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POHow obtain the true residual deviance and degrees of freedom in R of a glm model when a set of parameters gets pasted() as a vector
 

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  1. POHow obtain the true residual deviance and degrees of freedom in R of a glm model when a set of parameters gets pasted() as a vector
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    <p>I'm writing a script (in python, with the R parts in pypeR) such that I need to use a function in R that compares two models with an F-ratio test.</p> <p>The models are like this:</p> <p><strong>Model 1:</strong> <code>Response ~ Predictor A + Predictor B + Predictor C.... + Predictor n</code><br> <strong>Model 2:</strong> <code>Response ~ Predictor 1</code></p> <p>Together predictors <code>A+B+...n</code> make up <code>Predictor 1</code>, so there's no problem with nesting here (trust me).</p> <p>When I pass <code>Predictor A + Predictor B + Predictor C.... + Predictor n</code> to the function I've created, I <em>think</em> it's treating them as one variable (because the degrees of freedom is the same as that of <code>Model 2</code>). Perhaps this is because I'm using <code>paste()</code>? Anyway, the actual number of predictors in model 1 will be changing across runs (which is why I need it as a function), so I'm not sure how else to accommodate this besides using <code>paste()</code>.</p> <p>Bear in mind that paste may not actually be the problem here; I just wanted to let people know that <em>I</em> thought the problem might be.</p> <p>Are there any suggestions for <em>how I might obtain to true residual deviance and degrees of freedom</em> for <code>model 1</code>? It can be a hack. For instance, I was simply subtracting <code>length(vector of predictors) - 1</code> to obtain the degrees of freedom. I have no idea what a similar hack for residual deviance would be.</p> <p>Here's the function and an example instantiation:</p> <pre><code>make_and_compare_models &lt;- function(fitness_trait_name, data_frame_name, vector_for_multiple_regression, predictor_for_single_regression, fam){ fit1&lt;-glm(formula=as.formula(paste(fitness_trait_name,"~", paste(vector_for_multiple_regression, sep="+"))), family=fam, data=data_frame_name) #print ('length of vector of predictors') additional.degrees.of.freedom.fit1&lt;-length(vector_for_multiple_regression)-1 ##the paste above prevents R from recognizing all of the vectors as separate predictors. This -1 gives you the difference in parameter number between the two models. print ("summary fit 1") print(summary(fit1)) dev1&lt;-(fit1$deviance) print ('residual deviance of fit1') print (dev1) print(fit1$df.residual) ##this is how I'd correct for degrees of freedom #df1=fit1$df.residual-additional.degrees.of.freedom.fit1 #fit1$df.residual=df1 ##if the old way df1=fit1$df.residual print(fit1$df.residual) print ('df1') print (df1) fit2&lt;- glm(data=data_frame_name, formula=as.formula(paste(fitness_trait_name,"~",predictor_for_single_regression)), family=fam) print("summary fit 2") print(summary(fit2)) print ("deviance of fit2") dev2&lt;-(fit2$deviance) print(dev2) df2=fit2$df.residual print ('df2') print (df2) F.ratio&lt;-((dev2-dev1)/(df2-df1))/(dev1/df1) print('F.ratio') print(F.ratio) new.p&lt;-1-pf(F.ratio,abs(df1-df2),max(df2,df1)) print('new.p') print(new.p) } data &lt;- structure(list(ID = c(1L, 2L, 4L, 7L, 9L, 10L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 20L, 21L, 22L, 23L, 24L, 25L, 27L, 28L, 29L, 31L, 34L, 37L, 38L, 39L, 40L, 41L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 52L, 55L, 56L, 59L, 60L, 61L, 62L, 63L, 65L, 66L, 67L, 68L, 69L, 71L), QnWeight_initial = c(158L, 165L, 137L, 150L, 153L, 137L, 158L, 163L, 159L, 151L, 145L, 144L, 157L, 144L, 133L, 148L, 151L, 151L, 147L, 158L, 178L, 164L, 134L, 151L, 148L, 142L, 127L, 179L, 162L, 150L, 151L, 153L, 163L, 155L, 163L, 170L, 149L, 165L, 128L, 134L, 145L, 147L, 148L, 160L, 131L, 155L, 169L, 143L, 123L, 151L), Survived_eclosion = c(0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Days_wrkr_eclosion_minus20 = c(NA, 1L, NA, 3L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 1L, NA, 0L, 7L, 1L, 0L, 1L, 0L, 1L, 2L, 2L, NA, 2L, 3L, 2L, 2L, NA, 0L, 1L, NA, NA, 0L, 0L, 0L, 0L, 3L, 3L, 3L, 1L, 0L, 2L, NA, 1L, 0L, 1L, 1L, 3L, 1L, 2L), MLH = c(0.5, 0.666666667, 0.555555556, 0.25, 1, 0.5, 0.333333333, 0.7, 0.5, 0.7, 0.5, 0.666666667, 0.375, 0.4, 0.5, 0.333333333, 0.4, 0.375, 0.3, 0.5, 0.3, 0.2, 0.4, 0.875, 0.6, 0.4, 0.222222222, 0.222222222, 0.6, 0.6, 0.3, 0.4, 0.714285714, 0.4, 0.3, 0.6, 0.4, 0.7, 0.625, 0.555555556, 0.25, 0.5, 0.5, 0.6, 0.25, 0.428571429, 0.3, 0.25, 0.375, 0.555555556), Acon5 = c(0.35387674, 0.35387674, 0.35387674, 0.35387674, 0.35387674, 0.35387674, 0.35387674, 0, 0, 1, 0, 1, 0.35387674, 0, 0, 0.35387674, 1, 1, 0, 0, 0, 1, 0, 0.35387674, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0.35387674), Baez = c(1, 1, 1, 0.467836257, 1, 1, 0, 0, 1, 1, 0, 0.467836257, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0.467836257, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1), C294 = c(0, 1, 0, 0, 1, 0.582542694, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0.582542694, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1), C316 = c(1, 1, 0, 0, 0.519685039, 0.519685039, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0.519685039, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0.519685039, 1, 0, 1, 1, 0, 0.519685039, 1, 0.519685039, 1, 1, 1, 0.519685039, 0.519685039, 0, 0.519685039, 0.519685039, 0), i_120_PigTail = c(1, 1, 0, 1, 0.631236443, 0.631236443, 1, 1, 1, 1, 1, 0, 0.631236443, 1, 1, 1, 0, 0.631236443, 1, 1, 1, 0, 0, 1, 1, 1, 0.631236443, 0, 1, 1, 0, 1, 0.631236443, 1, 0, 1, 0, 0, 1, 0.631236443, 0.631236443, 0, 1, 0, 0.631236443, 0.631236443, 1, 0.631236443, 0.631236443, 1), i129 = c(0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Jackstraw_PigTail = c(0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Neil_Young = c(0.529636711, 0, 1, 0, 0.529636711, 0.529636711, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1), Ramble = c(0, 0, 0, 0, 0.215163934, 0.215163934, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0.215163934, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0.215163934, 0, 0, 0, 0), Sol_18 = c(1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0.404669261, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1)), .Names = c("ID", "QnWeight_initial", "Survived_eclosion", "Days_wrkr_eclosion_minus20", "MLH", "Acon5", "Baez", "C294", "C316", "i_120_PigTail", "i129", "Jackstraw_PigTail", "Neil_Young", "Ramble", "Sol_18"), class = "data.frame", row.names = c(NA, -50L)) make_and_compare_models("QnWeight_initial", data, c("Acon5","Baez","C294","C316","i_120_PigTail","i129","Jackstraw_PigTail","Neil_Young","Ramble","Sol_18"), "MLH", "gaussian") </code></pre>
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