Tutorial from Amidlige and MBuynak#4
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| initialGuess=numpy.array([1,1,1]) | ||
| fit-minimize(Monod,initialGuess,method="Monod Equation",options={'disp':True},args=data) |
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fit=minimize(Monod,initialGuess,method="Nelder-Mead",options={'disp':True},args=data)
| K=p[1] | ||
| sigma=p[2] | ||
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| expected=uMax* (x/(x+K)) |
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expected=uMax* (obs.S/(obs.S+K))
| sigma=p[2] | ||
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| expected=uMax* (x/(x+K)) | ||
| nll=-1+norm(expected,sigma).logpdf(obs.y).sum |
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nll=-1+norm(expected,sigma).logpdf(obs.u).sum
| os.chdir("/Users/madelinebuynak/Desktop") | ||
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| #load data | ||
| data=pandas.read_csv("leafDecomp.csv",header=0,sep='\t') |
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data=pandas.read_csv("leafDecomp.csv")
| initialGuess=numpy.array([1,1,1]) | ||
| fitNull=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp':True},args=subset) | ||
| fitAlter=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp':True},args=subset) | ||
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subset=ponzr.loc[data.mutation.isin(['WT','M124K']),:]
input=pandas.DataFrame({'y':subset.ponzr1Counts,'design':0})
input.loc[subset.mutation=='M124K','design']=1
M124Knull=minimize(null, initialGuess,method="Nelder-Mead",args=subset)
M124Ktrt=minimize(alter, initialGuess,method="Nelder-Mead",args=subset)
1-chi2.cdf(x=2*(M124Knull.fun-M124Ktrt.fun),df=1)
Same for the other two mutations
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| #I213N: p-value ~ 0.88 (no effect of treatment) | ||
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The structure looks good. Try to make the syntax work better
-0.5 pts
| def alter (p,obs): | ||
| B0=p[0] | ||
| B1=p[1] | ||
| sigma=p[3] |
| nll=-1*norm(expected,sigma).logpdf(obs.y).sum() | ||
| return nll | ||
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| def alter (p,obs): |
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You want to name the function different than the previous one
| initialGuess=numpy.array([1,1,1]) | ||
| fitNull=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp':True},args=subset) | ||
| fitAlter=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp':True},args=subset) | ||
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initialGuess=numpy.array([1,1,1,1])
fit3a=minimize(null,initialGuess,method="Nelder-Mead",options={'disp':True},args=data)
fit3b=minimize(alter1,initialGuess,method="Nelder-Mead",options={'disp':True},args=data)
fit3c=minimize(alter2,initialGuess,method="Nelder-Mead",options={'disp':True},args=data)
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| #quadratic model is the best fit, but linear model is better than constant model | ||
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The structure is correct. Try to improve the syntax
-0.5 pts
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| print(fit.x) | ||
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