lme_pqi | R Documentation |
Calculate PQI using linear mixed effect model from q2e estimates
lme_pqi(q2e_vals, logq = TRUE, g = NULL, outdir = NULL, return_model = F)
logq |
Whether q values are log-scaled before entering the LME model |
g |
Reliability power in the error normal distribution from linear mixed effects model |
outdir |
Optional. directory where results tables and plots are saved |
q2e |
data.frame with q2e estimations per sample, replicate and peptide |
list contaning the following:
sample: data.frame with aggregated PQI estimates per sample
pep: data.frame of fitted and residuals per replicate and peptide
estimates: list of model estimates
sample
data.frame has the following columns:
Sample: sample name
Prediction: sample random effects obtained from custom formula that. It is the log(PQI)
sd: log(PQI) standard error
RanefModel: sample random effects, as calculated by ranef
.
It should be the same as Prediction, up to numerical precision
PQI.Model, exponentials estimates. It is the PQI
pep
data.frame contains:
Sample, replicate and peptide IDs
q: is the q calculated by the WLS from the isotopic distributions
Reliability: minimum least square of q caluclated in the WLS step
Loqq: log(q)
resp: either q or log(q), according to whether logq was FALSE or TRUE
Fitted: fitted q value
Res: pearson residuals from Fitted
Fitted0: fitted q values at the peptide level. It's equal to the peptide fixed effect
Res0: pearson residuals from Fitted0
estimates
list contains
alpha: peptide fixed effects
sigma2_S: sample random effect variance
sigma2_R: replicate random effect variance
gamma: Reliability 2·gamma exponent
sigma2: peptide fixed effects variance
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