EDIT 2 27/7: I figured out that I must get the correct output if I set the dispersion parameter to 0.64.
I tried doing this with
summary(fit, dispersion = 0.64)
but this doesn't work. Probably because it is an imputed dataset with pooled estimates. It does work in a dataset without imputation. Does anyone know how to rewrite this line so it will work in the pooled dataset?
EDIT 26/7: I have managed to find the solution to this in the sample data:
gamma.shape(fit)
summary(fit, dispersion = gamma.dispersion(fit))
summary(fit, dispersion=1/gamma.shape(fit)$alpha)
The final line reproduces similar S.E.'s and p-values as SPSS. However, when I try to use the same method in the pooled dataset, it doesn't work. This is the error I get:
gamma.shape(fit)
Error in -y : invalid argument to unary operator
> gamma.shape(pool(fit))
Error in UseMethod("gamma.shape") :
no applicable method for 'gamma.shape' applied to an object of class
"c('mipo', 'data.frame')"
>
Any clues on how to solve this?
I am experiencing some difficulties with R. I previously posted a question here which brought me further, but also led to a different question:
Is it possible to change the scale parameter method in R? I am performing a GLM with gamma log-link, but the estimated S.E.'s, confidence intervals and p-values differ between R and SPSS. I would like to replicate the exact SPSS findings in R, and I can't figure out how.
My original dataset is an imputed dataset (mids object, created with the MICE package). For simplification, I have created a sample dataset with less data:
## Sample data
F <- structure(list(HbA1c = c(69, 75, 62, 96, NA, 86, 44, 49, NA, 63, 43, 75, 48, 56, 79, 78, 67, 66, 75, 67, 65, 66, 34, 62, 79, 60, 91, 51, 84, 72, 65, NA, NA, 62, 61, 69, 63, NA, 85, 38, 42, 80, 59, 96, 59, 49, 62, 98, 71, 78, 50, 43, 44, 69, 56, 38, 59, 74, 115, 69, 67, 51, NA, 107, 71, 86, 78, 41, 60, 59, 74, 73, 49, 34, 71, 57, 55, 74, 67, 61, 48, 59, 70, NA, 55, 72, 69, 82, 40, 58, NA, 53, 46, 69, 60, 39, 76, 69, 61, 86, 58, 63, 66, 103, 73, 54, 59, 46, 58, 70, 57, 53, 49, 53, 58, 71, 60, 76, 64, 97, 60, 49, 53, 44, 53, 73, 59, 75, 61, 55, 68, 56, 51, 91, 92, 76, 51, 55, 61, 83, 52, 62, 71, 75, 54, 64, 90, 65, NA, 69, 70, 70, 59, 62, 60, 63, 58, 58, 63, 60, 49, 62, 95, 42, 99, 67, 117, 68, 55, 55, 70, 60, 61, 91, 33, 89, 60, 47, 62, 72, 40, 88, 59, 56, 57, 59, 74, 41, 53, 76, 48, 73, 65, 96, 58, 55, 67, 45, 45, 69, 72, 44, 59, 43, 90, 69, 69, 71, 93, 42, 87, 54, 83, 60, 48, NA, 53, 56, 57, 77, 63, NA, 63, 60, 68, 51, 48, 65, 61, 79, 63, 62, 53, 67, 53, 53, 63, 55, 61, 51, 53, 46, NA, 78, 76, 73, 51, 49, 68, 86, 71, 55, 57, 113, 63, 68, 94, NA, 38, 50, NA, 42, 60, 57, 49, 60, 81, 69, 55, 82, 64, 55, 74, 71, 56, 60, NA, 47, 49, 98, 55, 80, 71, 69, 35, 53, 90, 64, 82, 132, 64, 70, 65, 34, 65, 54, NA, 68, 58, 76, 82, 66, 74, 66, NA, 54, 53, 78, 62, 88, 69, 49, 83, 54, 55, 56, 66, 84, 47, 82, 53, 62, 163, 41, 55, 89, 76, 81, 45, 50, 89, 72, 90, 47, 38, 83, NA, 53, 74, 55, 47, 49, 56, 74, 107, 86, 48, 59, 86, 44, 55, 64, 81, 66, 63, 98, 51, NA, 60, 50, 55, 52, 79, 58, 50, 89, NA, 36, 50, 70, NA, 86, 57, 60, 78, 53, 70, 79, 49, 78, 83, 66, 57, 62, 80, 70, NA, 67, 80, 46, 79, 47, 145, 87, 53, 65, 73, 75, 53, 50, 71, NA, 65, 106, 123, 51, 55, 43, 48, 86, 61, 64, 55, 71, 61, 96, 80, 69, 66, 74, 88, 48, 68, 55, 52, 58, 69, 66, 44, 45, 64, 84, 72, 49, NA, 71, 70, 104, 78, 73, 47, 75, 45, 57, 88, 86, 55, 72, 47, 53, 113, 62, 54), BMI = c(26.7, 34.5, 24.3, NA, 19.1, 37.9, 29.1, 27.1, NA, 21.1, 48.5, 26.2, 26.9, NA, 25.5, 25.3, 44.3, 25.2, 26.7, NA, 25.5, 25.9, 31.2, 33, 21.8, 23.7, 32, 23.6, 32.4, 29.7, NA, 22.9, 24.4, 33.9, 35.4, 41.2, 20.4, NA, 30.1, 21, NA, NA, 29.5, 16.6, 38.1, 23.9, 19.1, 35.4, 24.2, NA, 26.1, 20, 28.7, 30.7, 25.4, 29.6, 25.4, 26.2, 18.3, 31, NA, NA, 31.5, 32, 35.6, 24.3, 33.3, 35.5, NA, 24.1, NA, 33.4, 28.4, NA, 25.9, 26.7, 35.5, 31.6, 25, 25.5, 22.2, 22.3, 23.4, 35.3, 26.1, 32.6, 20.9, 35.9, 29.1, 32.8, 32.2, 28.9, 28.9, 28.8, 19.7, 29.4, 28.8, 28.2, 20.9, 33.5, 17.6, 38.6, 27.1, NA, 29, 25.6, 22.5, 30.6, 35.6, 32.5, 23.4, 27.2, 23.6, 26.6, 23.5, 30.3, 30.6, 26.4, 38.1, 34.7, NA, 24.6, 22.2, 39.8, 23, 35.8, 31.4, 22.8, 29.3, 27, 31.1, NA, NA, 32.4, 36, NA, 52.8, 22, 27.1, 23.3, 22.7, 25, 42.6, 30.2, 25.3, 30.5, 25.3, 28.4, 30.1, 32.4, NA, 32, 18.8, 23.1, 28.5, 25.1, 22.8, 23.6, 18.5, NA, 27.1, 25.3, 19.8, 20.8, 32.7, 30.1, 34.8, 37.5, NA, 28.1, 46, 23.5, 26.3, 22.2, 28.2, 29.3, 24.2, 29.7, 28.9, 28, 31.3, 28.6, 29.1, 28.4, 23.1, 34.9, 22.7, 26.9, 28.9, 35.9, 23, 25.8, 22.8, 19.2, 27.9, 29.2, 35, 25.1, 20.5, 23.9, 34.3, 23.1, 25.1, 20.5, 24.6, 24.4, 23.7, 22.4, 40.1, 21.9, 50, 34.2, 30.5, 20.7, 29.3, 32.6, 32.1, 23.9, NA, 34, 22.6, 30.2, 28.6, 27.5, 33, 24, 28.8, NA, 32.8, 21.8, NA, 37.8, 26.4, 36.2, 20.8, 24.4, 31, 31.9, 27.6, 25.4, 22.7, NA, 27.7, 32.4, 34, 26.2, 26.7, 23.7, 32, 24.1, 35.8, 23.5, 38.9, 35.3, NA, 23.9, 30.2, 24.4, 24.4, 27.9, NA, 25.7, 25.6, 25.8, 47.9, 25.6, 36.1, NA, 24.2, 24.8, 21.4, 22.3, 24.3, 24.7, 22.5, 25.9, 30.1, 27.4, 27.8, 22.6, 24.4, NA, 33.8, 41.9, 21.4, 32.5, 41.1, 27.2, NA, 37.8, 29, 23.2, 28.7, 25.2, 32.6, 29, 24.4, 23.1, 22.8, 23.1, 39.8, 26.6, 25.3, 53.5, 25, 22.9, 22.2, 30.2, 27.4, 27.4, NA, 25.2, 22.4, 20.2, 23.9, 23.3, 31.2, 24, 23.5, 38.8, 30, 30.6, 28.9, 23.1, 34.4, 28.7, 30.8, 21.6, 24.1, 25.5, 39.2, 29.3, 36.2, 28.3, NA, NA, NA, 29.5, 33.1, 23.4, 23.5, 25.1, 34.4, 24.5, 29.7, 22.2, 25.5, 23.3, 37.5, 26.8, 44.5, 32.4, 26.1, 21.4, 26.5, 32.7, 26.9, NA, 27.4, 36.3, 25.1, 37.7, NA, 27.6, 24.2, 46.9, 30.8, 29.3, 25.4, 35.7, 36.8, 35, 22.3, 28.3, 20.4, 25, 35, NA, 39.4, 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36, 60, 33, 35, 26, 44, 78, 22, 26, 77, 62, 121, 51, 28, 68, 63, 43, 64, 81, 120, 95, 98, 23, 11, 21, 10, 7, 41, 7, 33, 6, 40, 20, 2, 31, 23, 23, 13, 68, 9, 8, 41, 19, 27, 29, 46, NA, 35, 16, 12, 9, 14, 20, 7, 2, 4, 6, 6, 6, 4, 9, 6, 8, 9, 12, 9, 7, 8, 12, 11, 11, 14, 12, 14, 12, 16, 15, 22, 23, 19, 11, 12, 13, 17, 18, 19, 27, 15, 9, 17, 18, 19, 17, 19, 12, 16, 54, 21, 30, 23, 25, 24, 37, 35, 27, 47, 22, 27, 27, 30, 32, 32, 31, 39, 28, 36, 54, 50, 45, 42, 88, 56, 63, 82, 60, 70, 139, 122, 71, 130, 84, 33, 111, 111, 246, 157, 54, 24, 41, 22, 7, 33, 15, 9, 6, 16, 67, 3, 22, 48, 15, 57, 25, 48, 74, 40, 25, 18, 21, 3, 6, 7, 7, 14, 9, 11, 16, 14, 14, 14, 28, 18, 22, 21, 26, 39, 24, 22, 18, 22, 19, 19, 45, 15, 13, 22, 31, 29, 46, 37, 23, 35, 68, 39, 51, 35, 50, 80, 69, 51, 41, 90, 43, 32, 48, 34, 53, 25, 66, 39, 83, 70, 237, 81, 126, 95, 170, 35, 94, 8), etndich = c(1, 2, 1, 2, 1, 2, 1, 1, NA, 1, 1, 1, 1, 1, NA, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, NA, 2, 1, NA, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, NA, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, NA, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, NA, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, NA, NA, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, NA, 2, 1, 1, 1, 1, NA, 1, 1, 2, 1, 1, 1, 2, 1, NA, 1, 1, 1, 1, 1, 1, 1, NA, NA, 2, 1, 1, 2, 2, NA, 2, NA, 2, 2, 1, NA, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, NA, 1, 1, NA, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2), Leeftijd = c(26, 69, 67, 38, 29, 50, 29, 23, 52, 39, 50, 29, 36, 52, 43, 53, 47, 33, 52, 55, 43, 64, 35, 24, 51, 39, 50, 51, 46, 51, 30, 32, 28, 25, 52, 48, 60, 31, 61, 47, 46, 56, 38, 72, 88, 34, 56, 27, 27, 56, 52, 49, 34, 25, 22, 60, 61, 42, 45, 51, 42, 61, 69, 57, 35, 50, 42, 50, 51, 46, 28, 34, 52, 33, 30, 64, 65, 35, 31, 57, 75, 43, 46, 35, 65, 29, 29, 75, 49, 31, 57, 29, 40, 75, 30, 34, 58, 47, 37, 43, 34, 47, 46, 42, 49, 57, 46, 36, 51, 80, 45, 47, 48, 23, 51, 53, 44, 64, 44, 33, 40, 42, 29, 60, 28, 47, 47, 39, 25, 41, 39, 27, 57, 66, 42, 22, 59, 27, 43, 53, 65, 52, 41, 50, 55, 29, 55, 39, 41, 25, 74, 68, 55, 29, 77, 45, 18, 34, 49, 74, 44, 33, 48, 82, 61, 54, 46, 30, 33, 65, 51, 44, 50, 57, 27, 56, 85, 52, 31, 62, 62, 34, 48, 28, 28, 63, 30, 40, 44, 37, 73, 70, 39, 59, 56, 61, 40, 43, 33, 58, 44, 62, 26, 72, 67, 59, 48, 37, 52, 37, 57, 53, 59, 44, 71, 81, 33, 61, 50, 33, 48, 50, 63, 46, 60, 58, 40, 63, 39, 71, 38, 40, 56, 36, 52, 61, 83, 59, 43, 69, 50, 57, 38, 50, 27, 43, 46, 30, 50, 34, 68, 53, 48, 84, 41, 57, 61, 72, 27, 80, 71, 69, 61, 43, 67, 60, 58, 67, 72, 40, 79, 52, 80, 33, 25, 80, 67, 56, 66, 54, 50, 65, 39, 36, 69, 39, 34, 41, 36, 61, 33, 42, 43, 45, 48, 67, 69, 66, 37, 28, 64, 65, 68, 62, 84, 82, 59, 61, 74, 52, 41, 30, 33, 55, 55, 26, 53, 33, 64, 65, 74, 67, 70, 58, 51, 62, 67, 52, 40, 57, 57, 57, 59, 56, 61, 58, 45, 63, 61, 50, 70, 32, 50, 74, 70, 49, 42, 71, 51, 67, 46, 45, 75, 54, 75, 45, 46, 64, 60, 55, 61, 65, 68, 71, 43, 78, 53, 63, 85, 75, 66, 67, 54, 63, 68, 84, 58, 72, 70, 58, 29, 63, 83, 64, 75, 59, 76, 61, 62, 65, 61, 72, 20, 43, 67, 33, 62, 63, 51, 34, 68, 68, 60, 67, 44, 64, 69, 53, 69, 47, 41, 38, 57, 71, 70, 68, 25, 60, 71, 48, 64, 62, 72, 60, 45, 67, 59, 73, 27, 64, 66, 57, 72, 71, 77, 58, 56, 65, 74, 44, 22, 63, 42, 80, 52, 66, 60, 56, 54, 42, 68, 57, 37)), .Names = c("HbA1c", "BMI", "Comorb", "PAIDhoog", "PHQhoog", "totaalhealthcareutilization", "etndich", "Leeftijd"), row.names = c(NA, -453L), variable.labels = structure(c("HbA1c", "BMI level", "", "", "", "", "", ""), .Names = c("HbA1c", "BMI", "Comorb", "PAIDhoog", "PHQhoog", "totaalhealthcareutilization", "etndich", "Leeftijd")), codepage = 65001L, class = "data.frame")
When I perform a GLM, the R output is as follows:
fit <- glm(formula = totaalhealthcareutilization ~ PAIDhoog + PHQhoog +
Comorb + Leeftijd + etndich, family = Gamma(link = log),
data = df)
summary(fit)
#
#Call:
#glm(formula = totaalhealthcareutilization ~ PAIDhoog + PHQhoog +
# Comorb + Leeftijd + etndich, family = Gamma(link = log),
# data = df)
#
#Deviance Residuals:
# Min 1Q Median 3Q Max
#-2.1297 -0.7231 -0.3018 0.2075 3.1365
#
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 3.638751 0.267741 13.591 < 2e-16 ***
#PAIDhoog0 -0.201881 0.131777 -1.532 0.1264
#PHQhoog0 -0.126989 0.157416 -0.807 0.4203
#Comorbgeen -0.638842 0.144459 -4.422 1.28e-05 ***
#Comorb1 -0.348187 0.158484 -2.197 0.0286 *
#Leeftijd 0.007311 0.003534 2.069 0.0392 *
#etndich1 -0.151836 0.118872 -1.277 0.2023
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#(Dispersion parameter for Gamma family taken to be 0.9432289)
#
# Null deviance: 286.49 on 381 degrees of freedom
#Residual deviance: 243.01 on 375 degrees of freedom
# (71 observations deleted due to missingness)
#AIC: 3156
#
#Number of Fisher Scoring iterations: 6
And the SPSS output:
Parameter Estimates
Parameter B Std. Error 95% Wald Confidence Interval Hypothesis Test
Lower Upper Wald Chi-Square df Sig.
(Intercept) 3,639 ,2177 3,212 4,065 279,350 1 ,000
[PAIDhoog=0] -,202 ,1056 -,409 ,005 3,657 1 ,056
[PAIDhoog=1] 0a . . . . . .
[PHQhoog=0] -,127 ,1260 -,374 ,120 1,015 1 ,314
[PHQhoog=1] 0a . . . . . .
[Comorb=1] -,639 ,1148 -,864 -,414 30,940 1 ,000
[Comorb=2] -,348 ,1250 -,593 -,103 7,758 1 ,005
[Comorb=3] 0a . . . . . .
[etndich=1,00] -,152 ,0936 -,335 ,032 2,633 1 ,105
[etndich=2,00] 0a . . . . . .
Leeftijd ,007 ,0028 ,002 ,013 6,599 1 ,010
(Scale) ,581b ,0387 ,510 ,662
Dependent Variable: totaalhealthcareutilization
Model: (Intercept), PAIDhoog, PHQhoog, Comorb, etndich, Leeftijd
a Set to zero because this parameter is redundant.
b Maximum likelihood estimate.
If I change the 'scale parameter method' in SPSS to 'Pearson Chi-Square', the exact R results are reproduced. However, I want to do the exact same thing with the SPSS results in R. Is it possible to change the scale parameter method in R?
If any more information is necessary, please let me know.
