help with linear model
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@eleni-christodoulou-2653
Last seen 6.2 years ago
Singapore
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 [[alternative HTML version deleted]]
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Ista Zahn ▴ 10
@ista-zahn-3753
Last seen 10.2 years ago
I'm not familiar with microarray data, so I hope I'm not off base here. Data frames are structured so that variables appear in the columns and cases in the rows. From your formula it looks like you're trying to fit a model using rows as variables and columns as cases. There is probably a way to do this, but It might be easier to just flip your data. One way to do this is dataNew <- as.data.frame(t(data)) row.names(dataNew) <- names(data) names(dataNew) <- paste("I",row.names(data), sep="") #variable names should start with a letter (note that naming your data "data" is not a good practice.) Now you should be able to run your model as before (prefixing "I" to the variable names to match the new naming scheme): m1 = lm(norm ~ I206427_s_at + I205338_s_at + I209848_s_at + I205694_at + I201909_at + I208894_at + I216512_s_at + I205337_at + I201850_at + I210982_s_at, data=dataNew) Hope it helps, Ista On Mon, Oct 26, 2009 at 5:48 AM, Eleni Christodoulou <elenichri at="" gmail.com=""> wrote: > 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 > > ? ? ? ?[[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Ista Zahn Graduate student University of Rochester Department of Clinical and Social Psychology http://yourpsyche.org
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@claus-jurgen-scholz-3117
Last seen 10.2 years ago
Dear Eleni, I think different covariates should be represented as columns in your data, so a simple matrix transposition could help. Try: new.data <- t(data) # matrix transposition my.lm <- lm(norm~., data=new.data) # linear regression with all covariates Bests, Claus-J?rgen Eleni Christodoulou schrieb: > 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 > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >
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Petr PIKAL ▴ 20
@petr-pikal-3754
Last seen 10.2 years ago
Hi r-help-bounces at r-project.org napsal dne 26.10.2009 10:48:51: > 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? Usual expectation is that data are arranged columnwise. Each column is a variable and each row is an observation. So you shall transform your data to this form e.g. by t(yourdata). Other issue can be if your data are really numeric what you can test by str(yourdata) which shall show a structure of your data. If everything is OK than lm(norm ~ . , data = data1new) shall produce linear model of norm on all other columns in data frame data1new) Regards Petr > > Thank you very much in advance, > Eleni > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
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Thank you all for your replies. I have tried transposing my data and before but I did not mention it because I was getting the same error. In the present case though it worked because I put >lm1=lm(*norm~*.,data=t(data)) instead of >lm1=lm(*fm1*, data=t(data)) where *fm1=norm~cols...* I actually didn't know that there exists such a difference between norm~cols and norm~. I wonder why... Thank you all again! Best, Eleni On Mon, Oct 26, 2009 at 12:24 PM, Petr PIKAL <petr.pikal@precheza.cz> wrote: > Hi > > > r-help-bounces@r-project.org napsal dne 26.10.2009 10:48:51: > > > 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? > > Usual expectation is that data are arranged columnwise. Each column is a > variable and each row is an observation. So you shall transform your data > to this form e.g. by > > t(yourdata). > > Other issue can be if your data are really numeric what you can test by > > str(yourdata) > > which shall show a structure of your data. > If everything is OK than > > lm(norm ~ . , data = data1new) shall produce linear model of norm on all > other columns in data frame data1new) > > Regards > Petr > > > > > Thank you very much in advance, > > Eleni > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > [[alternative HTML version deleted]]
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Entering edit mode
r-help-bounces at r-project.org napsal dne 26.10.2009 11:31:26: > Thank you all for your replies. I have tried transposing my data and before > but I did not mention it because I was getting the same error. In the > present case though it worked because I put > >lm1=lm(*norm~*.,data=t(data)) > instead of > >lm1=lm(*fm1*, data=t(data)) > where *fm1=norm~cols...* There shall not be any difference. I suspect that your formula definition has superfluous commas and/or t(data) change names which you suppose to be e.g. 206427_s_at but it can not be valid name. look at head(t(data)) how names are changed. You need to change your formula according to names. Regards Petr > I actually didn't know that there exists such a difference between norm~cols > and norm~. > I wonder why... > > Thank you all again! > Best, > Eleni > > On Mon, Oct 26, 2009 at 12:24 PM, Petr PIKAL <petr.pikal at="" precheza.cz=""> wrote: > > > Hi > > > > > > r-help-bounces at r-project.org napsal dne 26.10.2009 10:48:51: > > > > > 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? > > > > Usual expectation is that data are arranged columnwise. Each column is a > > variable and each row is an observation. So you shall transform your data > > to this form e.g. by > > > > t(yourdata). > > > > Other issue can be if your data are really numeric what you can test by > > > > str(yourdata) > > > > which shall show a structure of your data. > > If everything is OK than > > > > lm(norm ~ . , data = data1new) shall produce linear model of norm on all > > other columns in data frame data1new) > > > > Regards > > Petr > > > > > > > > Thank you very much in advance, > > > Eleni > > > > > > [[alternative HTML version deleted]] > > > > > > ______________________________________________ > > > R-help at r-project.org mailing list > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide > > http://www.R-project.org/posting-guide.html > > > and provide commented, minimal, self-contained, reproducible code. > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
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