-
Notifications
You must be signed in to change notification settings - Fork 3
/
AICc_table_generation.R
270 lines (171 loc) · 8.51 KB
/
AICc_table_generation.R
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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
## Create AICc tables for PERMANOVA output.
require(vegan)
require(tibble)
require(stringr)
## -- For two variables: ------------------------------------
AICc.table.2var <- function(sig.vars, control.var.char = NULL, c.var = 0, matrix.char, perm, type = "AICc", method = "bray") {
varcomb.2.AICc <- tibble(variables = rep("var.name", choose(length(sig.vars),2)),
AICc.values = rep(0),
`Pseudo-_F_` = rep(0),
`p-value` = rep(0),
`Var Explnd` = rep(0),
Model = rep("model"))
if (is.character(control.var.char) == TRUE & c.var == 0) {c.var = length(control.var.char)}
combo.list <- combn(x = sig.vars, m = 2, simplify = FALSE)
if (!is.null(control.var.char)) {
control.var.char <- paste0(control.var.char, " +")
}
for (r in 1:choose(length(sig.vars),2)) {
# label the row with variable names
varcomb.2.AICc[r,1] <- paste(control.var.char, paste(combo.list[[r]], collapse = " + "))
# create a temporary PERMANOVA to take info from
temp <- adonis2(
as.formula(paste0(
matrix.char,
" ~ ",
control.var.char,
paste0(combo.list[[r]], collapse = "+")
)),
permutations = perm,
method = method,
by = NULL
)
varcomb.2.AICc[r, 2] <- AICc.PERMANOVA2(temp)[type]
varcomb.2.AICc[r, 3] <- temp$F[1]
varcomb.2.AICc[r, 4] <- temp$`Pr(>F)`[1]
varcomb.2.AICc$`Var Explnd`[r] <- temp$SumOfSqs[1] / temp$SumOfSqs[3]
r <- r + 1
}
varcomb.2.AICc$`Delta AICc` <- varcomb.2.AICc$AICc.values -
min(varcomb.2.AICc$AICc.values)
varcomb.2.AICc$`Relative Likelihood` <-
exp(-.5 * (varcomb.2.AICc$AICc.values - min(varcomb.2.AICc$AICc.values)))
# Relative likelihood compared with best model; see
# https://en.wikipedia.org/wiki/Likelihood_function
# https://www.rdocumentation.org/packages/qpcR/versions/1.4-1/topics/akaike.weights
return(varcomb.2.AICc)
}
## -- For N variables: ---------------------------------------------------
AICc.table.Nvar <- function(sig.vars, control.var.char = NULL, c.var = 0, matrix.char, perm, n.var = 1, composite = FALSE, type = "AICc", method = "bray") {
if (n.var > length(sig.vars)) { stop("n.var greater than number of significant variables")}
if (!is.null(control.var.char)) {
control.var.char <- paste0(control.var.char, " + ")
}
if (is.character(control.var.char) == TRUE & c.var == 0) {c.var = 1}
varcomb.N.AICc <- tibble(variables = rep("var.name", choose(length(sig.vars), n.var)),
AICc.values = rep(0),
`Pseudo-_F_` = rep(0),
`p-value` = rep(0),
`Var Explnd` = rep(0),
Model = rep("model"))
combo.list <- combn(x = sig.vars, m = n.var, simplify = FALSE)
for (r in 1:choose(length(sig.vars), n.var)) {
# label the row with variable names
varcomb.N.AICc[r,1] <- paste(control.var.char, paste(combo.list[[r]], collapse = " and "))
# create a temporary PERMANOVA to take info from
temp <- adonis2(
as.formula(paste0(
matrix.char,
" ~ ",
control.var.char,
paste0(combo.list[[r]], collapse = "+")
)),
permutations = perm,
method = method,
by = NULL
)
varcomb.N.AICc[r,2] <- AICc.PERMANOVA2(temp)[type]
varcomb.N.AICc[r,3] <- temp$`F`[1]
varcomb.N.AICc[r,4] <- temp$`Pr(>F)`[1]
varcomb.N.AICc$`Var Explnd`[r] <- temp$SumOfSqs[1] / temp$SumOfSqs[3]
r <- r + 1
}
# Calculate diagnostic variables:
if (composite == FALSE) {
varcomb.N.AICc$`Delta AICc` <- varcomb.N.AICc$AICc.values -
min(varcomb.N.AICc$AICc.values)
varcomb.2.AICc$`Relative Likelihood` <-
exp(-.5 * (varcomb.2.AICc$AICc.values - min(varcomb.2.AICc$AICc.values)))
}
return(varcomb.N.AICc)
}
## -- Wrapper function: ---------------------------------------------------
# comb.incl is which # of variables for the combinations you want to include.
# e.g. all combinations of 2 variables, 3 variables, etc.
AICc.table.all <- function(sig.vars, control.var.char = NULL, matrix.char, perm = 999, comb.incl = 1, extra.var = FALSE, extra.var.char = NULL, type = "AICc", method = "bray") {
varcomb.all <- data.frame()
# If there is a control variable, create a one variable model with only
# control variable, for comparison with rest of proposed models
#
if (!is.null(control.var.char)) {
temp <- AICc.table.Nvar(sig.vars = control.var.char, control.var.char = NULL,
matrix.char = matrix.char, n.var = 1, composite = TRUE,
type = type, method = method, perm = perm)
varcomb.all <- rbind(varcomb.all, temp)
}
# Iterate through the comb.incl. e.g. all 1 var models, then 2 var models, then 3 var models...
for (i in comb.incl) {
temp <- AICc.table.Nvar(sig.vars = sig.vars, control.var.char = control.var.char,
matrix.char = matrix.char, n.var = i, composite = TRUE,
type = type, method = method, perm = perm)
varcomb.all <- rbind(varcomb.all, temp)
}
# If you want to include a non-significant variable for comparison...
if (extra.var == TRUE) {
for (i in 1:length(extra.var.char)) {
temp <- AICc.table.Nvar(sig.vars = extra.var.char[i], control.var.char = control.var.char,
matrix.char = matrix.char, n.var = 1, composite = TRUE,
type = type, method = method, perm = perm)
varcomb.all <- rbind(varcomb.all, temp)
}
}
varcomb.all$`Delta AICc` <- varcomb.all$AICc.values -
min(varcomb.all$AICc.values)
varcomb.all$`Relative Likelihood` <- exp((min(varcomb.all$AICc.values) -
varcomb.all$AICc.values)/2)
# exp( -0.5 * ∆AIC score for that model)
return(varcomb.all)
}
## -- Sum of AIC Weights by Var: ---------------------------------------------------
# This requires an AIC/AICc table output from one of the above functions. The
# rationalle behind this approach can be found in Arnold, T. W. (2010).
# Uninformative parameters and model selection using Akaike's Information
# Criterion. The Journal of Wildlife Management, 74(6), 1175-1178.
# Calculation method from http://brianomeara.info/tutorials/aic
AICc.weights.byvar <- function(sig.vars, AIC.table.output){
results.table <- tibble("Significant Variable" = sig.vars,
"Summed AIC Weight" = rep(0))
for (i in 1:length(sig.vars)){
summed.weight = 0
for (j in 1:nrow(AIC.table.output)){
if (grepl(AIC.table.output$variables[j], pattern = sig.vars[i], fixed = TRUE)) {
summed.weight <- summed.weight + AIC.table.output$`Relative Likelihood`[j]
} else summed.weight <- summed.weight
}
#sig vars loop
results.table[i, 2] <- summed.weight
}
# function loop
return(results.table)
}
# create a table with sig vars fed into the table + AIC weight sum column
#
# for each significant variable,
# for each row
# check each row to see if there is a pattern match
# add the relative likelihood to sum if so
#
# report
## Testing
# sig.vars <- sigvars.symbionfNGS[-1]
# matrix.char <- "sqrt(symbio.transpose.nf)"
# control.var.char <- sigvars.symbionfNGS[1]
#
#
# AICc.table.2var(sig.vars = sigvars.symbionfNGS[-1], control.var.char = sigvars.symbionfNGS[1], matrix.char = "sqrt(symbio.transpose.nf)")[3]
#
# AICc.table.Nvar(sig.vars = sigvars.symbionfNGS[-1], control.var.char = sigvars.symbionfNGS[1], matrix.char = "sqrt(symbio.transpose.nf)", n.var = 1)[3]
#
# test <- AICc.table.all(sig.vars = sigvars.symbionfNGS[-1], control.var.char = sigvars.symbionfNGS[1],
# matrix.char = "sqrt(symbio.transpose.nf)", comb.incl = 1)
#