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
|
#' Helper functions to create nlme models from mmkin row objects
#'
#' These functions facilitate setting up a nonlinear mixed effects model for
#' an mmkin row object. An mmkin row object is essentially a list of mkinfit
#' objects that have been obtained by fitting the same model to a list of
#' datasets.
#'
#' @param object An mmkin row object containing several fits of the same model to different datasets
#' @import nlme
#' @rdname nlme
#' @seealso \code{\link{nlme.mmkin}}
#' @examples
#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
#' m_SFO <- mkinmod(parent = mkinsub("SFO"))
#' d_SFO_1 <- mkinpredict(m_SFO,
#' c(k_parent_sink = 0.1),
#' c(parent = 98), sampling_times)
#' d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
#' d_SFO_2 <- mkinpredict(m_SFO,
#' c(k_parent_sink = 0.05),
#' c(parent = 102), sampling_times)
#' d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
#' d_SFO_3 <- mkinpredict(m_SFO,
#' c(k_parent_sink = 0.02),
#' c(parent = 103), sampling_times)
#' d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
#'
#' d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
#' d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
#' d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
#' ds <- c(d1 = d1, d2 = d2, d3 = d3)
#'
#' f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
#' mean_dp <- mean_degparms(f)
#' grouped_data <- nlme_data(f)
#' nlme_f <- nlme_function(f)
#' # These assignments are necessary for these objects to be
#' # visible to nlme and augPred when evaluation is done by
#' # pkgdown to generated the html docs.
#' assign("nlme_f", nlme_f, globalenv())
#' assign("grouped_data", grouped_data, globalenv())
#'
#' library(nlme)
#' m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
#' data = grouped_data,
#' fixed = parent_0 + log_k_parent_sink ~ 1,
#' random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
#' start = mean_dp)
#' summary(m_nlme)
#' plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
#' # augPred does not seem to work on fits with more than one state
#' # variable
#'
#' @return A function that can be used with nlme
#' @export
nlme_function <- function(object) {
if (nrow(object) > 1) stop("Only row objects allowed")
mkin_model <- object[[1]]$mkinmod
degparm_names <- names(mean_degparms(object))
# Inspired by https://stackoverflow.com/a/12983961/3805440
# and https://stackoverflow.com/a/26280789/3805440
model_function_alist <- replicate(length(degparm_names) + 2, substitute())
names(model_function_alist) <- c("name", "time", degparm_names)
model_function_body <- quote({
arg_frame <- as.data.frame(as.list((environment())), stringsAsFactors = FALSE)
res_frame <- arg_frame[1:2]
parm_frame <- arg_frame[-(1:2)]
parms_unique <- unique(parm_frame)
n_unique <- nrow(parms_unique)
times_ds <- list()
names_ds <- list()
for (i in 1:n_unique) {
times_ds[[i]] <-
arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"]
names_ds[[i]] <-
arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"]
}
res_list <- lapply(1:n_unique, function(x) {
transparms_optim <- unlist(parms_unique[x, , drop = TRUE])
parms_fixed <- object[[1]]$bparms.fixed
odeini_optim_parm_names <- grep('_0$', names(transparms_optim), value = TRUE)
odeini_optim <- transparms_optim[odeini_optim_parm_names]
names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names)
odeini_fixed_parm_names <- grep('_0$', names(parms_fixed), value = TRUE)
odeini_fixed <- parms_fixed[odeini_fixed_parm_names]
names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names)
odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names)
odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,
transform_rates = object[[1]]$transform_rates,
transform_fractions = object[[1]]$transform_fractions)
odeparms_fixed_names <- setdiff(names(parms_fixed), odeini_fixed_parm_names)
odeparms_fixed <- parms_fixed[odeparms_fixed_names]
odeparms <- c(odeparms_optim, odeparms_fixed)
out_wide <- mkinpredict(mkin_model,
odeparms = odeparms, odeini = odeini,
solution_type = object[[1]]$solution_type,
outtimes = sort(unique(times_ds[[x]])))
out_array <- out_wide[, -1, drop = FALSE]
rownames(out_array) <- as.character(unique(times_ds[[x]]))
out_times <- as.character(times_ds[[x]])
out_names <- as.character(names_ds[[x]])
out_values <- mapply(function(times, names) out_array[times, names],
out_times, out_names)
return(as.numeric(out_values))
})
res <- unlist(res_list)
return(res)
})
model_function <- as.function(c(model_function_alist, model_function_body))
return(model_function)
}
#' @rdname nlme
#' @return If random is FALSE (default), a named vector containing mean values
#' of the fitted degradation model parameters. If random is TRUE, a list with
#' fixed and random effects, in the format required by the start argument of
#' nlme for the case of a single grouping variable ds?
#' @param random Should a list with fixed and random effects be returned?
#' @export
mean_degparms <- function(object, random = FALSE) {
if (nrow(object) > 1) stop("Only row objects allowed")
degparm_mat_trans <- sapply(object, parms, transformed = TRUE)
mean_degparm_names <- setdiff(rownames(degparm_mat_trans), names(object[[1]]$errparms))
fixed <- apply(degparm_mat_trans[mean_degparm_names, ], 1, mean)
if (random) {
degparm_mat_trans[mean_degparm_names, ]
random <- t(apply(degparm_mat_trans[mean_degparm_names, ], 2, function(column) column - fixed))
rownames(random) <- as.character(1:nrow(random))
return(list(fixed = fixed, random = list(ds = random)))
} else {
return(fixed)
}
}
#' @rdname nlme
#' @importFrom purrr map_dfr
#' @return A \code{\link{groupedData}} object
#' @export
nlme_data <- function(object) {
if (nrow(object) > 1) stop("Only row objects allowed")
ds_names <- colnames(object)
ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
names(ds_list) <- ds_names
ds_nlme <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
ds_nlme$variable <- as.character(ds_nlme$variable)
ds_nlme$ds <- ordered(ds_nlme$ds, levels = unique(ds_nlme$ds))
ds_nlme_renamed <- data.frame(ds = ds_nlme$ds, name = ds_nlme$variable,
time = ds_nlme$time, value = ds_nlme$observed,
stringsAsFactors = FALSE)
ds_nlme_grouped <- groupedData(value ~ time | ds, ds_nlme_renamed, order.groups = FALSE)
return(ds_nlme_grouped)
}
|
