multiple-components
Oliver Jayasinghe and Rex Parsons
Source:vignettes/multiple-components.Rmd
multiple-components.Rmd
GLMMcosinor allows specification of multi-component cosinor models. This is useful if there are multiple explanatory variables with known periods affecting the response variable.
Generating a two-component model
To generate a multi-component model, set n_components
in
the amp_acro()
part of the formula to the desired number of
components. Then, optionally assign groups to each component in the
group
argument. If only one group entry is supplied but
n_components
is greater than 1, then the single group entry
will be matched to each component.
The period
argument must also match the length of
n_components
, where the order of the periods corresponds to
their assigned component. For example, if n_components = 2
,
and period = c(12,6)
, then the first component has a
period
of 12 and the second a period of 6. Similarly to the
group
argument, if only one period is supplied despite
n_components
being greater than 1, then this period will be
matched to each component.
For example:
library(GLMMcosinor)
testdata_two_components <- simulate_cosinor(
1000,
n_period = 10,
mesor = 7,
amp = c(0.1, 0.4),
acro = c(1, 1.5),
beta.mesor = 4.4,
beta.amp = c(2, 1),
beta.acro = c(1, -1.5),
family = "poisson",
period = c(12, 6),
n_components = 2,
beta.group = TRUE
)
object <- cglmm(
Y ~ group + amp_acro(
time_col = times,
n_components = 2,
period = c(12, 6),
group = c("group", "group")
),
data = testdata_two_components,
family = poisson()
)
object
#>
#> Conditional Model
#>
#> Raw formula:
#> Y ~ group + group:main_rrr1 + group:main_sss1 + group:main_rrr2 + group:main_sss2
#>
#> Raw Coefficients:
#> Estimate
#> (Intercept) 6.99894
#> group1 -2.60342
#> group0:main_rrr1 0.05248
#> group1:main_rrr1 1.08250
#> group0:main_sss1 0.08753
#> group1:main_sss1 1.68129
#> group0:main_rrr2 0.02926
#> group1:main_rrr2 0.06860
#> group0:main_sss2 0.40068
#> group1:main_sss2 -0.99822
#>
#> Transformed Coefficients:
#> Estimate
#> (Intercept) 6.99894
#> [group=1] -2.60342
#> [group=0]:amp1 0.10205
#> [group=1]:amp1 1.99964
#> [group=0]:amp2 0.40175
#> [group=1]:amp2 1.00057
#> [group=0]:acr1 1.03070
#> [group=1]:acr1 0.99875
#> [group=0]:acr2 1.49790
#> [group=1]:acr2 -1.50218
In the output, the suffix on the estimates for amplitude and acrophase represents its component:
[group=0]:amp1 = 0.10205
represents the estimate for amplitude ofgroup 0
for the first component[group=1]:amp1 = 1.99964
represents the estimate for amplitude ofgroup 1
for the first component[group=0]:amp2 = 0.40175
represnts the estimate for amplitude ofgroup 0
for the second component[group=1]:amp2 = 1.00057
represnts the estimate for amplitude ofgroup 1
for the second componentSimilarly for acrophase estimates
autoplot(object)
If a multicomponent model has one component that is grouped with
other components that aren’t, the vector input for group
must still be the same length as n_components
but have the
non-grouped components represented as group = NA
.
For example, if wanted only the first component to have a grouped
component, we would specify the group
argument as
group = c("group", NA))
. Here, the first component is
grouped by group
, and the second component is not grouped.
The data was simulated such that the second component was the same for
both groups.
testdata_two_components_grouped <- simulate_cosinor(
1000,
n_period = 5,
mesor = 3.7,
amp = c(0.1, 0.4),
acro = c(1, 1.5),
beta.mesor = 4,
beta.amp = c(2, 0.4),
beta.acro = c(1, 1.5),
family = "poisson",
period = c(12, 6),
n_components = 2,
beta.group = TRUE
)
object <- cglmm(
Y ~ group + amp_acro(
time_col = times,
n_components = 2,
period = c(12, 6),
group = c("group", NA)
),
data = testdata_two_components_grouped,
family = poisson()
)
object
#>
#> Conditional Model
#>
#> Raw formula:
#> Y ~ group + main_rrr2 + main_sss2 + group:main_rrr1 + group:main_sss1
#>
#> Raw Coefficients:
#> Estimate
#> (Intercept) 3.69558
#> group1 0.31184
#> main_rrr2 0.02612
#> main_sss2 0.39710
#> group0:main_rrr1 0.04946
#> group1:main_rrr1 1.07681
#> group0:main_sss1 0.09546
#> group1:main_sss1 1.67644
#>
#> Transformed Coefficients:
#> Estimate
#> (Intercept) 3.69558
#> [group=1] 0.31184
#> [group=0]:amp1 0.10752
#> [group=1]:amp1 1.99248
#> amp2 0.39795
#> [group=0]:acr1 1.09273
#> [group=1]:acr1 0.99984
#> acr2 1.50512
We would interpret the output the transformed coefficients as follows:
MESOR for
group 0
is3.69558
.MESOR difference to
group 0
forgroup 1
is[group=1] = 0.31184
The estimate for the amplitude of the first component for
group 0
is[group=0]:amp1 = 0.10752
The estimate for the amplitude of the first component for
group 1
is[group=1]:amp1 = 1.99248
The estimate for the amplitude of the second component is
amp2 = 0.39795
and the same for bothgroup 0
andgroup 1
The estimate for the acrophase of the first component for
group 0
is[group=0]:acr1 = 1.09273
radiansThe estimate for the acrophase of the first component for
group 1
is[group=1]:acr1 = 0.99984
radiansThe estimate for the acrophase of the second component is
acr2 = 1.50512
radians and is the same for bothgroup 0
andgroup 1
autoplot(object, superimpose.data = TRUE)
In this example, it is not strictly necessary to specify
group = c("group", NA))
since specifying
group = c("group","group")
still yields accurate
estimates:
object <- cglmm(
Y ~ group + amp_acro(
time_col = times,
n_components = 2,
period = c(12, 6),
group = c("group", "group")
),
data = testdata_two_components_grouped,
family = poisson()
)
object
#>
#> Conditional Model
#>
#> Raw formula:
#> Y ~ group + group:main_rrr1 + group:main_sss1 + group:main_rrr2 + group:main_sss2
#>
#> Raw Coefficients:
#> Estimate
#> (Intercept) 3.69549
#> group1 0.31048
#> group0:main_rrr1 0.05027
#> group1:main_rrr1 1.07082
#> group0:main_sss1 0.09515
#> group1:main_sss1 1.68461
#> group0:main_rrr2 0.01368
#> group1:main_rrr2 0.03613
#> group0:main_sss2 0.39617
#> group1:main_sss2 0.39776
#>
#> Transformed Coefficients:
#> Estimate
#> (Intercept) 3.69549
#> [group=1] 0.31048
#> [group=0]:amp1 0.10761
#> [group=1]:amp1 1.99614
#> [group=0]:amp2 0.39641
#> [group=1]:amp2 0.39939
#> [group=0]:acr1 1.08472
#> [group=1]:acr1 1.00457
#> [group=0]:acr2 1.53629
#> [group=1]:acr2 1.48022
If a multicomponent model is specified
(n_components > 1
) but the length of group
or period
is 1, then it will be assumed that the one
group
and/or period
values specified apply to
all components. For example, if n_components = 2
,
but group = "group"
, then the one element in this
group
vector will be replicated to produce
group = c("group","group")
which now has a length that
matches n_components
. The same applies for
period
.
For instance, the following two cglmm()
calls fit the
same models:
cglmm(
Y ~ group + amp_acro(times,
n_components = 2,
period = 12,
group = "group"
),
data = testdata_two_components,
family = poisson()
)
#>
#> Conditional Model
#>
#> Raw formula:
#> Y ~ group + group:main_rrr1 + group:main_sss1 + group:main_rrr2 + group:main_sss2
#>
#> Raw Coefficients:
#> Estimate
#> (Intercept) 7.04448
#> group1 -2.22027
#> group0:main_rrr1 0.07222
#> group1:main_rrr1 0.39492
#> group0:main_sss1 0.11292
#> group1:main_sss1 1.11176
#> group0:main_rrr2 NA
#> group1:main_rrr2 NA
#> group0:main_sss2 NA
#> group1:main_sss2 NA
#>
#> Transformed Coefficients:
#> Estimate
#> (Intercept) 7.04448
#> [group=1] -2.22027
#> [group=0]:amp1 0.13404
#> [group=1]:amp1 1.17982
#> [group=0]:amp2 NA
#> [group=1]:amp2 NA
#> [group=0]:acr1 1.00181
#> [group=1]:acr1 1.22947
#> [group=0]:acr2 NA
#> [group=1]:acr2 NA
cglmm(
Y ~ group + amp_acro(times,
n_components = 2,
period = c(12, 12),
group = c("group", "group")
),
data = testdata_two_components,
family = poisson()
)
#>
#> Conditional Model
#>
#> Raw formula:
#> Y ~ group + group:main_rrr1 + group:main_sss1 + group:main_rrr2 + group:main_sss2
#>
#> Raw Coefficients:
#> Estimate
#> (Intercept) 7.04448
#> group1 -2.22027
#> group0:main_rrr1 0.07222
#> group1:main_rrr1 0.39492
#> group0:main_sss1 0.11292
#> group1:main_sss1 1.11176
#> group0:main_rrr2 NA
#> group1:main_rrr2 NA
#> group0:main_sss2 NA
#> group1:main_sss2 NA
#>
#> Transformed Coefficients:
#> Estimate
#> (Intercept) 7.04448
#> [group=1] -2.22027
#> [group=0]:amp1 0.13404
#> [group=1]:amp1 1.17982
#> [group=0]:amp2 NA
#> [group=1]:amp2 NA
#> [group=0]:acr1 1.00181
#> [group=1]:acr1 1.22947
#> [group=0]:acr2 NA
#> [group=1]:acr2 NA
Generating a three-component model
The plot below shows a 3-component model with the simulated data overlayed: