<b>HLMdiag</b>: A Suite of Diagnostics for Hierarchical Linear Models in<i>R</i>

Loy, Adam; Hofmann, Heike

doi:10.18637/jss.v056.i05

Cited by 59 publications

(48 citation statements)

References 39 publications

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“…lme4 does not provide influence diagnostics, but these (and other useful diagnostic procedures) are available in the dependent packages HLMdiag and influence.ME (Loy and Hofmann 2014;Nieuwenhuis, Te Grotenhuis, and Pelzer 2012).…”

Section: Diagnostic Plotsmentioning

confidence: 99%

Fitting Linear Mixed-Effects Models Usinglme4

Bates¹,

Mächler²,

Bolker³

et al. 2015

J. Stat. Soft.

70,177

50,877

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Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.

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Section: Diagnostic Plotsmentioning

confidence: 99%

Fitting Linear Mixed-Effects Models Usinglme4

Bates¹,

Mächler²,

Bolker³

et al. 2015

J. Stat. Soft.

70,177

50,877

View full text Add to dashboard Cite

show abstract

“…Secondary analyses were performed to estimate whether maternal CT exposure exerts effects over and above those of gestational conditions, including maternal socio-demographic, biophysical, obstetric, behavioral, and psychological factors. Model diagnostics were performed using the HLMdiag package (62). …”

Section: Methodsmentioning

confidence: 99%

Maternal Exposure to Childhood Trauma Is Associated During Pregnancy With Placental-Fetal Stress Physiology

Moog

Buß

Entringer

et al. 2016

Biological Psychiatry

104

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Background The effects of exposure to childhood trauma (CT) may be transmitted across generations, however the time period(s) and mechanism(s) have yet to be clarified. We address the hypothesis that intergenerational transmission may begin during intrauterine life via the effect of maternal CT exposure on placental-fetal stress physiology, specifically placental corticotrophin-releasing hormone (pCRH). Methods The study was conducted in a sociodemographically-diverse cohort of 295 pregnant women. CT exposure was assessed using the Childhood Trauma Questionnaire. Placental CRH concentrations were quantified in maternal blood collected serially over the course of gestation. Linear mixed effects and Bayesian piecewise linear models were employed to test hypothesized relationships. Results Maternal CT exposure (CT+) was significantly associated with pCRH production. Compared to non-exposed women, CT+ was associated with an almost 25% increase in pCRH towards the end of gestation, and the pCRH trajectory of CT+ women exhibited an approximately two-fold steeper increase after the pCRH inflection point at 19 wks gestation. Conclusions To the best of our knowledge, this finding represents the first report linking maternal CT exposure with placental-fetal stress physiology, thus identifying a potential novel biological pathway of intergenerational transmission that may operate as early as during intrauterine life.

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“…These low taxonomic level data sets were analysed with variance components analyses based on a maximum likelihood analysis of a nested design of random effects of genus, species-within-genera, and replicates-within-species. These analyses were implemented in LME4 (Douglas Bates et al ., 2014) and HLMdiag (Loy, 2014) in R. SE of variance components were estimated using the Mixed procedure of SAS 9.2 (SAS Institute Inc., Cary, NC, USA). For genome size, the replicates were the duplicate runs from the same plant.…”

Section: Methodsmentioning

confidence: 99%

Environmental adaptation in stomatal size independent of the effects of genome size

et al. 2014

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Cell sizes are linked across multiple tissues, including stomata, and this variation is closely correlated with genome size. These associations raise the question of whether generic changes in cell size cause suboptimal changes in stomata, requiring subsequent evolution under selection for stomatal size. We tested the relationships among guard cell length, genome size and vegetation type using phylogenetically independent analyses on 67 species of the ecologically and structurally diverse family, Proteaceae. We also compared how genome and stomatal sizes varied at ancient (among genera) and more recent (within genus) levels. The observed 60-fold range in genome size in Proteaceae largely reflected the mean chromosome size. Compared with variation among genera, genome size varied much less within genera (< 6% of total variance) than stomatal size, implying evolution in stomatal size subsequent to changes in genome size. Open vegetation and closed forest had significantly different relationships between stomatal and genome sizes. Ancient changes in genome size clearly influenced stomatal size in Proteaceae, but adaptation to habitat strongly modified the genome–stomatal size relationship. Direct adaptation to the environment in stomatal size argues that new proxies for past concentrations of atmospheric CO2 that incorporate stomatal size are superior to older models based solely on stomatal frequency.

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HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models inR

Cited by 59 publications

References 39 publications

Fitting Linear Mixed-Effects Models Usinglme4

Fitting Linear Mixed-Effects Models Usinglme4

Maternal Exposure to Childhood Trauma Is Associated During Pregnancy With Placental-Fetal Stress Physiology

Environmental adaptation in stomatal size independent of the effects of genome size

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