AB.diff -0.59718295 -1.0204890 -0.1738769 0.21597645
#
# $time
# user system elapsed
# 19.53 0.00 19.54
## Example 2: Given the correlation is 0.5.
# Assume random effects are independent.
# Add an interval constraint on the variance components.
# "HL" method
macc(data.ML,model.type="multilevel",method="HL",delta=0.5)
# $delta
# [1] 0.5
# $Coefficients
# Estimate LB UB SE
# A 0.51760568 0.3692319 0.6659794 0.07570229
# C 0.53916412 0.3512951 0.7270331 0.09585330
# B -1.07675116 -1.3093989 -0.8441035 0.11869999
# C2 -0.06079748 -0.4163135 0.2947186 0.18138908
# AB.prod -0.55733252 -0.7573943 -0.3572708 0.10207419
# AB.diff -0.59996161 -1.0020641 -0.1978591 0.20515811
#
# $time
# user system elapsed
# 2.44 0.22 1.03
##############################################################################
##############################################################################
# Two-level mediation model
# Data was generated with 50 subjects.
# The correlation between model errors in the single level is 0.5.
# We comment out our examples due to the computation time.
data(env.two)
data.TL<-get("data2",env.two)
## Example 1: Correlation is unknown and to be estimated.
# Assume errors in the coefficients regression models are independent.
# Add an interval constraint on the variance components.
# "HL" method
# macc(data.TL,model.type="twolevel",method="HL")
# $delta
# [1] 0.5066551
# $Coefficients
# Estimate
# A 0.51714224
# C 0.54392056
# B -1.05048406
# C2 -0.02924135
# AB.prod -0.54324968
# AB.diff -0.57316190
#
# $time
# user system elapsed
# 3.07 0.00 3.07
# "TS" method
# macc(data.TL,model.type="twolevel",method="TS")
# $delta
# [1] 0.4481611
# $Coefficients
# Estimate
# A 0.52013697
# C 0.47945755
# B -0.90252718
# C2 -0.02924135
# AB.prod -0.46943775
# AB.diff -0.50869890
#
# $time
# user system elapsed
# 1.60 0.00 1.59
## Example 2: Given the correlation is 0.5.
# Assume random effects are independent.
# Add an interval constraint on the variance components.
# "HL" method
macc(data.TL,model.type="twolevel",method="HL",delta=0.5)
# $delta
# [1] 0.5
# $Coefficients
# Estimate
# A 0.51718063
# C 0.53543300
# B -1.03336668
# C2 -0.02924135
# AB.prod -0.53443723
# AB.diff -0.56467434
#
# $time
# user system elapsed
# 0.21 0.00 0.20
##############################################################################
|
macc documentation built on Aug. 24, 2017, 1:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
As an Amazon Associate I earn from qualifying purchases.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.