diversity_table_compare: this function
In dlemas/microbes: tools for analyzing microbiome data
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
[text]
Usage
1
diversity_table_compare(meta_group, div_table)
Arguments
meta_group
[text]
div_table
[text]
Details
[text]
Value
[text]
Note
[text]
Author(s)
[text]
References
[text]
See Also
[text]
Examples
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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 (meta_group, div_table)
{
table1.df = table_group_compare(meta_group)
variable = length(unique(meta_group))
group.switch = paste("T", variable, sep = "")
switch(group.switch, T2 = {
div.names = names(div_table)
remove = c("participant", "group")
index = div.names[!div.names %in% remove]
myIndex <- length(index)
myIndex
for (i in 1:myIndex) {
div = index[i]
div_table.grp1 = subset(div_table, group == 1)
div_table.grp2 = subset(div_table, group == 2)
all_mean = round(mean(div_table[, div], na.rm = T),
digits = 2)
all_sd = round(sd(div_table[, div], na.rm = T), digits = 2)
all_mean_sd = paste("(", all_mean, " ± ", all_sd,
")", sep = "")
grp1_mean = round(mean(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_sd = round(sd(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_mean_sd = paste("(", grp1_mean, " ± ", grp1_sd,
")", sep = "")
grp2_mean = round(mean(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_sd = round(sd(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_mean_sd = paste("(", grp2_mean, " ± ", grp2_sd,
")", sep = "")
stat = summary(aov(div_table[, div] ~ div_table$group))
df = stat[[1]][[1]][[2]]
t.stat = round(stat[[1]][[4]][[1]], digits = 2)
p.value = round(stat[[1]][[5]][[1]], digits = 3)
table1.df[i, 1] = div
table1.df[i, 2] = all_mean
table1.df[i, 3] = all_sd
table1.df[i, 4] = all_mean_sd
table1.df[i, 5] = grp1_mean
table1.df[i, 6] = grp1_sd
table1.df[i, 7] = grp1_mean_sd
table1.df[i, 8] = grp2_mean
table1.df[i, 9] = grp2_sd
table1.df[i, 10] = grp2_mean_sd
table1.df[i, 11] = t.stat
table1.df[i, 12] = df
table1.df[i, 13] = p.value
}
}, T3 = {
div.names = names(div_table)
remove = c("participant", "group")
index = div.names[!div.names %in% remove]
myIndex <- length(index)
myIndex
for (i in 1:myIndex) {
div = index[i]
div_table.grp1 = subset(div_table, group == 1)
div_table.grp2 = subset(div_table, group == 2)
div_table.grp3 = subset(div_table, group == 3)
all_mean = round(mean(div_table[, div], na.rm = T),
digits = 2)
all_sd = round(sd(div_table[, div], na.rm = T), digits = 2)
all_mean_sd = paste("(", all_mean, " ± ", all_sd,
")", sep = "")
grp1_mean = round(mean(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_sd = round(sd(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_mean_sd = paste("(", grp1_mean, " ± ", grp1_sd,
")", sep = "")
grp2_mean = round(mean(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_sd = round(sd(div_table.grp3[, div], na.rm = T),
digits = 2)
grp2_mean_sd = paste("(", grp2_mean, " ± ", grp2_sd,
")", sep = "")
grp3_mean = round(mean(div_table.grp3[, div], na.rm = T),
digits = 2)
grp3_sd = round(sd(div_table.grp2[, div], na.rm = T),
digits = 2)
grp3_mean_sd = paste("(", grp3_mean, " ± ", grp3_sd,
")", sep = "")
stat = summary(aov(div_table[, div] ~ div_table$group))
df = stat[[1]][[1]][[2]]
t.stat = round(stat[[1]][[4]][[1]], digits = 2)
p.value = round(stat[[1]][[5]][[1]], digits = 3)
table1.df[i, 1] = div
table1.df[i, 2] = all_mean
table1.df[i, 3] = all_sd
table1.df[i, 4] = all_mean_sd
table1.df[i, 5] = grp1_mean
table1.df[i, 6] = grp1_sd
table1.df[i, 7] = grp1_mean_sd
table1.df[i, 8] = grp2_mean
table1.df[i, 9] = grp2_sd
table1.df[i, 10] = grp2_mean_sd
table1.df[i, 11] = grp3_mean
table1.df[i, 12] = grp3_sd
table1.df[i, 13] = grp3_mean_sd
table1.df[i, 14] = t.stat
table1.df[i, 15] = df
table1.df[i, 16] = p.value
}
}, T4 = {
div.names = names(div_table)
remove = c("participant", "group")
index = div.names[!div.names %in% remove]
myIndex <- length(index)
myIndex
for (i in 1:myIndex) {
div = index[i]
div_table.grp1 = subset(div_table, group == 1)
div_table.grp2 = subset(div_table, group == 2)
div_table.grp3 = subset(div_table, group == 3)
div_table.grp4 = subset(div_table, group == 4)
all_mean = round(mean(div_table[, div], na.rm = T),
digits = 2)
all_sd = round(sd(div_table[, div], na.rm = T), digits = 2)
all_mean_sd = paste("(", all_mean, " ± ", all_sd,
")", sep = "")
grp1_mean = round(mean(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_sd = round(sd(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_mean_sd = paste("(", grp1_mean, " ± ", grp1_sd,
")", sep = "")
grp2_mean = round(mean(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_sd = round(sd(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_mean_sd = paste("(", grp2_mean, " ± ", grp2_sd,
")", sep = "")
grp3_mean = round(mean(div_table.grp3[, div], na.rm = T),
digits = 2)
grp3_sd = round(sd(div_table.grp3[, div], na.rm = T),
digits = 2)
grp3_mean_sd = paste("(", grp3_mean, " ± ", grp3_sd,
")", sep = "")
grp4_mean = round(mean(div_table.grp4[, div], na.rm = T),
digits = 2)
grp4_sd = round(sd(div_table.grp4[, div], na.rm = T),
digits = 2)
grp4_mean_sd = paste("(", grp4_mean, " ± ", grp4_sd,
")", sep = "")
stat = summary(aov(div_table[, div] ~ div_table$group))
df = stat[[1]][[1]][[2]]
t.stat = round(stat[[1]][[4]][[1]], digits = 2)
p.value = round(stat[[1]][[5]][[1]], digits = 3)
table1.df[i, 1] = div
table1.df[i, 2] = all_mean
table1.df[i, 3] = all_sd
table1.df[i, 4] = all_mean_sd
table1.df[i, 5] = grp1_mean
table1.df[i, 6] = grp1_sd
table1.df[i, 7] = grp1_mean_sd
table1.df[i, 8] = grp2_mean
table1.df[i, 9] = grp2_sd
table1.df[i, 10] = grp2_mean_sd
table1.df[i, 11] = grp3_mean
table1.df[i, 12] = grp3_sd
table1.df[i, 13] = grp3_mean_sd
table1.df[i, 14] = grp4_mean
table1.df[i, 15] = grp4_sd
table1.df[i, 16] = grp4_mean_sd
table1.df[i, 17] = t.stat
table1.df[i, 18] = df
table1.df[i, 19] = p.value
}
}, stop("This function can't accomodate when meta_group>4!"))
return(table1.df)
}
dlemas/microbes documentation built on May 15, 2019, 9:15 a.m.
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Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
[text]
Usage
1 | diversity_table_compare(meta_group, div_table)
|
Arguments
meta_group |
[text] |
div_table |
[text] |
Details
[text]
Value
[text]
Note
[text]
Author(s)
[text]
References
[text]
See Also
[text]
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 | ##---- 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 (meta_group, div_table)
{
table1.df = table_group_compare(meta_group)
variable = length(unique(meta_group))
group.switch = paste("T", variable, sep = "")
switch(group.switch, T2 = {
div.names = names(div_table)
remove = c("participant", "group")
index = div.names[!div.names %in% remove]
myIndex <- length(index)
myIndex
for (i in 1:myIndex) {
div = index[i]
div_table.grp1 = subset(div_table, group == 1)
div_table.grp2 = subset(div_table, group == 2)
all_mean = round(mean(div_table[, div], na.rm = T),
digits = 2)
all_sd = round(sd(div_table[, div], na.rm = T), digits = 2)
all_mean_sd = paste("(", all_mean, " ± ", all_sd,
")", sep = "")
grp1_mean = round(mean(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_sd = round(sd(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_mean_sd = paste("(", grp1_mean, " ± ", grp1_sd,
")", sep = "")
grp2_mean = round(mean(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_sd = round(sd(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_mean_sd = paste("(", grp2_mean, " ± ", grp2_sd,
")", sep = "")
stat = summary(aov(div_table[, div] ~ div_table$group))
df = stat[[1]][[1]][[2]]
t.stat = round(stat[[1]][[4]][[1]], digits = 2)
p.value = round(stat[[1]][[5]][[1]], digits = 3)
table1.df[i, 1] = div
table1.df[i, 2] = all_mean
table1.df[i, 3] = all_sd
table1.df[i, 4] = all_mean_sd
table1.df[i, 5] = grp1_mean
table1.df[i, 6] = grp1_sd
table1.df[i, 7] = grp1_mean_sd
table1.df[i, 8] = grp2_mean
table1.df[i, 9] = grp2_sd
table1.df[i, 10] = grp2_mean_sd
table1.df[i, 11] = t.stat
table1.df[i, 12] = df
table1.df[i, 13] = p.value
}
}, T3 = {
div.names = names(div_table)
remove = c("participant", "group")
index = div.names[!div.names %in% remove]
myIndex <- length(index)
myIndex
for (i in 1:myIndex) {
div = index[i]
div_table.grp1 = subset(div_table, group == 1)
div_table.grp2 = subset(div_table, group == 2)
div_table.grp3 = subset(div_table, group == 3)
all_mean = round(mean(div_table[, div], na.rm = T),
digits = 2)
all_sd = round(sd(div_table[, div], na.rm = T), digits = 2)
all_mean_sd = paste("(", all_mean, " ± ", all_sd,
")", sep = "")
grp1_mean = round(mean(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_sd = round(sd(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_mean_sd = paste("(", grp1_mean, " ± ", grp1_sd,
")", sep = "")
grp2_mean = round(mean(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_sd = round(sd(div_table.grp3[, div], na.rm = T),
digits = 2)
grp2_mean_sd = paste("(", grp2_mean, " ± ", grp2_sd,
")", sep = "")
grp3_mean = round(mean(div_table.grp3[, div], na.rm = T),
digits = 2)
grp3_sd = round(sd(div_table.grp2[, div], na.rm = T),
digits = 2)
grp3_mean_sd = paste("(", grp3_mean, " ± ", grp3_sd,
")", sep = "")
stat = summary(aov(div_table[, div] ~ div_table$group))
df = stat[[1]][[1]][[2]]
t.stat = round(stat[[1]][[4]][[1]], digits = 2)
p.value = round(stat[[1]][[5]][[1]], digits = 3)
table1.df[i, 1] = div
table1.df[i, 2] = all_mean
table1.df[i, 3] = all_sd
table1.df[i, 4] = all_mean_sd
table1.df[i, 5] = grp1_mean
table1.df[i, 6] = grp1_sd
table1.df[i, 7] = grp1_mean_sd
table1.df[i, 8] = grp2_mean
table1.df[i, 9] = grp2_sd
table1.df[i, 10] = grp2_mean_sd
table1.df[i, 11] = grp3_mean
table1.df[i, 12] = grp3_sd
table1.df[i, 13] = grp3_mean_sd
table1.df[i, 14] = t.stat
table1.df[i, 15] = df
table1.df[i, 16] = p.value
}
}, T4 = {
div.names = names(div_table)
remove = c("participant", "group")
index = div.names[!div.names %in% remove]
myIndex <- length(index)
myIndex
for (i in 1:myIndex) {
div = index[i]
div_table.grp1 = subset(div_table, group == 1)
div_table.grp2 = subset(div_table, group == 2)
div_table.grp3 = subset(div_table, group == 3)
div_table.grp4 = subset(div_table, group == 4)
all_mean = round(mean(div_table[, div], na.rm = T),
digits = 2)
all_sd = round(sd(div_table[, div], na.rm = T), digits = 2)
all_mean_sd = paste("(", all_mean, " ± ", all_sd,
")", sep = "")
grp1_mean = round(mean(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_sd = round(sd(div_table.grp1[, div], na.rm = T),
digits = 2)
grp1_mean_sd = paste("(", grp1_mean, " ± ", grp1_sd,
")", sep = "")
grp2_mean = round(mean(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_sd = round(sd(div_table.grp2[, div], na.rm = T),
digits = 2)
grp2_mean_sd = paste("(", grp2_mean, " ± ", grp2_sd,
")", sep = "")
grp3_mean = round(mean(div_table.grp3[, div], na.rm = T),
digits = 2)
grp3_sd = round(sd(div_table.grp3[, div], na.rm = T),
digits = 2)
grp3_mean_sd = paste("(", grp3_mean, " ± ", grp3_sd,
")", sep = "")
grp4_mean = round(mean(div_table.grp4[, div], na.rm = T),
digits = 2)
grp4_sd = round(sd(div_table.grp4[, div], na.rm = T),
digits = 2)
grp4_mean_sd = paste("(", grp4_mean, " ± ", grp4_sd,
")", sep = "")
stat = summary(aov(div_table[, div] ~ div_table$group))
df = stat[[1]][[1]][[2]]
t.stat = round(stat[[1]][[4]][[1]], digits = 2)
p.value = round(stat[[1]][[5]][[1]], digits = 3)
table1.df[i, 1] = div
table1.df[i, 2] = all_mean
table1.df[i, 3] = all_sd
table1.df[i, 4] = all_mean_sd
table1.df[i, 5] = grp1_mean
table1.df[i, 6] = grp1_sd
table1.df[i, 7] = grp1_mean_sd
table1.df[i, 8] = grp2_mean
table1.df[i, 9] = grp2_sd
table1.df[i, 10] = grp2_mean_sd
table1.df[i, 11] = grp3_mean
table1.df[i, 12] = grp3_sd
table1.df[i, 13] = grp3_mean_sd
table1.df[i, 14] = grp4_mean
table1.df[i, 15] = grp4_sd
table1.df[i, 16] = grp4_mean_sd
table1.df[i, 17] = t.stat
table1.df[i, 18] = df
table1.df[i, 19] = p.value
}
}, stop("This function can't accomodate when meta_group>4!"))
return(table1.df)
}
|
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