meanscore {meanscore} | R Documentation |
Weighted logistic regression using the Mean Score method
meanscore(x=x,y=y,z=z,factor=NULL,print.all=FALSE)
x |
matrix of predictor variables, one column of which contains some missing values (NA) |
y |
response variable (binary 0-1) |
z |
matrix of the surrogate or auxiliary variables
which must be categorical OPTIONAL ARGUMENTS |
print.all |
logical value determining all output to be printed. The default is False (F). |
factor |
factor variables; if the columns of the matrix of predictor variables have names, supply these names, otherwise supply the column numbers. MS.NPREV will fit separate coefficients for each level of the factor variables. |
The response, predictor and surrogate variables
must be numeric. The function will automatically
call the CODING function to recode the z matrix
to give a new.z
vector which takes a unique value
for each combination (type help(coding
) for further
particulars), as follows:
z1 | z2 | z3 | new.z |
0 | 0 | 0 | 1 |
1 | 0 | 0 | 2 |
0 | 1 | 0 | 3 |
1 | 1 | 0 | 4 |
0 | 0 | 1 | 5 |
1 | 0 | 1 | 6 |
0 | 1 | 1 | 7 |
1 | 1 | 1 | 8 |
The values of this new.z are reported as new.z
see
coding
.
A list called "parameters" containing the following will be returned:
est |
the vector of estimates of the regression coefficients |
se |
the vector of standard errors of the estimates |
z |
Wald statistic for each coefficient |
pvalue |
2-sided p-value (H0: coeff=0) when print.all = TRUE, it will also return the following lists: |
Ihat |
the Fisher information matrix |
varsi |
variance of the score for each (ylevel,zlevel) stratum |
Reilly,M and M.S. Pepe. 1995. A mean score method for missing and auxiliary
covariate data in regression models. Biometrika 82:299-314
ms.nprev
,coding
,
ectopic
,simNA
,glm
.
## Not run: THE SIMULATED DATASET EXAMPLE ## End(Not run) ## Not run: We use the simulated dataset which is stored in the matrix simNA. You can load the dataset using: ## End(Not run) data(simNA) help (simNA) #gives a detailed description of the data. ## Not run: To analyze this data using the meanscore function: meanscore(y=simNA[,1],z=simNA[,2],x=simNA[,3]) ## Not run: This will give the following: [1] "For calls to ms.nprev, input n1 or prev in the following order!!" ylevel z new.z n1 n2 [1,] 0 0 0 310 150 [2,] 0 1 1 166 85 [3,] 1 0 0 177 86 [4,] 1 1 1 347 179 $parameters est se z pvalue (Intercept) 0.0493998 0.07155138 0.6904103 0.4899362 x 1.0188437 0.10187094 10.0013188 0.0000000 ## End(Not run) ## Not run: If you extract the complete cases (n=500) to a matrix called "complete", using ## End(Not run) complete=simNA[!is.na(simNA[,3]),] ## Not run: then summary(glm(complete[,1]~complete[,3], family="binomial")) ## Not run: gives the following results: ## Not run: Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.05258 0.09879 0.532 0.595 complete[, 3] 1.01942 0.12050 8.460 <2e-16 *** ## End(Not run) ## Not run: Notice that the Mean Score estimates above had smaller standard errors, reflecting the additional information in the incomplete observations used in the analysis. Also note that since z is a surrogate for x, it is not used in the complete case analysis. ## End(Not run) ## Not run: THE ECTOPIC DATASET EXAMPLE ## Not run: This is a real-data example of an application of Mean Score to a case-control study of the association between ectopic pregnancy and sexually transmitted diseases (see Reilly and Pepe, 1995). To learn more about the dataset, type help(ectopic). The data frame called "ectopic" is in the data subfolder of the meanscore library. You can load the data by typing: ## End(Not run) data(ectopic) ## Not run: The following lines will reproduce the results presented in Table 3 of Reilly & Pepe (1995) ## End(Not run) # use gonnorhoea, contracept and sexpatr as auxiliary variables ectopic.z=ectopic[,3:5] # the auxiliary variables defined above and the chlamydia antibody status # are the predictor variables in the logistic regression model ectopic.x=ectopic[,2:5] meanscore(x=ectopic.x,z=ectopic.z,y=ectopic[,1])