diagnose_models {batchtma} | R Documentation |
Model diagnostics after batch adjustment
Description
After adjust_batch
has performed
adjustment for batch effects, diagnose_models
provides an overview of parameters and adjustment models.
Information is only available about the most recent
run of adjust_batch
on a dataset.
Usage
diagnose_models(data)
Arguments
data |
Batch-adjusted dataset (in which
|
Value
List:
-
adjust_method
Method used for batch adjustment (seeadjust_batch
). -
markers
Variables of biomarkers for adjustment -
suffix
Suffix appended to variable names -
batchvar
Variable indicating batch -
confounders
Confounders, i.e. determinants of biomarker levels that differ between batches. Returned only if used by the model. -
adjust_parameters
Tibble of parameters used to obtain adjust biomarker levels. Parameters differ between methods:-
simple
,standardize
, andipw
: Estimated adjustment parameters are a tibble with onebatchmean
permarker
and.batchvar
. -
quantreg
returns a tibble with numerous values permarker
and.batchvar
: unadjusted (un_...
) and adjusted (ad_...
) estimates of the lower (..._lo
) and upper quantile (..._hi
) and interquantile range (..._iq
), plus the lower (all_lo
) and upper quantiles (all_hi
) across all batches. -
quantnorm
does not explicitly estimate parameters.
-
-
model_fits
List of model fit objects, one per biomarker. Models differ between methods:-
standardize
: Linear regression model for the biomarker with.batchvar
andconfounders
as predictors, from which marginal predictions of batch means for each batch are obtained. -
ipw
: Logistic (2 batches) or multinomial models for assignment to a specific batch with.batchvar
as the response andconfounders
as the predictors, used to generate stabilized inverse-probability weights that are then used in a linear regression model to estimate marginally standardized batch means. -
quantreg
: Quantile regression with the marker as the response variable and.batchvar
andconfounders
as predictors. -
simple
andquantnorm
do not fit any regression models.
-
Examples
# Data frame with two batches
# Batch 2 has higher values of biomarker and confounder
df <- data.frame(
tma = rep(1:2, times = 10),
biomarker = rep(1:2, times = 10) +
runif(max = 5, n = 20),
confounder = rep(0:1, times = 10) +
runif(max = 10, n = 20)
)
# Adjust for batch effects
df2 <- adjust_batch(
data = df,
markers = biomarker,
batch = tma,
method = quantreg,
confounders = confounder
)
# Show overview of model diagnostics:
diagnose_models(data = df2)
# Obtain first fitted regression model:
fit <- diagnose_models(data = df2)$model_fits[[1]][[1]]
# Obtain residuals for this model:
residuals(fit)