prm_waterfall | R Documentation |
Differences are second listed model minus first listed. Eg, in
eta_waterfall(run1,run2)
, the when etas in run2 are greater than
those in run1, the difference will be positive.
prm_waterfall(
xpdb_s,
...,
.inorder = FALSE,
type = "bh",
max_nind = 0.7,
scale_diff = TRUE,
show_n = TRUE,
title = "Parameter changes between models | @run",
subtitle = "Based on @nobs observations in @nind individuals",
caption = "@dir",
tag = NULL,
facets = NULL,
facet_scales = "free_x",
.problem,
.subprob,
.method,
quiet
)
eta_waterfall(
xpdb_s,
...,
.inorder = FALSE,
type = "bh",
max_nind = 0.7,
scale_diff = TRUE,
show_n = TRUE,
title = "Eta changes between models | @run",
subtitle = "Based on @nobs observations in @nind individuals",
caption = "@dir",
tag = NULL,
facets = NULL,
facet_scales = "free_x",
.problem,
.subprob,
.method,
quiet
)
iofv_waterfall(
xpdb_s,
...,
.inorder = FALSE,
type = "bh",
max_nind = 0.7,
scale_diff = FALSE,
show_n = TRUE,
title = "iOFV changes between models | @run",
subtitle = "Based on @nobs observations in @nind individuals",
caption = "@dir",
tag = NULL,
facets = NULL,
facet_scales = "free_x",
.problem,
.subprob,
.method,
quiet
)
xpdb_s |
< |
... |
See < |
.inorder |
See < |
type |
See Details. |
max_nind |
If less than 1, the percentile of absolute change values above which to plot. If above 1, the absolute number of subjects is included. To show all, use an extreme positive number like 9999. |
scale_diff |
< |
show_n |
< |
title |
Plot title |
subtitle |
Plot subtitle |
caption |
Plot caption |
tag |
Plot tag |
facets |
< |
facet_scales |
< |
.problem |
The problem to be used, by default returns the last one. |
.subprob |
The subproblem to be used, by default returns the last one. |
.method |
The estimation method to be used, by default returns the last one. |
quiet |
Silence extra debugging output |
For type-based customization of plots:
b
bar plot (from geom_bar
)
h
hline at 0 (from geom_hline
)
t
text of change value (from geom_text
)
<xpose_plot
> object
# Parameter value changes
pheno_set %>%
# Ensure param is set
focus_qapply(set_var_types, param=c(CL,V)) %>%
prm_waterfall(run5,run6)
# EBE value changes
pheno_set %>%
eta_waterfall(run5,run6)
# iOFV changes
pheno_set %>%
focus_qapply(backfill_iofv) %>%
# Note the default scaling is flipped here
iofv_waterfall(run5,run6)
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