Dear list,
I have been searching for a week to fit a simple linear model to my data. I
have looked into the previous posts but I haven't found anything relevant to
my problem. I guess it is something simple...I just cannot see it.
I have the following data frame, named "data", which is a subset of a
microarray experiment. The columns are the samples and the rows are the
probes. I binded the first line, called "norm", which represents the
estimated output. I want to create a linear model which shows the
relationship between the gene expressions (rows) and the output (norm).
*data*
GSM276723.CEL GSM276724.CEL GSM276725.CEL GSM276726.CEL
norm 0.897000 0.590000 0.683000 0.949000
206427_s_at 5.387205 6.036506 8.824783 10.864122
205338_s_at 6.454779 13.143095 6.123212 12.726562
209848_s_at 6.703062 7.783330 12.175654 9.339651
205694_at 5.894131 5.794516 12.876555 11.534664
201909_at 12.616538 12.913255 12.275182 12.767743
208894_at 13.049286 9.317874 12.873516 13.527182
216512_s_at 6.324789 12.783791 6.216932 12.013404
205337_at 6.175940 12.158796 6.117519 12.041078
201850_at 6.633013 6.465900 6.535434 7.749985
210982_s_at 12.444791 8.597388 12.197696 12.963449
GSM276727.CEL GSM276728.CEL GSM276729.CEL GSM276731.CEL
norm 0.302000 0.597000 0.270000 0.530000
206427_s_at 5.690357 8.014055 13.034753 5.493977
205338_s_at 5.757048 7.706341 13.258410 5.562588
209848_s_at 6.461028 7.036515 13.633649 5.874098
205694_at 5.519552 5.297107 6.498811 5.146150
201909_at 12.814454 11.592632 6.594229 6.650796
208894_at 13.835359 13.028096 5.839909 6.045578
216512_s_at 6.033096 7.273650 12.669054 5.946932
205337_at 5.879028 7.381713 12.633829 5.379559
201850_at 9.684397 6.560014 8.523229 6.573052
210982_s_at 13.342729 12.470517 5.903681 5.658115
GSM276732.CEL GSM276735.CEL GSM276736.CEL GSM276737.CEL
norm 0.43400 0.647000 0.113000 1.000000
206427_s_at 12.80257 5.645002 6.519554 13.572480
205338_s_at 13.38057 5.804107 11.090690 14.024922
209848_s_at 13.27718 6.490851 9.784199 14.101162
205694_at 11.37717 5.802105 7.944963 14.060492
201909_at 13.24126 12.263899 12.578315 6.443491
208894_at 12.29916 7.563361 9.971493 7.094214
216512_s_at 13.00303 5.905789 10.512761 13.647573
205337_at 12.63560 5.430138 10.707242 13.020312
201850_at 12.71874 6.275480 6.987962 12.354580
210982_s_at 11.53559 7.225199 9.322706 6.617615
GSM276738.CEL GSM276739.CEL GSM276740.CEL GSM276742.CEL
norm 0.35700 0.967000 0.823000 1.000000
206427_s_at 13.33764 13.607918 13.190551 12.387189
205338_s_at 13.65492 12.812950 12.237476 12.912605
209848_s_at 13.48525 13.435389 13.851347 12.540495
205694_at 7.70928 10.045331 13.391456 11.103841
201909_at 12.47093 11.937344 6.631023 7.160071
208894_at 12.20508 8.892181 6.478889 5.927860
216512_s_at 13.42313 12.151691 11.620552 12.341763
205337_at 12.67544 12.036528 11.641203 12.275845
201850_at 11.85481 13.172666 12.964316 12.156142
210982_s_at 11.49940 8.380404 6.121762 5.921634
GSM276743.CEL GSM276744.CEL GSM276745.CEL GSM276747.CEL
norm 0.899000 0.927000 0.754000 0.437000
206427_s_at 12.665097 12.604673 11.446630 13.000295
205338_s_at 13.261141 12.448096 13.185698 12.510952
209848_s_at 13.396711 13.882529 13.040600 12.984137
205694_at 10.888474 7.094063 8.630120 12.321685
201909_at 12.100560 6.666787 12.330600 6.572282
208894_at 7.741437 8.348155 10.106442 6.009902
216512_s_at 12.830373 11.504074 12.300163 11.525958
205337_at 12.264569 11.676281 11.940917 11.618351
201850_at 11.055564 12.202366 7.327056 12.853055
210982_s_at 7.285289 8.129298 9.577032 5.924993
GSM276748.CEL GSM276752.CEL GSM276754.CEL GSM276756.CEL
norm 0.321000 0.620000 0.155000 0.946000
206427_s_at 9.081283 11.446978 8.191261 13.192507
205338_s_at 13.737773 13.698520 12.983830 10.948681
209848_s_at 13.234025 12.956672 10.644642 13.176656
205694_at 7.953865 7.397013 7.170732 13.618932
201909_at 12.533684 7.049442 6.804030 7.135974
208894_at 11.868729 8.558455 6.629858 6.850639
216512_s_at 13.589290 12.781853 12.060414 10.143297
205337_at 13.084386 12.442617 12.104849 10.364035
201850_at 6.615453 8.104145 7.058739 6.514298
210982_s_at 11.058085 7.891520 6.516261 6.532226
GSM276758.CEL GSM276759.CEL
norm 0.767000 0.218000
206427_s_at 5.742074 11.232337
205338_s_at 6.375289 13.406557
209848_s_at 6.226996 6.835458
205694_at 5.864042 11.218719
201909_at 6.907489 7.316435
208894_at 12.596987 12.408412
216512_s_at 6.308256 12.318892
205337_at 6.063775 12.389912
201850_at 6.816491 6.602764
210982_s_at 11.985288 11.853911
*What I did is the following:*
>fm1=as.formula((norm) ~ "206427_s_at" + "205338_s_at" + "209848_s_at" +
"205694_at" + "201909_at" + "208894_at" + "216512_s_at" + "205337_at" +
"201850_at" + "210982_s_at")
>lm1=lm(fm1,data1new)
And I receive the following error:
Error in terms.formula(formula, data = data) :
invalid model formula in ExtractVars
*I have also tried:*
>cols=rownames(data3) %%%%Where data3 is the same data frame with data
above, but without the "norm" row binded yet
thus: > cols
[1] "206427_s_at" "205338_s_at" "209848_s_at" "205694_at" "201909_at"
[6] "208894_at" "216512_s_at" "205337_at" "201850_at" "210982_s_at"
> lm1=lm(fm1,data1new)
and in this case Ireceive the following error:
Error in model.frame.default(formula = fm1, data = data1new,
drop.unused.levels = TRUE) :
variable lengths differ (found for 'cols')
Could anyone help me with this?
Thank you very much in advance,
Eleni
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