Ermitteln Sie die Anzahl der Parameter eines linearen Modells

chl

2010-12-16 01:56:17 UTC

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Versuchen Sie etwas wie:

  > x <- replizieren (2, rnorm (100)) > y <- 1.2 * x [, 1] + rnorm (100) > Zusammenfassung (lm .fit <-lm (y ~ x)) >-Länge (lm.fit $ -Koeffizienten) [1] 3> # oder>-Länge (coef (lm.fit)) [1] 3

Mit

  > str (lm.fit)

können Sie sich besser vorstellen, was ein R-Objekt enthält

Vielen Dank. In der Zwischenzeit wurde mir klar, dass ich auch die Anzahl der Beobachtungen brauche (n). Ein Weg ist folgender: dim (model.matrix (model)) [1] Können Sie einen anderen Weg vorschlagen?

@Brani Versuchen Sie "nrow (na.omit (cbind (y, x1, x2))" (nach Ihrer Notation) oder "length (lm.fit $ model $ y)", um die Anzahl der Beobachtungen zu ermitteln, die bei der Schätzung der Modellparameter verwendet werden . Übrigens ist es besser, einen data.frame zu erstellen, z. `df <- data.frame (y, x1, x2)` und benutze dann `lm (y ~., data = df)`.

vqv

2010-12-17 05:00:58 UTC

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Ein allgemeinerer Ansatz ist die Verwendung der Funktion logLik () . Es gibt ein Objekt mit dem Attribut df zurück, das den angepassten Modellen Freiheitsgrade gibt. Der Vorteil dieses Ansatzes besteht darin, dass er mit vielen anderen Modellklassen (einschließlich glm ) funktioniert. Im Fall einer gewöhnlichen linearen Regression ( lm ) entspricht dies der Anzahl der Parameter + 1 für die Schätzung der Fehlervarianz.

Aus dem logLik Dokumentation:

Für "lm" -Anpassungen wird angenommen, dass die Skala geschätzt wurde (nach maximaler Wahrscheinlichkeit oder REML) und alle Konstanten in der Log-Wahrscheinlichkeit enthalten sind.

Auf diese Weise können Sie auch die Anzahl der Beobachtungen ermitteln.

  > X1 <rnorm (10) > X2 <rnorm (10) > Y <-X1 + X2 + rnorm (10) >-Modell <-lm (Y ~ X1 + X2) > ll <-logLik (Modell) >-Attribute (ll) $ nall [1] 10 $ nobs [1] 10 $ df [1] 4 $ class [1] "logLik"

dickoa

2010-12-16 04:24:19 UTC

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Vielleicht ist es ein bisschen hackisch, aber Sie können Folgendes tun:

  n <-Länge (Koeffizienten (Modell))

F. Tusell

2010-12-16 03:19:19 UTC

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Ich denke, Sie könnten die Komponente lm.fit $ rank verwenden oder lm.fit $ df.residual von der Stichprobengröße subtrahieren, um das zu erhalten, was Sie wollen. (Ich gehe davon aus, dass Sie die Anzahl der freien Parameter möchten.)

Sam

2014-04-09 10:52:20 UTC

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Die R-Funktion logLik scheint eine attraktive Lösung zu sein, um den Modellfreiheitsgrad im Allgemeinen zu extrahieren, da sie auf viele Modellobjekte angewendet werden kann, einschließlich lm , glm , nls , Arima , um nur einige zu nennen.

Aber der df der -Attribute (logLik (obj)) scheint 1 größer als der wahre Wert zu sein. Verwenden Sie es daher mit Vorsicht.

Kannst du erklären, warum du denkst, dass es größer ist, als es sein sollte? Haben Sie ein einfaches Beispiel, das Sie zeigen könnten? (Ignorieren Sie vielleicht die Existenz eines Varianzparameters?)

user2860703

2016-05-03 03:14:57 UTC

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Hier ist eine andere Lösung, wenn Ihr Modell nls ist, mit einem Beispieldatensatz in r:

  # Passen Sie ein nls-Modell an (dies ist in diesem Fall eine Lichtantwortkurve der Photosynthese) mod< -nls (Antwort ~ (alpha * x + Pm-sqrt (((alpha * x + Pm) ^ 2) -4 * alpha * x * Pm * theta)) / (2 * theta) + Rd, Daten = Daten, start = list (alpha = 0,05, theta = 0,5, Rd = -1, Pm = 30)) # Es gibt vier Parameter: Pm, alpha, theta und Rd.length (mod $ m $ getPars ())

Wie @Sam hervorhob, gibt logLik die Anzahl der Parameter +1 an.

  -Attribute (logLik (mod)) $ df

Hier ist der Beispieldatensatz, damit Sie die obigen Befehle reproduzieren können.

  Data<-Struktur (list (x = c (-8, -3,97194388777555, 0,0561122244488974, 4,08416833667335, 8,11222444889779, 12,1402805611222, 16,1683366733467, 20,1963927855711, 24,2244488977956, 28,25250501002, 32,2805611222445, 36,3086172344689, 40,3366733466934, 44,3647294589178, 48,3927855711423, 52,4208416833667, 56,4488977955912, 60,4769539078156, 64,5050100200401, 68,5330661322645, 72,561122244489, 76,5891783567134, 80,6172344689379, 84,6452905811623, 88,6733466933868, 92,7014028056112, 96,7294589178357, 100,75751503006, 104,785571142285, 108,813627254509, 112,841683366733, 116,869739478958, 120,897795591182, 124,925851703407, 128,953907815631, 132,981963927856, 137,01002004008, 141,038076152305, 145,066132264529, 149,094188376753, 153,122244488978, 157.150300601202, 161.178356713427, 165.206412825651, 169.234468937876, 173.2625250501, 177.290581162325, 181.318637274549, 185.346693386774, 189.374749498998, 193.40280. 6, 209,51503006012, 213,543086172345, 217,571142284569, 221,599198396794, 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1901.29859719439, 1905.32665330661, 1909.35470941884, 1913.38276553106, 1917.41082164329, 1921.43887775551, 1925.46693386774, 1929.49498997996, 1933.52304609218, 1937.55110220441, 1941.57915831663, 1945.60721442886, 1949.63527054108, 1953.66332665331, 1957.69138276553, 1961.71943887776, 1965.74749498998, 1969.7755511022, 1973.80360721443, 1977.83166332665, 1981.85971943888, 1985.8877755511, 1989.91583166333, 1993.94388777555, 1997.97194388778, 2002), response = c(-3.58957478518253, -3.62025086478834, -1.04832718629931, -0.932815570713867, -2.0059547584204, -1.80378256401687, -1.07078839210859, -1.47563839178252, 0.172463665814555, -0.247936953593207, -0.547987522372114, -0.942197740134927, -0.173128438044605, -1.07747254510461, 1.10668446828936, 1.05530666799013, 0.777604622119702, 1.06274622733744, 0.900238206583803, 1.31412394039238, 1.89499625411371, 1.04033412499542, 1.38191631121636, 0.999431819175433, 2.68812421375804, 1.47394328081264, 2.72714724358873, 1.71016394833754, 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