I am trying to use a for loop to fit a model to each of my metabolites. There are 790 columns I need to apply this to. The output of the results are three values ( estimate, std.error and p value), and my empty matrix has 790 rows and three columns for the results to be entered into. (Therefore, I aim to get an estimate, std.error and p value for each of the 790 metabolites, in order to be able to gauge whether there are increases or decreases comparing control relative to disease, and whether there is any statistical significance).
Please find below the code I have tried so far, any suggestions would be greatly appreciated.
results.out <- matrix(0, nrow=790, ncol=3)
require(lme4)
require(lmerTest)
for(i in 1:ncol(data[, 8:792])){
fit <- lmer (data1[,i] ~ Diseasestatus + BB + ACOG + WA + BMI + Age + (1|ParticpantID), data=data, REML=F, na.action=na.omit)
results<- summary(fit)$coef[2, c(1, 2, 5)][i]
results.out[, i] <- results
}
The error message I get with the above is
Error in eval_f(x, ...) : Downdated VtV is not positive definite
( not sure whether this might be due to the presence of 0 and 1 in some columns. For instance disease is 1 and control is 0. Taking the medication BB, ACOG, WA is denoted 1, not taking it is 0.
or trying one of the apply functions also gets an error
output <- apply(data[,8:792], 2, function(i){
fit <- lmer (data[,i] ~ Diseasestatus + BB + ACOG + WA + BMI + Age + (1|ParticpantID), data=data, REML=F, na.action=na.omit)
results<- summary(fit)$coef[2, c(1, 2, 5)][i]
})
dplyr::bind_rows(output, .id="Metabolite")
The error message from the above is;
Error in model.frame.default(data = data, na.action =
na.omit, :
invalid type (list) for variable 'data[, i]'
A snapshot of my data in case that is useful can be found below;
structure(list(Participant_ID = c(34L, 35L, 119L, 157L, 158L,
208L, 209L, 1364L, 1365L, 127911L, 127912L, 154110L, 154120L,
167113L, 167123L, 171713L, 171724L, 184212L, 184213L), BB = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 1L), ACOG = c(1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L), WA = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), BMI =
c(23.94688606,
25.87052536, 26.38413048, 24.10971069, 27.77280045, 24.93728065,
26.8804493, 23.90113258, 25.07429123, 27.60118484, 23.12600708,
26.39195442, 23.01516533, 31.3666172, 31.80447578, 24.03654861,
25.11828613, 24.17065239, 28.48728561), Age = c(76L, 76L, 68L,
68L, 68L, 57L, 57L, 56L, 56L, 60L, 60L, 44L, 44L, 58L, 58L, 71L,
71L, 56L, 56L), Diseasestatus = c(0L, 0L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L), Met1 = c(0.326537646,
0.362137501, 0.403331692, 0.343789581, 0.437786804, 0.720648545,
0.974583105, 0.565800103, 0.613001417, 0.547743467, 0.337683125,
0.393250468, 0.465795971, 0.390206584, 0.172261362, 0.382496277,
0.435237338, 0.945312001, 0.321214419), Met2 = c(0.465736593,
0.540715637, 0.472693123, 0.681156674, 0.416291697, 0.487306504,
0.499092007, 0.634904337, 0.408109505, 0.808546214, 0.4113336,
0.924069141, 0.673204104, 0.693500596, 0.522794352, 0.373602067,
0.716407827, 0.649634492, 0.514429127), Met3 = c(0.902854296,
0.413241218, 0.418436978, 0.599698582, 0.806269489, 0.746859677,
0.461750237, 0.534943022, 0.511101841, 0.339406025, 0.235624644,
0.405761674, 0.312947287, 0.409833325, 0.026137354, 0.477175654,
0.387610389, 0.226427797, 0.19742037), Met4 = c(0.99425024, 0.923934731,
0.804677487, 0.31081605, 0.351561982, 0.529615606, 0.756342125,
0.968115646, 0.989016517, 0.938703504, 0.841777433, 0.103150219,
0.68397041, 0.903129097, 0.897388285, 0.905293975, 0.992337012,
0.358619626, 0.159601445), Met5 = c(0.527268407, 0.646332723,
0.646042578, 0.163344212, 0.202267074, 0.536976636, 0.789061409,
0.725657854, 0.697350164, 0.044081822, 0.959496477, 0.295039796,
0.120109301, 0.160817478, 0.901107461, 0.529179518, 0.573373775,
0.560701172, 0.325806613), Met6 = c(0.809497068, 0.614253411,
0.375421856, 0.446069992, 0.710859888, 0.474587655, 0.217817798,
0.464787031, 0.5540375, 0.62822217, 0.082906217, 0.294754096,
0.862216149, 0.427856328, 0.418944666, 0.516181576, 0.544516281,
0.519113772, 0.279522811), Met7 = c(0.419627992, 0.365954584,
0.434398151, 0.313441811, 0.368051981, 0.660614914, 0.825809828,
0.412109302, 0.545740249, 0.326247449, 0.373035298, 0.380623499,
0.428859232, 0.321044089, 0.24939936, 0.298372835, 0.387467105,
0.906034877, 0.147250125), Met8 = c(0.549683979, 0.347795497,
0.465729386, 0.625045713, 0.551784129, 0.348174756, 0.4334509,
0.594903245, 0.561353241, 0.621274979, 0.231389704, 0.308801446,
0.464799907, 0.401663011, 0.332966555, 0.109698561, 0.184359915,
0.091447702, 0.20568595), Met9 = c(0.605266628, 0.316564583,
0.166558136, 0.337470002, 0.458328756, 0.409329111, 0.269424154,
0.514746553, 0.408357879, 0.572246814, 0.264718681, 0.125162297,
0.211230627, 0.655667116, 0.034006203, 0.189685846, 0.243832622,
0.360657636, 0.259174139), Met10 = c(0.576174353, 0.214361265,
0.523133504, 0.549085457, 0.430400583, 0.53943429, 0.441563681,
0.401805576, 0.386025835, 0.514017513, 0, 0.330305736, 0.567380079,
0.50505895, 0.242814909, 0.306522744, 0.132950297, 0.207312191,
0.328760686)), class = "data.frame", row.names = c(NA, -19L))
