wrap: ~~function to do ... ~~
In jservadio/TrialistNof1: Bayesian N-of-1 calculations
Usage
1
wrap(a)
Arguments
a
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (a)
{
Intensity2 = Enjoyment2 = Activity2 = Fatigue2 = Drowsy2 = Sleep2 = Thinking2 = Constipation2 = Sharpness2 = Hotness2 = Sensitivity2 = Block2 = Day2 = rep(NA,
nrow(a$data))
for (i in 1:nrow(a$data)) {
Day2[i] = as.numeric(as.Date(a$data[[1]][i]) - as.Date(a$metadata[[3]]))
Block2[i] = a$data[[3]][i]
Intensity2[i] = a$data[[4]][i]
Enjoyment2[i] = a$data[[5]][i]
Activity2[i] = a$data[[6]][i]
Fatigue2[i] = a$data[[7]][i]
Drowsy2[i] = a$data[[8]][i]
Sleep2[i] = a$data[[9]][i]
Thinking2[i] = a$data[[10]][i]
Constipation2[i] = a$data[[11]][i]
Sharpness2[i] = a$data[[12]][i]
Hotness2[i] = a$data[[13]][i]
Sensitivity2[i] = a$data[[14]][i]
}
Day2 = Day2 + 1
Pain2 = Intensity2 + Enjoyment2 + Activity2
Neuropain2 = Sharpness2 + Hotness2 + Sensitivity2
Treat2 = rep(NA, nrow(a$data))
for (i in 1:nrow(a$data)) {
if (!is.na(a$data[[2]][i]) & a$data[[2]][i] == "A") {
Treat2[i] = 0
}
if (!is.na(a$data[[2]][i]) & a$data[[2]][i] == "B") {
Treat2[i] = 1
}
}
Covs2 = cbind(Day2, Block2)
for (i in 1:length(Pain2)) {
if (Pain2[i] < 1) {
Pain2[i] = 1
}
if (Pain2[i] > 30) {
Pain2[i] = 30
}
if (Fatigue2[i] < 1) {
Fatigue2[i] = 1
}
if (Fatigue2[i] > 4) {
Fatigue2[i] = 4
}
if (Drowsy2[i] < 1) {
Drowsy2[i] = 1
}
if (Drowsy2[i] > 5) {
Drowsy2[i] = 5
}
if (Sleep2[i] < 1) {
Sleep2[i] = 1
}
if (Sleep2[i] > 4) {
Sleep2[i] = 4
}
if (Thinking2[i] < 1) {
Thinking2[i] = 1
}
if (Thinking2[i] > 4) {
Thinking2[i] = 4
}
if (Constipation2[i] < 1) {
Constipation2[i] = 1
}
if (Constipation2[i] > 4) {
Constipation2[i] = 4
}
if (Neuropain2[i] < 1) {
Neuropain2[i] = 1
}
if (Neuropain2[i] > 30) {
Neuropain2[i] = 30
}
}
observations = cbind(Day2, Pain2, Fatigue2, Drowsy2, Sleep2,
Thinking2, Constipation2, Neuropain2, Treat2, Block2)
logit = function(x) log(x/(1 - x))
nof1 = analyze(Pain = Pain2, Fatigue = Fatigue2, Drowsy = Drowsy2,
Sleep = Sleep2, Thinking = Thinking2, Constipation = Constipation2,
Neuropain = Neuropain2, Treat = Treat2, score.range = c(30,
4, 5, 4, 4, 4, 30), Covs = NULL, slopeprior = list("norm",
0, 0.1), nChains = 3, conv.limit = 1.05, niters = 10000,
setsize = 1000, alphaprior = list("norm", 0, 1e-06),
beta.norm.prior = list("norm", 0, 1e-06), beta.ord.prior = list("norm",
0, 1e-06), dcprior = list("unif", 0, 20), c1prior = list("unif",
-20, 20), varprior = list("Sd", "unif"), varprior.params = c(0,
5), path = "")
P025 = t(cbind(nof1$Pain$interval$P025, nof1$Fatigue$interval$P025,
nof1$Drowsy$interval$P025, nof1$Sleep$interval$P025,
nof1$Thinking$interval$P025, nof1$Constipation$interval$P025,
nof1$"Neuropathic Pain"$interval$P025))
Median = t(cbind(nof1$Pain$interval$Median, nof1$Fatigue$interval$Median,
nof1$Drowsy$interval$Median, nof1$Sleep$interval$Median,
nof1$Thinking$interval$Median, nof1$Constipation$interval$Median,
nof1$"Neuropathic Pain"$interval$Median))
P975 = t(cbind(nof1$Pain$interval$P975, nof1$Fatigue$interval$P975,
nof1$Drowsy$interval$P975, nof1$Sleep$interval$P975,
nof1$Thinking$interval$P975, nof1$Constipation$interval$P975,
nof1$"Neuropathic Pain"$interval$P975))
P975 = t(cbind(nof1$Pain$interval$P975, nof1$Fatigue$interval$P975,
nof1$Drowsy$interval$P975, nof1$Sleep$interval$P975,
nof1$Thinking$interval$P975, nof1$Constipation$interval$P975,
nof1$"Neuropathic Pain"$interval$P975))
Prob1 = t(cbind(nof1$Pain$probs$"Proportion < -0.2", nof1$Fatigue$probs$"Proportion < -0.2",
nof1$Drowsy$probs$"Proportion < -0.2", nof1$Sleep$probs$"Proportion < -0.2",
nof1$Thinking$probs$"Proportion < -0.2", nof1$Constipation$probs$"Proportion < -0.2",
nof1$"Neuropathic Pain"$probs$"Proportion < -0.2"))
Prob2 = t(cbind(nof1$Pain$probs$"Proportion -0.2 - 0", nof1$Fatigue$probs$"Proportion -0.2 - 0",
nof1$Drowsy$probs$"Proportion -0.2 - 0", nof1$Sleep$probs$"Proportion -0.2 - 0",
nof1$Thinking$probs$"Proportion -0.2 - 0", nof1$Constipation$probs$"Proportion -0.2 - 0",
nof1$"Neuropathic Pain"$probs$"Proportion -0.2 - 0"))
Prob3 = t(cbind(nof1$Pain$probs$"Proportion 0 - 0.2", nof1$Fatigue$probs$"Proportion 0 - 0.2",
nof1$Drowsy$probs$"Proportion 0 - 0.2", nof1$Sleep$probs$"Proportion 0 - 0.2",
nof1$Thinking$probs$"Proportion 0 - 0.2", nof1$Constipation$probs$"Proportion 0 - 0.2",
nof1$"Neuropathic Pain"$probs$"Proportion 0 - 0.2"))
Prob4 = t(cbind(nof1$Pain$probs$"Proportion > 0.2", nof1$Fatigue$probs$"Proportion > 0.2",
nof1$Drowsy$probs$"Proportion > 0.2", nof1$Sleep$probs$"Proportion > 0.2",
nof1$Thinking$probs$"Proportion > 0.2", nof1$Constipation$probs$"Proportion > 0.2",
nof1$"Neuropathic Pain"$probs$"Proportion > 0.2"))
Results = cbind(P025, Median, P975, Prob1, Prob2, Prob3,
Prob4)
colnames(Results) <- c("P025", "Median", "P975", "P(< - 0.2)",
"P(-0.2 - 0)", "P(0 - 0.2)", "P(> 0.2)")
rownames(Results) <- c("Pain", "Fatigue", "Drowsy", "Sleep",
"Thinking", "Constipation", "Neuropain")
Results_mod = matrix(NA, ncol = 10, nrow = 7)
for (i in 1:nrow(Results_mod)) {
if (Results[i, 2] < 0) {
Results_mod[i, 1] = "B"
}
if (Results[i, 2] > 0) {
Results_mod[i, 1] = "A"
}
if (Results[i, 2] == 0) {
Results_mod[i, 1] = "Neither"
}
if (Results[i, 3] < 0) {
Results_mod[i, 4] = "B"
}
if (Results[i, 3] > 0) {
Results_mod[i, 4] = "A"
}
if (Results[i, 1] < 0) {
Results_mod[i, 6] = "B"
}
if (Results[i, 1] > 0) {
Results_mod[i, 6] = "A"
}
Results_mod[i, 2] = abs(Results[i, 2])
Results_mod[i, 3] = abs(Results[i, 3])
Results_mod[i, 5] = abs(Results[i, 1])
Results_mod[i, 7] = Results[i, 4]
Results_mod[i, 8] = Results[i, 5]
Results_mod[i, 9] = Results[i, 7]
Results_mod[i, 10] = Results[i, 6]
}
colnames(Results_mod) = c("more_effective_regimen", "median_effect",
"upper_bound", "upper_bound_regimen", "lower_bound",
"lower_bound_regimen", "b_clinically_better", "b_marginally_better",
"a_clinically_better", "a_marginally_better")
rownames(Results_mod) = c("pain", "fatigue", "drowsiness",
"sleep_problems", "thinking_problems", "constipation",
"neuropathic_pain")
graph_5 = list(more_effective_regimen = Results_mod[1, 1],
median_effect = as.numeric(Results_mod[1, 2]), upper_bound = as.numeric(Results_mod[1,
3]), upper_bound_regimen = Results_mod[1, 4], lower_bound = as.numeric(Results_mod[1,
5]), lower_bound_regimen = Results_mod[1, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[1,
7]), b_marginally_better = as.numeric(Results_mod[1,
8]), a_clinically_better = as.numeric(Results_mod[1,
9]), a_marginally_better = as.numeric(Results_mod[1,
10]))
pain = list(graph_5, graph_6)
names(pain) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[4, 1],
median_effect = as.numeric(Results_mod[4, 2]), upper_bound = as.numeric(Results_mod[4,
3]), upper_bound_regimen = Results_mod[4, 4], lower_bound = as.numeric(Results_mod[4,
5]), lower_bound_regimen = Results_mod[4, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[4,
7]), b_marginally_better = as.numeric(Results_mod[4,
8]), a_clinically_better = as.numeric(Results_mod[4,
9]), a_marginally_better = as.numeric(Results_mod[4,
10]))
sleep_problems = list(graph_5, graph_6)
names(sleep_problems) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[6, 1],
median_effect = as.numeric(Results_mod[6, 2]), upper_bound = as.numeric(Results_mod[6,
3]), upper_bound_regimen = Results_mod[6, 4], lower_bound = as.numeric(Results_mod[6,
5]), lower_bound_regimen = Results_mod[6, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[6,
7]), b_marginally_better = as.numeric(Results_mod[6,
8]), a_clinically_better = as.numeric(Results_mod[6,
9]), a_marginally_better = as.numeric(Results_mod[6,
10]))
constipation = list(graph_5, graph_6)
names(constipation) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[3, 1],
median_effect = as.numeric(Results_mod[3, 2]), upper_bound = as.numeric(Results_mod[3,
3]), upper_bound_regimen = Results_mod[3, 4], lower_bound = as.numeric(Results_mod[3,
5]), lower_bound_regimen = Results_mod[3, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[3,
7]), b_marginally_better = as.numeric(Results_mod[3,
8]), a_clinically_better = as.numeric(Results_mod[3,
9]), a_marginally_better = as.numeric(Results_mod[3,
10]))
drowsiness = list(graph_5, graph_6)
names(drowsiness) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[5, 1],
median_effect = as.numeric(Results_mod[5, 2]), upper_bound = as.numeric(Results_mod[5,
3]), upper_bound_regimen = Results_mod[5, 4], lower_bound = as.numeric(Results_mod[5,
5]), lower_bound_regimen = Results_mod[5, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[5,
7]), b_marginally_better = as.numeric(Results_mod[5,
8]), a_clinically_better = as.numeric(Results_mod[5,
9]), a_marginally_better = as.numeric(Results_mod[5,
10]))
thinking_problems = list(graph_5, graph_6)
names(thinking_problems) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[2, 1],
median_effect = as.numeric(Results_mod[2, 2]), upper_bound = as.numeric(Results_mod[2,
3]), upper_bound_regimen = Results_mod[2, 4], lower_bound = as.numeric(Results_mod[2,
5]), lower_bound_regimen = Results_mod[2, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[2,
7]), b_marginally_better = as.numeric(Results_mod[2,
8]), a_clinically_better = as.numeric(Results_mod[2,
9]), a_marginally_better = as.numeric(Results_mod[2,
10]))
fatigue = list(graph_5, graph_6)
names(fatigue) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[7, 1],
median_effect = as.numeric(Results_mod[7, 2]), upper_bound = as.numeric(Results_mod[7,
3]), upper_bound_regimen = Results_mod[7, 4], lower_bound = as.numeric(Results_mod[7,
5]), lower_bound_regimen = Results_mod[7, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[7,
7]), b_marginally_better = as.numeric(Results_mod[7,
8]), a_clinically_better = as.numeric(Results_mod[7,
9]), a_marginally_better = as.numeric(Results_mod[7,
10]))
neuropathic_pain = list(graph_5, graph_6)
names(neuropathic_pain) = c("graph_5", "graph_6")
y = list(pain, sleep_problems, constipation, drowsiness,
thinking_problems, fatigue, neuropathic_pain)
names(y) = c("pain", "sleep_problems", "constipation", "drowsiness",
"thinking_problems", "fatigue", "neuropathic_pain")
return(y)
}
jservadio/TrialistNof1 documentation built on May 20, 2019, 2:08 a.m.
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Usage
1 | wrap(a)
|
Arguments
a |
Examples
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##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (a)
{
Intensity2 = Enjoyment2 = Activity2 = Fatigue2 = Drowsy2 = Sleep2 = Thinking2 = Constipation2 = Sharpness2 = Hotness2 = Sensitivity2 = Block2 = Day2 = rep(NA,
nrow(a$data))
for (i in 1:nrow(a$data)) {
Day2[i] = as.numeric(as.Date(a$data[[1]][i]) - as.Date(a$metadata[[3]]))
Block2[i] = a$data[[3]][i]
Intensity2[i] = a$data[[4]][i]
Enjoyment2[i] = a$data[[5]][i]
Activity2[i] = a$data[[6]][i]
Fatigue2[i] = a$data[[7]][i]
Drowsy2[i] = a$data[[8]][i]
Sleep2[i] = a$data[[9]][i]
Thinking2[i] = a$data[[10]][i]
Constipation2[i] = a$data[[11]][i]
Sharpness2[i] = a$data[[12]][i]
Hotness2[i] = a$data[[13]][i]
Sensitivity2[i] = a$data[[14]][i]
}
Day2 = Day2 + 1
Pain2 = Intensity2 + Enjoyment2 + Activity2
Neuropain2 = Sharpness2 + Hotness2 + Sensitivity2
Treat2 = rep(NA, nrow(a$data))
for (i in 1:nrow(a$data)) {
if (!is.na(a$data[[2]][i]) & a$data[[2]][i] == "A") {
Treat2[i] = 0
}
if (!is.na(a$data[[2]][i]) & a$data[[2]][i] == "B") {
Treat2[i] = 1
}
}
Covs2 = cbind(Day2, Block2)
for (i in 1:length(Pain2)) {
if (Pain2[i] < 1) {
Pain2[i] = 1
}
if (Pain2[i] > 30) {
Pain2[i] = 30
}
if (Fatigue2[i] < 1) {
Fatigue2[i] = 1
}
if (Fatigue2[i] > 4) {
Fatigue2[i] = 4
}
if (Drowsy2[i] < 1) {
Drowsy2[i] = 1
}
if (Drowsy2[i] > 5) {
Drowsy2[i] = 5
}
if (Sleep2[i] < 1) {
Sleep2[i] = 1
}
if (Sleep2[i] > 4) {
Sleep2[i] = 4
}
if (Thinking2[i] < 1) {
Thinking2[i] = 1
}
if (Thinking2[i] > 4) {
Thinking2[i] = 4
}
if (Constipation2[i] < 1) {
Constipation2[i] = 1
}
if (Constipation2[i] > 4) {
Constipation2[i] = 4
}
if (Neuropain2[i] < 1) {
Neuropain2[i] = 1
}
if (Neuropain2[i] > 30) {
Neuropain2[i] = 30
}
}
observations = cbind(Day2, Pain2, Fatigue2, Drowsy2, Sleep2,
Thinking2, Constipation2, Neuropain2, Treat2, Block2)
logit = function(x) log(x/(1 - x))
nof1 = analyze(Pain = Pain2, Fatigue = Fatigue2, Drowsy = Drowsy2,
Sleep = Sleep2, Thinking = Thinking2, Constipation = Constipation2,
Neuropain = Neuropain2, Treat = Treat2, score.range = c(30,
4, 5, 4, 4, 4, 30), Covs = NULL, slopeprior = list("norm",
0, 0.1), nChains = 3, conv.limit = 1.05, niters = 10000,
setsize = 1000, alphaprior = list("norm", 0, 1e-06),
beta.norm.prior = list("norm", 0, 1e-06), beta.ord.prior = list("norm",
0, 1e-06), dcprior = list("unif", 0, 20), c1prior = list("unif",
-20, 20), varprior = list("Sd", "unif"), varprior.params = c(0,
5), path = "")
P025 = t(cbind(nof1$Pain$interval$P025, nof1$Fatigue$interval$P025,
nof1$Drowsy$interval$P025, nof1$Sleep$interval$P025,
nof1$Thinking$interval$P025, nof1$Constipation$interval$P025,
nof1$"Neuropathic Pain"$interval$P025))
Median = t(cbind(nof1$Pain$interval$Median, nof1$Fatigue$interval$Median,
nof1$Drowsy$interval$Median, nof1$Sleep$interval$Median,
nof1$Thinking$interval$Median, nof1$Constipation$interval$Median,
nof1$"Neuropathic Pain"$interval$Median))
P975 = t(cbind(nof1$Pain$interval$P975, nof1$Fatigue$interval$P975,
nof1$Drowsy$interval$P975, nof1$Sleep$interval$P975,
nof1$Thinking$interval$P975, nof1$Constipation$interval$P975,
nof1$"Neuropathic Pain"$interval$P975))
P975 = t(cbind(nof1$Pain$interval$P975, nof1$Fatigue$interval$P975,
nof1$Drowsy$interval$P975, nof1$Sleep$interval$P975,
nof1$Thinking$interval$P975, nof1$Constipation$interval$P975,
nof1$"Neuropathic Pain"$interval$P975))
Prob1 = t(cbind(nof1$Pain$probs$"Proportion < -0.2", nof1$Fatigue$probs$"Proportion < -0.2",
nof1$Drowsy$probs$"Proportion < -0.2", nof1$Sleep$probs$"Proportion < -0.2",
nof1$Thinking$probs$"Proportion < -0.2", nof1$Constipation$probs$"Proportion < -0.2",
nof1$"Neuropathic Pain"$probs$"Proportion < -0.2"))
Prob2 = t(cbind(nof1$Pain$probs$"Proportion -0.2 - 0", nof1$Fatigue$probs$"Proportion -0.2 - 0",
nof1$Drowsy$probs$"Proportion -0.2 - 0", nof1$Sleep$probs$"Proportion -0.2 - 0",
nof1$Thinking$probs$"Proportion -0.2 - 0", nof1$Constipation$probs$"Proportion -0.2 - 0",
nof1$"Neuropathic Pain"$probs$"Proportion -0.2 - 0"))
Prob3 = t(cbind(nof1$Pain$probs$"Proportion 0 - 0.2", nof1$Fatigue$probs$"Proportion 0 - 0.2",
nof1$Drowsy$probs$"Proportion 0 - 0.2", nof1$Sleep$probs$"Proportion 0 - 0.2",
nof1$Thinking$probs$"Proportion 0 - 0.2", nof1$Constipation$probs$"Proportion 0 - 0.2",
nof1$"Neuropathic Pain"$probs$"Proportion 0 - 0.2"))
Prob4 = t(cbind(nof1$Pain$probs$"Proportion > 0.2", nof1$Fatigue$probs$"Proportion > 0.2",
nof1$Drowsy$probs$"Proportion > 0.2", nof1$Sleep$probs$"Proportion > 0.2",
nof1$Thinking$probs$"Proportion > 0.2", nof1$Constipation$probs$"Proportion > 0.2",
nof1$"Neuropathic Pain"$probs$"Proportion > 0.2"))
Results = cbind(P025, Median, P975, Prob1, Prob2, Prob3,
Prob4)
colnames(Results) <- c("P025", "Median", "P975", "P(< - 0.2)",
"P(-0.2 - 0)", "P(0 - 0.2)", "P(> 0.2)")
rownames(Results) <- c("Pain", "Fatigue", "Drowsy", "Sleep",
"Thinking", "Constipation", "Neuropain")
Results_mod = matrix(NA, ncol = 10, nrow = 7)
for (i in 1:nrow(Results_mod)) {
if (Results[i, 2] < 0) {
Results_mod[i, 1] = "B"
}
if (Results[i, 2] > 0) {
Results_mod[i, 1] = "A"
}
if (Results[i, 2] == 0) {
Results_mod[i, 1] = "Neither"
}
if (Results[i, 3] < 0) {
Results_mod[i, 4] = "B"
}
if (Results[i, 3] > 0) {
Results_mod[i, 4] = "A"
}
if (Results[i, 1] < 0) {
Results_mod[i, 6] = "B"
}
if (Results[i, 1] > 0) {
Results_mod[i, 6] = "A"
}
Results_mod[i, 2] = abs(Results[i, 2])
Results_mod[i, 3] = abs(Results[i, 3])
Results_mod[i, 5] = abs(Results[i, 1])
Results_mod[i, 7] = Results[i, 4]
Results_mod[i, 8] = Results[i, 5]
Results_mod[i, 9] = Results[i, 7]
Results_mod[i, 10] = Results[i, 6]
}
colnames(Results_mod) = c("more_effective_regimen", "median_effect",
"upper_bound", "upper_bound_regimen", "lower_bound",
"lower_bound_regimen", "b_clinically_better", "b_marginally_better",
"a_clinically_better", "a_marginally_better")
rownames(Results_mod) = c("pain", "fatigue", "drowsiness",
"sleep_problems", "thinking_problems", "constipation",
"neuropathic_pain")
graph_5 = list(more_effective_regimen = Results_mod[1, 1],
median_effect = as.numeric(Results_mod[1, 2]), upper_bound = as.numeric(Results_mod[1,
3]), upper_bound_regimen = Results_mod[1, 4], lower_bound = as.numeric(Results_mod[1,
5]), lower_bound_regimen = Results_mod[1, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[1,
7]), b_marginally_better = as.numeric(Results_mod[1,
8]), a_clinically_better = as.numeric(Results_mod[1,
9]), a_marginally_better = as.numeric(Results_mod[1,
10]))
pain = list(graph_5, graph_6)
names(pain) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[4, 1],
median_effect = as.numeric(Results_mod[4, 2]), upper_bound = as.numeric(Results_mod[4,
3]), upper_bound_regimen = Results_mod[4, 4], lower_bound = as.numeric(Results_mod[4,
5]), lower_bound_regimen = Results_mod[4, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[4,
7]), b_marginally_better = as.numeric(Results_mod[4,
8]), a_clinically_better = as.numeric(Results_mod[4,
9]), a_marginally_better = as.numeric(Results_mod[4,
10]))
sleep_problems = list(graph_5, graph_6)
names(sleep_problems) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[6, 1],
median_effect = as.numeric(Results_mod[6, 2]), upper_bound = as.numeric(Results_mod[6,
3]), upper_bound_regimen = Results_mod[6, 4], lower_bound = as.numeric(Results_mod[6,
5]), lower_bound_regimen = Results_mod[6, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[6,
7]), b_marginally_better = as.numeric(Results_mod[6,
8]), a_clinically_better = as.numeric(Results_mod[6,
9]), a_marginally_better = as.numeric(Results_mod[6,
10]))
constipation = list(graph_5, graph_6)
names(constipation) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[3, 1],
median_effect = as.numeric(Results_mod[3, 2]), upper_bound = as.numeric(Results_mod[3,
3]), upper_bound_regimen = Results_mod[3, 4], lower_bound = as.numeric(Results_mod[3,
5]), lower_bound_regimen = Results_mod[3, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[3,
7]), b_marginally_better = as.numeric(Results_mod[3,
8]), a_clinically_better = as.numeric(Results_mod[3,
9]), a_marginally_better = as.numeric(Results_mod[3,
10]))
drowsiness = list(graph_5, graph_6)
names(drowsiness) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[5, 1],
median_effect = as.numeric(Results_mod[5, 2]), upper_bound = as.numeric(Results_mod[5,
3]), upper_bound_regimen = Results_mod[5, 4], lower_bound = as.numeric(Results_mod[5,
5]), lower_bound_regimen = Results_mod[5, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[5,
7]), b_marginally_better = as.numeric(Results_mod[5,
8]), a_clinically_better = as.numeric(Results_mod[5,
9]), a_marginally_better = as.numeric(Results_mod[5,
10]))
thinking_problems = list(graph_5, graph_6)
names(thinking_problems) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[2, 1],
median_effect = as.numeric(Results_mod[2, 2]), upper_bound = as.numeric(Results_mod[2,
3]), upper_bound_regimen = Results_mod[2, 4], lower_bound = as.numeric(Results_mod[2,
5]), lower_bound_regimen = Results_mod[2, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[2,
7]), b_marginally_better = as.numeric(Results_mod[2,
8]), a_clinically_better = as.numeric(Results_mod[2,
9]), a_marginally_better = as.numeric(Results_mod[2,
10]))
fatigue = list(graph_5, graph_6)
names(fatigue) = c("graph_5", "graph_6")
graph_5 = list(more_effective_regimen = Results_mod[7, 1],
median_effect = as.numeric(Results_mod[7, 2]), upper_bound = as.numeric(Results_mod[7,
3]), upper_bound_regimen = Results_mod[7, 4], lower_bound = as.numeric(Results_mod[7,
5]), lower_bound_regimen = Results_mod[7, 6])
graph_6 = list(b_clinically_better = as.numeric(Results_mod[7,
7]), b_marginally_better = as.numeric(Results_mod[7,
8]), a_clinically_better = as.numeric(Results_mod[7,
9]), a_marginally_better = as.numeric(Results_mod[7,
10]))
neuropathic_pain = list(graph_5, graph_6)
names(neuropathic_pain) = c("graph_5", "graph_6")
y = list(pain, sleep_problems, constipation, drowsiness,
thinking_problems, fatigue, neuropathic_pain)
names(y) = c("pain", "sleep_problems", "constipation", "drowsiness",
"thinking_problems", "fatigue", "neuropathic_pain")
return(y)
}
|
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